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https://git-lfs.github.com/spec/v1 +oid sha256:1812dc4980816d17e36922f785ca1f8b443b1ed30484b468b9e05a6ae99bacfa +size 1974413 diff --git a/Davis_Data/videos/swing.mp4 b/Davis_Data/videos/swing.mp4 new file mode 100755 index 0000000000000000000000000000000000000000..882ad7758f1d64877c364263cdf763a1cac17054 --- /dev/null +++ b/Davis_Data/videos/swing.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:101ab66539b7acd491ee63e621fe689fd25923968e4e420c287c6ea4f3b06c18 +size 2174073 diff --git a/Davis_out/bear/cfg.yaml b/Davis_out/bear/cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..84685b3ebc27e3e8bcc672e059dd525a59e7bbe5 --- /dev/null +++ b/Davis_out/bear/cfg.yaml @@ -0,0 +1,68 @@ +batch_size: 8 +data: + camera_type: droid_recon + depth_type: aligned_depth_anything + end: -1 + image_type: JPEGImages + load_from_cache: false + mask_erosion_radius: 3 + mask_type: Annotations + num_targets_per_frame: 4 + res: 480p + root_dir: SOM_data_lcx/Davis_Data + scene_norm_dict: null + seq_name: bear + start: 0 + track_2d_type: bootstapir +loss: + w_depth_const: 0.1 + w_depth_grad: 1 + w_depth_reg: 0.5 + w_mask: 1.0 + w_rgb: 1.0 + w_scale_var: 0.01 + w_smooth_bases: 0.1 + w_smooth_tracks: 2.0 + w_track: 2.0 + w_z_accel: 1.0 +lr: + bg: + colors: 0.01 + means: 0.00016 + opacities: 0.05 + quats: 0.001 + scales: 0.005 + fg: + colors: 0.01 + means: 0.00016 + motion_coefs: 0.01 + opacities: 0.01 + quats: 0.001 + scales: 0.005 + motion_bases: + rots: 0.00016 + transls: 0.00016 +num_bg: 100000 +num_dl_workers: 4 +num_epochs: 500 +num_fg: 40000 +num_motion_bases: 10 +optim: + control_every: 100 + cull_opacity_threshold: 0.1 + cull_scale_threshold: 0.5 + cull_screen_threshold: 0.15 + densify_scale_threshold: 0.01 + densify_screen_threshold: 0.05 + densify_xys_grad_threshold: 0.0002 + max_steps: 5000 + reset_opacity_every_n_controls: 30 + stop_control_by_screen_steps: 4000 + stop_control_steps: 4000 + stop_densify_steps: 15000 + warmup_steps: 200 +port: null +save_videos_every: 50 +validate_every: 50 +vis_debug: false +work_dir: SOM_output_lcx/Davis_out/bear diff --git a/Davis_out/bear/checkpoints/last.ckpt b/Davis_out/bear/checkpoints/last.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..fd58f9c09d83f9bb22c6dd274b84e2ca64f06b5c --- /dev/null +++ b/Davis_out/bear/checkpoints/last.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc6c1e90687f97e64dd6e436781493c0aba7dd6e726103926e3bb396b30a52ac +size 138448040 diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/__init__.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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colors: float = 1e-2 + motion_coefs: float = 1e-2 + + +@dataclass +class BGLRConfig: + means: float = 1.6e-4 + opacities: float = 5e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, 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import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db367682ee99a59fd914240269d8f8e13e00bd1f --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/metrics.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/params.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/renderer.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/trainer.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/transforms.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/validator.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-25-234902/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-25-234902/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-25-234902/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-25-234902/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-234902/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db367682ee99a59fd914240269d8f8e13e00bd1f --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/metrics.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/params.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/renderer.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/trainer.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/transforms.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/validator.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-25-235122/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-25-235122/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-25-235122/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-25-235122/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235122/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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@abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db367682ee99a59fd914240269d8f8e13e00bd1f --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/metrics.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/params.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/renderer.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/trainer.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/transforms.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/validator.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-25-235708/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-25-235708/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-25-235708/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-25-235708/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-25-235708/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db367682ee99a59fd914240269d8f8e13e00bd1f --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005029/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005029/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005029/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005029/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005029/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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@abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..db367682ee99a59fd914240269d8f8e13e00bd1f --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005304/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005304/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005304/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005304/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005304/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b278e42803429ce7265169b2f24f942723fea3eb --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depthcrafter" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005638/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005638/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005638/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005638/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005638/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005721/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005721/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005721/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005721/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005721/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005843/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005843/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005843/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005843/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005843/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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@abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/params.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-005936/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-005936/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-005936/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-005936/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-005936/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/params.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-010130/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-010130/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-010130/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-010130/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-010130/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/params.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-011247/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-011247/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-011247/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-011247/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011247/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__init__.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__pycache__/__init__.cpython-310.pyc differ 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0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/base_dataset.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/casual_dataset.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/colmap.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/iphone_dataset.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/data/utils.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/init_utils.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/loss_utils.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/metrics.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/params.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/renderer.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/scene_model.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/tensor_dataclass.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/trainer.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/trajectories.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/transforms.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/validator.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__init__.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/playback_panel.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/render_panel.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/utils.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/viewer.py b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/bear/code/2024-10-26-011902/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/bear/code/2024-10-26-011902/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/bear/code/2024-10-26-011902/scripts/evaluate_iphone.py b/Davis_out/bear/code/2024-10-26-011902/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/bear/code/2024-10-26-011902/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: {np.mean(mpsnr_all):.4f}") + print(f"mean NV mSSIM: {np.mean(mssim_all):.4f}") + print(f"mean NV mLPIPS: {np.mean(mlpips_all):.4f}") diff --git a/Davis_out/bear/events.out.tfevents.1729875863.cvsv00-140.1364044.0 b/Davis_out/bear/events.out.tfevents.1729875863.cvsv00-140.1364044.0 new file mode 100644 index 0000000000000000000000000000000000000000..20937c42caaa863fa72b33361463d24d58926bd0 --- /dev/null +++ b/Davis_out/bear/events.out.tfevents.1729875863.cvsv00-140.1364044.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15b67fd649f41ee1d3e9236ddabedfe5728b551bf894130c70da7fd5c02c1dc5 +size 1954 diff --git a/Davis_out/bear/events.out.tfevents.1729876371.cvsv00-140.1383415.0 b/Davis_out/bear/events.out.tfevents.1729876371.cvsv00-140.1383415.0 new file mode 100644 index 0000000000000000000000000000000000000000..ac506cd8d691635c29e184d93ffb7799a4715dc7 --- /dev/null +++ b/Davis_out/bear/events.out.tfevents.1729876371.cvsv00-140.1383415.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afa22c3b7ea6701b3eacc171e620bd42973e7fe217765aa629f2451fb02bad45 +size 168070 diff --git a/Davis_out/bear/events.out.tfevents.1729876747.cvsv00-140.1397593.0 b/Davis_out/bear/events.out.tfevents.1729876747.cvsv00-140.1397593.0 new file mode 100644 index 0000000000000000000000000000000000000000..9ed6242bba7e4572422f4f080d3df739b3a24f45 --- /dev/null +++ b/Davis_out/bear/events.out.tfevents.1729876747.cvsv00-140.1397593.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:350e37158a59503517662a6991b1d61bd982605d249a8167ec5caefae3253496 +size 5316378 diff --git a/Davis_out/dance-jump/cfg.yaml b/Davis_out/dance-jump/cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2900019d58762b992ad9cebd631372769a6b9075 --- /dev/null +++ b/Davis_out/dance-jump/cfg.yaml @@ -0,0 +1,68 @@ +batch_size: 8 +data: + camera_type: droid_recon + depth_type: aligned_depth_anything + end: -1 + image_type: JPEGImages + load_from_cache: false + mask_erosion_radius: 3 + mask_type: Annotations + num_targets_per_frame: 4 + res: 480p + root_dir: SOM_data_lcx/Davis_Data + scene_norm_dict: null + seq_name: dance-jump + start: 0 + track_2d_type: bootstapir +loss: + w_depth_const: 0.1 + w_depth_grad: 1 + w_depth_reg: 0.5 + w_mask: 1.0 + w_rgb: 1.0 + w_scale_var: 0.01 + w_smooth_bases: 0.1 + w_smooth_tracks: 2.0 + w_track: 2.0 + w_z_accel: 1.0 +lr: + bg: + colors: 0.01 + means: 0.00016 + opacities: 0.05 + quats: 0.001 + scales: 0.005 + fg: + colors: 0.01 + means: 0.00016 + motion_coefs: 0.01 + opacities: 0.01 + quats: 0.001 + scales: 0.005 + motion_bases: + rots: 0.00016 + transls: 0.00016 +num_bg: 100000 +num_dl_workers: 4 +num_epochs: 500 +num_fg: 40000 +num_motion_bases: 10 +optim: + control_every: 100 + cull_opacity_threshold: 0.1 + cull_scale_threshold: 0.5 + cull_screen_threshold: 0.15 + densify_scale_threshold: 0.01 + densify_screen_threshold: 0.05 + densify_xys_grad_threshold: 0.0002 + max_steps: 5000 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100644 index 0000000000000000000000000000000000000000..bda0fff5f7560d6d2cec6c1890c99acb7d74ee71 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/configs.py @@ -0,0 +1,67 @@ +from dataclasses import dataclass + + +@dataclass +class FGLRConfig: + means: float = 1.6e-4 + opacities: float = 1e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + motion_coefs: float = 1e-2 + + +@dataclass +class BGLRConfig: + means: float = 1.6e-4 + opacities: float = 5e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__init__.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__pycache__/base_dataset.cpython-310.pyc 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Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/base_dataset.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/casual_dataset.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/colmap.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/iphone_dataset.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/utils.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/init_utils.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/loss_utils.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/metrics.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/params.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/renderer.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/scene_model.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/tensor_dataclass.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trainer.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trajectories.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/transforms.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/validator.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__init__.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/playback_panel.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/render_panel.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/utils.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/viewer.py b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/dance-jump/code/2024-10-26-011416/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/dance-jump/code/2024-10-26-011416/scripts/evaluate_iphone.py b/Davis_out/dance-jump/code/2024-10-26-011416/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011416/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/__init__.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/casual_dataset.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/colmap.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/iphone_dataset.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/utils.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/init_utils.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/loss_utils.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/metrics.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/params.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/renderer.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/scene_model.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/tensor_dataclass.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trainer.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trajectories.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/transforms.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/validator.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__init__.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/playback_panel.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/render_panel.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/utils.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/viewer.py b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/dance-jump/code/2024-10-26-011904/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/dance-jump/code/2024-10-26-011904/scripts/evaluate_iphone.py b/Davis_out/dance-jump/code/2024-10-26-011904/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/dance-jump/code/2024-10-26-011904/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: {np.mean(mpsnr_all):.4f}") + print(f"mean NV mSSIM: {np.mean(mssim_all):.4f}") + print(f"mean NV mLPIPS: {np.mean(mlpips_all):.4f}") diff --git a/Davis_out/dance-jump/events.out.tfevents.1729876571.cvsv00-140.1387060.0 b/Davis_out/dance-jump/events.out.tfevents.1729876571.cvsv00-140.1387060.0 new file mode 100644 index 0000000000000000000000000000000000000000..67fc1727623b9538e9f817d454452fc3f8234f0d --- /dev/null +++ b/Davis_out/dance-jump/events.out.tfevents.1729876571.cvsv00-140.1387060.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8aa210bbc2800a6d8eb369d301a4f61dad2381f4056d4c69b411d02a5d7c5b34 +size 291846 diff --git a/Davis_out/dance-jump/events.out.tfevents.1729876749.cvsv00-140.1397717.0 b/Davis_out/dance-jump/events.out.tfevents.1729876749.cvsv00-140.1397717.0 new file mode 100644 index 0000000000000000000000000000000000000000..8fa31ab65585b36b9be2ae5a1fcaa5bc8ed132b4 --- /dev/null +++ b/Davis_out/dance-jump/events.out.tfevents.1729876749.cvsv00-140.1397717.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5140e41981c403dc9f0105cd6393ec1959e21a57529bfa907a6bb95f0e6b48c2 +size 3682424 diff --git a/Davis_out/horsejump-high/cfg.yaml b/Davis_out/horsejump-high/cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7aad1069fe78382d92ef5e966e8aab7c07acea7d --- /dev/null +++ b/Davis_out/horsejump-high/cfg.yaml @@ -0,0 +1,68 @@ +batch_size: 8 +data: + camera_type: droid_recon + depth_type: aligned_depth_anything + end: -1 + image_type: JPEGImages + load_from_cache: false + mask_erosion_radius: 3 + mask_type: Annotations + num_targets_per_frame: 4 + res: 480p + root_dir: SOM_data_lcx/Davis_Data + scene_norm_dict: null + seq_name: horsejump-high + start: 0 + track_2d_type: bootstapir +loss: + w_depth_const: 0.1 + w_depth_grad: 1 + w_depth_reg: 0.5 + w_mask: 1.0 + w_rgb: 1.0 + w_scale_var: 0.01 + w_smooth_bases: 0.1 + w_smooth_tracks: 2.0 + w_track: 2.0 + w_z_accel: 1.0 +lr: + bg: + colors: 0.01 + means: 0.00016 + opacities: 0.05 + quats: 0.001 + scales: 0.005 + fg: + colors: 0.01 + means: 0.00016 + motion_coefs: 0.01 + opacities: 0.01 + quats: 0.001 + scales: 0.005 + motion_bases: + rots: 0.00016 + transls: 0.00016 +num_bg: 100000 +num_dl_workers: 4 +num_epochs: 500 +num_fg: 40000 +num_motion_bases: 10 +optim: + control_every: 100 + cull_opacity_threshold: 0.1 + cull_scale_threshold: 0.5 + cull_screen_threshold: 0.15 + densify_scale_threshold: 0.01 + densify_screen_threshold: 0.05 + densify_xys_grad_threshold: 0.0002 + max_steps: 5000 + reset_opacity_every_n_controls: 30 + stop_control_by_screen_steps: 4000 + stop_control_steps: 4000 + stop_densify_steps: 15000 + warmup_steps: 200 +port: null +save_videos_every: 50 +validate_every: 50 +vis_debug: false +work_dir: SOM_output_lcx/Davis_out/horsejump-high diff --git a/Davis_out/horsejump-high/checkpoints/last.ckpt 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float = 1.6e-4 + opacities: float = 1e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + motion_coefs: float = 1e-2 + + +@dataclass +class BGLRConfig: + means: float = 1.6e-4 + opacities: float = 5e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__pycache__/base_dataset.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/__pycache__/base_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..64078ac09c63f899c53589c9eccddad0f180558f Binary files /dev/null and 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@abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/casual_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/colmap.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/iphone_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/utils.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/init_utils.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/loss_utils.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/metrics.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/params.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/renderer.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/scene_model.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/tensor_dataclass.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trainer.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trajectories.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/transforms.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/validator.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/playback_panel.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/render_panel.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/utils.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/viewer.py b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/evaluate_iphone.py b/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-011528/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/__pycache__/__init__.cpython-310.pyc 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0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/casual_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/colmap.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/iphone_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/utils.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/init_utils.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/loss_utils.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/metrics.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/params.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/renderer.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/scene_model.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/tensor_dataclass.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trainer.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trajectories.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/transforms.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/validator.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/playback_panel.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/render_panel.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/utils.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/viewer.py b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/evaluate_iphone.py b/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-014752/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/__pycache__/__init__.cpython-310.pyc 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0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/casual_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/colmap.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/iphone_dataset.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/utils.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/init_utils.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/loss_utils.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/metrics.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/params.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/renderer.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/scene_model.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/tensor_dataclass.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trainer.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trajectories.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/transforms.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/validator.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__init__.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/playback_panel.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/render_panel.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/utils.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/viewer.py b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/evaluate_iphone.py b/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/horsejump-high/code/2024-10-26-095302/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: {np.mean(mpsnr_all):.4f}") + print(f"mean NV mSSIM: {np.mean(mssim_all):.4f}") + print(f"mean NV mLPIPS: {np.mean(mlpips_all):.4f}") diff --git a/Davis_out/horsejump-high/events.out.tfevents.1729876637.cvsv00-140.1390067.0 b/Davis_out/horsejump-high/events.out.tfevents.1729876637.cvsv00-140.1390067.0 new file mode 100644 index 0000000000000000000000000000000000000000..e0d45ba7b267b50368210f2dcb31da0f1fc0ca47 --- /dev/null +++ b/Davis_out/horsejump-high/events.out.tfevents.1729876637.cvsv00-140.1390067.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5aadabdf7e0058398bbb6205272ca9554581c17f00a3e9039bee37448faf03e4 +size 184509 diff --git a/Davis_out/horsejump-high/events.out.tfevents.1729878476.cvsv00-140.1454728.0 b/Davis_out/horsejump-high/events.out.tfevents.1729878476.cvsv00-140.1454728.0 new file mode 100644 index 0000000000000000000000000000000000000000..61e45ed0f45de0f84f13e4b9197eb2078a1b5a88 --- /dev/null +++ b/Davis_out/horsejump-high/events.out.tfevents.1729878476.cvsv00-140.1454728.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f71db5626a7caf2fa73b66928525cd63b28e2690f2a489c76b4fd317fcdf7876 +size 3382378 diff --git a/Davis_out/horsejump-high/events.out.tfevents.1729907587.cvsv00-140.2315804.0 b/Davis_out/horsejump-high/events.out.tfevents.1729907587.cvsv00-140.2315804.0 new file mode 100644 index 0000000000000000000000000000000000000000..04f76b10ebd19c119768c9eb54830f16696920b0 --- /dev/null +++ b/Davis_out/horsejump-high/events.out.tfevents.1729907587.cvsv00-140.2315804.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0770f75f4b44b784eb61e62ac455762077cb754154c0975f765132a7aa4825d +size 88 diff --git a/Davis_out/swing/cfg.yaml b/Davis_out/swing/cfg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fc8dd7eced0ab52b16f88b2f12c43d90e9431174 --- /dev/null +++ b/Davis_out/swing/cfg.yaml @@ -0,0 +1,68 @@ +batch_size: 8 +data: + camera_type: droid_recon + depth_type: aligned_depth_anything + end: -1 + image_type: JPEGImages + load_from_cache: false + mask_erosion_radius: 3 + mask_type: Annotations + num_targets_per_frame: 4 + res: 480p + root_dir: SOM_data_lcx/Davis_Data + scene_norm_dict: null + seq_name: swing + start: 0 + track_2d_type: bootstapir +loss: + w_depth_const: 0.1 + w_depth_grad: 1 + w_depth_reg: 0.5 + w_mask: 1.0 + w_rgb: 1.0 + w_scale_var: 0.01 + w_smooth_bases: 0.1 + w_smooth_tracks: 2.0 + w_track: 2.0 + w_z_accel: 1.0 +lr: + bg: + colors: 0.01 + means: 0.00016 + opacities: 0.05 + quats: 0.001 + scales: 0.005 + fg: + colors: 0.01 + means: 0.00016 + motion_coefs: 0.01 + opacities: 0.01 + quats: 0.001 + scales: 0.005 + motion_bases: + rots: 0.00016 + transls: 0.00016 +num_bg: 100000 +num_dl_workers: 4 +num_epochs: 500 +num_fg: 40000 +num_motion_bases: 10 +optim: + control_every: 100 + cull_opacity_threshold: 0.1 + cull_scale_threshold: 0.5 + cull_screen_threshold: 0.15 + densify_scale_threshold: 0.01 + densify_screen_threshold: 0.05 + densify_xys_grad_threshold: 0.0002 + max_steps: 5000 + reset_opacity_every_n_controls: 30 + stop_control_by_screen_steps: 4000 + stop_control_steps: 4000 + stop_densify_steps: 15000 + warmup_steps: 200 +port: null +save_videos_every: 50 +validate_every: 50 +vis_debug: false +work_dir: SOM_output_lcx/Davis_out/swing diff --git a/Davis_out/swing/checkpoints/last.ckpt b/Davis_out/swing/checkpoints/last.ckpt new file mode 100644 index 0000000000000000000000000000000000000000..7a871eec579bb4576ef363dd8cd0d6aaaed1afa7 --- /dev/null +++ b/Davis_out/swing/checkpoints/last.ckpt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c1d54e757ec8bc4167a9286b3549c46010dd194055e053e883c121549e4e1ca +size 86551400 diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/__init__.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/__init__.py new file mode 100644 index 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0000000000000000000000000000000000000000..bda0fff5f7560d6d2cec6c1890c99acb7d74ee71 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/configs.py @@ -0,0 +1,67 @@ +from dataclasses import dataclass + + +@dataclass +class FGLRConfig: + means: float = 1.6e-4 + opacities: float = 1e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + motion_coefs: float = 1e-2 + + +@dataclass +class BGLRConfig: + means: float = 1.6e-4 + opacities: float = 5e-2 + scales: float = 5e-3 + quats: float = 1e-3 + colors: float = 1e-2 + + +@dataclass +class MotionLRConfig: + rots: float = 1.6e-4 + transls: float = 1.6e-4 + + +@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__init__.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a459b62f68a65b4c199e7f144cc292fd6cf9c84a Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__pycache__/base_dataset.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/__pycache__/base_dataset.cpython-310.pyc new file mode 100644 index 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a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/base_dataset.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/casual_dataset.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/colmap.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/iphone_dataset.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/data/utils.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/init_utils.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/loss_utils.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/metrics.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/params.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/renderer.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/scene_model.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/tensor_dataclass.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/trainer.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/trajectories.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/transforms.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/validator.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__init__.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/playback_panel.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/render_panel.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/utils.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/viewer.py b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/swing/code/2024-10-26-011330/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/swing/code/2024-10-26-011330/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/swing/code/2024-10-26-011330/scripts/evaluate_iphone.py b/Davis_out/swing/code/2024-10-26-011330/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-011330/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: 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+@dataclass +class SceneLRConfig: + fg: FGLRConfig + bg: BGLRConfig + motion_bases: MotionLRConfig + + +@dataclass +class LossesConfig: + w_rgb: float = 1.0 + w_depth_reg: float = 0.5 + w_depth_const: float = 0.1 + w_depth_grad: float = 1 + w_track: float = 2.0 + w_mask: float = 1.0 + w_smooth_bases: float = 0.1 + w_smooth_tracks: float = 2.0 + w_scale_var: float = 0.01 + w_z_accel: float = 1.0 + + +@dataclass +class OptimizerConfig: + max_steps: int = 5000 + ## Adaptive gaussian control + warmup_steps: int = 200 + control_every: int = 100 + reset_opacity_every_n_controls: int = 30 + stop_control_by_screen_steps: int = 4000 + stop_control_steps: int = 4000 + ### Densify. + densify_xys_grad_threshold: float = 0.0002 + densify_scale_threshold: float = 0.01 + densify_screen_threshold: float = 0.05 + stop_densify_steps: int = 15000 + ### Cull. + cull_opacity_threshold: float = 0.1 + cull_scale_threshold: float = 0.5 + cull_screen_threshold: float = 0.15 diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__init__.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..45685985e850017fc12f61c5a149cd8cdc12d728 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__init__.py @@ -0,0 +1,40 @@ +from dataclasses import asdict, replace + +from torch.utils.data import Dataset + +from .base_dataset import BaseDataset +from .casual_dataset import CasualDataset, CustomDataConfig, DavisDataConfig +from .iphone_dataset import ( + iPhoneDataConfig, + iPhoneDataConfig_Crafter, + iPhoneDataset, + iPhoneDatasetKeypointView, + iPhoneDatasetVideoView, +) + + +def get_train_val_datasets( + data_cfg: iPhoneDataConfig | DavisDataConfig | CustomDataConfig | iPhoneDataConfig_Crafter, load_val: bool +) -> tuple[BaseDataset, Dataset | None, Dataset | None, Dataset | None]: + train_video_view = None + val_img_dataset = None + val_kpt_dataset = None + if isinstance(data_cfg, iPhoneDataConfig) or isinstance(data_cfg, iPhoneDataConfig_Crafter): + train_dataset = iPhoneDataset(**asdict(data_cfg)) + train_video_view = iPhoneDatasetVideoView(train_dataset) + if load_val: + val_img_dataset = ( + iPhoneDataset( + **asdict(replace(data_cfg, split="val", load_from_cache=True)) + ) + if train_dataset.has_validation + else None + ) + val_kpt_dataset = iPhoneDatasetKeypointView(train_dataset) + elif isinstance(data_cfg, DavisDataConfig) or isinstance( + data_cfg, CustomDataConfig + ): + train_dataset = CasualDataset(**asdict(data_cfg)) + else: + raise ValueError(f"Unknown data config: {data_cfg}") + return train_dataset, train_video_view, val_img_dataset, val_kpt_dataset diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__pycache__/__init__.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__pycache__/utils.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..200f97129de5c41990fbcd698dd6cd1a21044542 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/base_dataset.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82267c3f4abdac6f7bb25365c8d465d522b690 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/base_dataset.py @@ -0,0 +1,77 @@ +from abc import abstractmethod + +import torch +from torch.utils.data import Dataset, default_collate + + +class BaseDataset(Dataset): + @property + @abstractmethod + def num_frames(self) -> int: ... + + @property + def keyframe_idcs(self) -> torch.Tensor: + return torch.arange(self.num_frames) + + @abstractmethod + def get_w2cs(self) -> torch.Tensor: ... + + @abstractmethod + def get_Ks(self) -> torch.Tensor: ... + + @abstractmethod + def get_image(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_depth(self, index: int) -> torch.Tensor: ... + + @abstractmethod + def get_mask(self, index: int) -> torch.Tensor: ... + + def get_img_wh(self) -> tuple[int, int]: ... + + @abstractmethod + def get_tracks_3d( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns 3D tracks: + coordinates (N, T, 3), + visibles (N, T), + invisibles (N, T), + confidences (N, T), + colors (N, 3) + """ + ... + + @abstractmethod + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Returns background points: + coordinates (N, 3), + normals (N, 3), + colors (N, 3) + """ + ... + + @staticmethod + def train_collate_fn(batch): + collated = {} + for k in batch[0]: + if k not in [ + "query_tracks_2d", + "target_ts", + "target_w2cs", + "target_Ks", + "target_tracks_2d", + "target_visibles", + "target_track_depths", + "target_invisibles", + "target_confidences", + ]: + collated[k] = default_collate([sample[k] for sample in batch]) + else: + collated[k] = [sample[k] for sample in batch] + return collated diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/casual_dataset.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/casual_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e378c6d2408c8ae5d2cdaf45cf6f77dead5c7bd2 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/casual_dataset.py @@ -0,0 +1,498 @@ +import os +from dataclasses import dataclass +from functools import partial +from typing import Literal, cast + +import cv2 +import imageio +import numpy as np +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from roma import roma +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.utils import ( + UINT16_MAX, + SceneNormDict, + get_tracks_3d_for_query_frame, + median_filter_2d, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class DavisDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "480p" + image_type: str = "JPEGImages" + mask_type: str = "Annotations" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything" #"aligned_depthcrafter" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 3 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +@dataclass +class CustomDataConfig: + seq_name: str + root_dir: str + start: int = 0 + end: int = -1 + res: str = "" + image_type: str = "images" + mask_type: str = "masks" + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + ] = "aligned_depth_anything" + camera_type: Literal["droid_recon"] = "droid_recon" + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir" + mask_erosion_radius: int = 7 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + num_targets_per_frame: int = 4 + load_from_cache: bool = False + + +class CasualDataset(BaseDataset): + def __init__( + self, + seq_name: str, + root_dir: str, + start: int = 0, + end: int = -1, + res: str = "480p", + image_type: str = "JPEGImages", + mask_type: str = "Annotations", + depth_type: Literal[ + "aligned_depth_anything", + "aligned_depth_anything_v2", + "depth_anything", + "depth_anything_v2", + "unidepth_disp", + "aligned_depthcrafter", + ] = "aligned_depth_anything", #"aligned_depthcrafter", + camera_type: Literal["droid_recon"] = "droid_recon", + track_2d_type: Literal["bootstapir", "tapir"] = "bootstapir", + mask_erosion_radius: int = 3, + scene_norm_dict: SceneNormDict | None = None, + num_targets_per_frame: int = 4, + load_from_cache: bool = False, + **_, + ): + super().__init__() + + self.seq_name = seq_name + self.root_dir = root_dir + self.res = res + self.depth_type = depth_type + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + self.has_validation = False + self.mask_erosion_radius = mask_erosion_radius + + self.img_dir = f"{root_dir}/{image_type}/{res}/{seq_name}" + self.img_ext = os.path.splitext(os.listdir(self.img_dir)[0])[1] + self.depth_dir = f"{root_dir}/{depth_type}/{res}/{seq_name}" + self.mask_dir = f"{root_dir}/{mask_type}/{res}/{seq_name}" + self.tracks_dir = f"{root_dir}/{track_2d_type}/{res}/{seq_name}" + self.cache_dir = f"{root_dir}/flow3d_preprocessed/{res}/{seq_name}" + # self.cache_dir = f"datasets/davis/flow3d_preprocessed/{res}/{seq_name}" + frame_names = [os.path.splitext(p)[0] for p in sorted(os.listdir(self.img_dir))] + + if end == -1: + end = len(frame_names) + self.start = start + self.end = end + self.frame_names = frame_names[start:end] + + self.imgs: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.depths: list[torch.Tensor | None] = [None for _ in self.frame_names] + self.masks: list[torch.Tensor | None] = [None for _ in self.frame_names] + + # load cameras + if camera_type == "droid_recon": + img = self.get_image(0) + H, W = img.shape[:2] + w2cs, Ks, tstamps = load_cameras( + f"{root_dir}/{camera_type}/{seq_name}.npy", H, W + ) + else: + raise ValueError(f"Unknown camera type: {camera_type}") + assert ( + len(frame_names) == len(w2cs) == len(Ks) + ), f"{len(frame_names)}, {len(w2cs)}, {len(Ks)}" + self.w2cs = w2cs[start:end] + self.Ks = Ks[start:end] + tmask = (tstamps >= start) & (tstamps < end) + self._keyframe_idcs = tstamps[tmask] - start + self.scale = 1 + + if scene_norm_dict is None: + cached_scene_norm_dict_path = os.path.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if os.path.exists(cached_scene_norm_dict_path) and self.load_from_cache: + guru.info("loading cached scene norm dict...") + scene_norm_dict = torch.load( + os.path.join(self.cache_dir, "scene_norm_dict.pth") + ) + else: + tracks_3d = self.get_tracks_3d(5000, step=self.num_frames // 10)[0] + scale, transfm = compute_scene_norm(tracks_3d, self.w2cs) + scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + os.makedirs(self.cache_dir, exist_ok=True) + torch.save(scene_norm_dict, cached_scene_norm_dict_path) + + # transform cameras + self.scene_norm_dict = cast(SceneNormDict, scene_norm_dict) + self.scale = self.scene_norm_dict["scale"] + transform = self.scene_norm_dict["transfm"] + guru.info(f"scene norm {self.scale=}, {transform=}") + self.w2cs = torch.einsum("nij,jk->nik", self.w2cs, torch.linalg.inv(transform)) + self.w2cs[:, :3, 3] /= self.scale + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + @property + def keyframe_idcs(self) -> torch.Tensor: + return self._keyframe_idcs + + def __len__(self): + return len(self.frame_names) + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_img_wh(self) -> tuple[int, int]: + return self.get_image(0).shape[1::-1] + + def get_image(self, index) -> torch.Tensor: + if self.imgs[index] is None: + self.imgs[index] = self.load_image(index) + img = cast(torch.Tensor, self.imgs[index]) + return img + + def get_mask(self, index) -> torch.Tensor: + if self.masks[index] is None: + self.masks[index] = self.load_mask(index) + mask = cast(torch.Tensor, self.masks[index]) + return mask + + def get_depth(self, index) -> torch.Tensor: + if self.depths[index] is None: + self.depths[index] = self.load_depth(index) + return self.depths[index] / self.scale + + def load_image(self, index) -> torch.Tensor: + path = f"{self.img_dir}/{self.frame_names[index]}{self.img_ext}" + return torch.from_numpy(imageio.imread(path)).float() / 255.0 + + def load_mask(self, index) -> torch.Tensor: + path = f"{self.mask_dir}/{self.frame_names[index]}.png" + r = self.mask_erosion_radius + mask = imageio.imread(path) + fg_mask = mask.reshape((*mask.shape[:2], -1)).max(axis=-1) > 0 + bg_mask = ~fg_mask + fg_mask_erode = cv2.erode( + fg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + bg_mask_erode = cv2.erode( + bg_mask.astype(np.uint8), np.ones((r, r), np.uint8), iterations=1 + ) + out_mask = np.zeros_like(fg_mask, dtype=np.float32) + out_mask[bg_mask_erode > 0] = -1 + out_mask[fg_mask_erode > 0] = 1 + return torch.from_numpy(out_mask).float() + + def load_depth(self, index) -> torch.Tensor: + path = f"{self.depth_dir}/{self.frame_names[index]}.npy" + disp = np.load(path) + depth = 1.0 / np.clip(disp, a_min=1e-6, a_max=1e6) + depth = torch.from_numpy(depth).float() + depth = median_filter_2d(depth[None, None], 11, 1)[0, 0] + return depth + + def load_target_tracks( + self, query_index: int, target_indices: list[int], dim: int = 1 + ): + """ + tracks are 2d, occs and uncertainties + :param dim (int), default 1: dimension to stack the time axis + return (N, T, 4) if dim=1, (T, N, 4) if dim=0 + """ + q_name = self.frame_names[query_index] + all_tracks = [] + for ti in target_indices: + t_name = self.frame_names[ti] + path = f"{self.tracks_dir}/{q_name}_{t_name}.npy" + tracks = np.load(path).astype(np.float32) + all_tracks.append(tracks) + return torch.from_numpy(np.stack(all_tracks, axis=dim)) + + def get_tracks_3d( + self, num_samples: int, start: int = 0, end: int = -1, step: int = 1, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_frames = self.num_frames + if end < 0: + end = num_frames + 1 + end + query_idcs = list(range(start, end, step)) + target_idcs = list(range(start, end, step)) + masks = torch.stack([self.get_mask(i) for i in target_idcs], dim=0) + fg_masks = (masks == 1).float() + depths = torch.stack([self.get_depth(i) for i in target_idcs], dim=0) + inv_Ks = torch.linalg.inv(self.Ks[target_idcs]) + c2ws = torch.linalg.inv(self.w2cs[target_idcs]) + + num_per_query_frame = int(np.ceil(num_samples / len(query_idcs))) + cur_num = 0 + tracks_all_queries = [] + for q_idx in query_idcs: + # (N, T, 4) + tracks_2d = self.load_target_tracks(q_idx, target_idcs) + num_sel = int( + min(num_per_query_frame, num_samples - cur_num, len(tracks_2d)) + ) + if num_sel < len(tracks_2d): + sel_idcs = np.random.choice(len(tracks_2d), num_sel, replace=False) + tracks_2d = tracks_2d[sel_idcs] + cur_num += tracks_2d.shape[0] + img = self.get_image(q_idx) + tidx = target_idcs.index(q_idx) + tracks_tuple = get_tracks_3d_for_query_frame( + tidx, img, tracks_2d, depths, fg_masks, inv_Ks, c2ws + ) + tracks_all_queries.append(tracks_tuple) + tracks_3d, colors, visibles, invisibles, confidences = map( + partial(torch.cat, dim=0), zip(*tracks_all_queries) + ) + return tracks_3d, visibles, invisibles, confidences, colors + + def get_bkgd_points( + self, + num_samples: int, + use_kf_tstamps: bool = True, + stride: int = 8, + down_rate: int = 8, + min_per_frame: int = 64, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + start = 0 + end = self.num_frames + H, W = self.get_image(0).shape[:2] + grid = torch.stack( + torch.meshgrid( + torch.arange(0, W, dtype=torch.float32), + torch.arange(0, H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + + if use_kf_tstamps: + query_idcs = self.keyframe_idcs.tolist() + else: + num_query_frames = self.num_frames // stride + query_endpts = torch.linspace(start, end, num_query_frames + 1) + query_idcs = ((query_endpts[:-1] + query_endpts[1:]) / 2).long().tolist() + + bg_geometry = [] + print(f"{query_idcs=}") + for query_idx in tqdm(query_idcs, desc="Loading bkgd points", leave=False): + img = self.get_image(query_idx) + depth = self.get_depth(query_idx) + bg_mask = self.get_mask(query_idx) < 0 + bool_mask = (bg_mask * (depth > 0)).to(torch.bool) + w2c = self.w2cs[query_idx] + K = self.Ks[query_idx] + + # get the bounding box of previous points that reproject into frame + # inefficient but works for now + bmax_x, bmax_y, bmin_x, bmin_y = 0, 0, W, H + for p3d, _, _ in bg_geometry: + if len(p3d) < 1: + continue + # reproject into current frame + p2d = torch.einsum( + "ij,jk,pk->pi", K, w2c[:3], F.pad(p3d, (0, 1), value=1.0) + ) + p2d = p2d[:, :2] / p2d[:, 2:].clamp(min=1e-6) + xmin, xmax = p2d[:, 0].min().item(), p2d[:, 0].max().item() + ymin, ymax = p2d[:, 1].min().item(), p2d[:, 1].max().item() + + bmin_x = min(bmin_x, int(xmin)) + bmin_y = min(bmin_y, int(ymin)) + bmax_x = max(bmax_x, int(xmax)) + bmax_y = max(bmax_y, int(ymax)) + + # don't include points that are covered by previous points + bmin_x = max(0, bmin_x) + bmin_y = max(0, bmin_y) + bmax_x = min(W, bmax_x) + bmax_y = min(H, bmax_y) + overlap_mask = torch.ones_like(bool_mask) + overlap_mask[bmin_y:bmax_y, bmin_x:bmax_x] = 0 + + bool_mask &= overlap_mask + if bool_mask.sum() < min_per_frame: + guru.debug(f"skipping {query_idx=}") + continue + + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + + num_sel = max(len(points) // down_rate, min_per_frame) + sel_idcs = np.random.choice(len(points), num_sel, replace=False) + points = points[sel_idcs] + point_normals = point_normals[sel_idcs] + point_colors = point_colors[sel_idcs] + guru.debug(f"{query_idx=} {points.shape=}") + bg_geometry.append((points, point_normals, point_colors)) + + bg_points, bg_normals, bg_colors = map( + partial(torch.cat, dim=0), zip(*bg_geometry) + ) + if len(bg_points) > num_samples: + sel_idcs = np.random.choice(len(bg_points), num_samples, replace=False) + bg_points = bg_points[sel_idcs] + bg_normals = bg_normals[sel_idcs] + bg_colors = bg_colors[sel_idcs] + + return bg_points, bg_normals, bg_colors + + def __getitem__(self, index: int): + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": torch.tensor(index), + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.get_image(index), + "depths": self.get_depth(index), + } + tri_mask = self.get_mask(index) + valid_mask = tri_mask != 0 # not fg or bg + mask = tri_mask == 1 # fg mask + data["masks"] = mask.float() + data["valid_masks"] = valid_mask.float() + + # (P, 2) + query_tracks = self.load_target_tracks(index, [index])[:, 0, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4) + target_tracks = self.load_target_tracks(index, target_inds.tolist(), dim=0) + data["query_tracks_2d"] = query_tracks + data["target_ts"] = target_inds + data["target_w2cs"] = self.w2cs[target_inds] + data["target_Ks"] = self.Ks[target_inds] + data["target_tracks_2d"] = target_tracks[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info(target_tracks[..., 2], target_tracks[..., 3]) + # (N, H, W) + target_depths = torch.stack([self.get_depth(i) for i in target_inds], dim=0) + H, W = target_depths.shape[-2:] + data["target_track_depths"] = F.grid_sample( + target_depths[:, None], + normalize_coords(target_tracks[..., None, :2], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + return data + + +def load_cameras( + path: str, H: int, W: int +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + assert os.path.exists(path), f"Camera file {path} does not exist." + recon = np.load(path, allow_pickle=True).item() + guru.debug(f"{recon.keys()=}") + traj_c2w = recon["traj_c2w"] # (N, 4, 4) + h, w = recon["img_shape"] + sy, sx = H / h, W / w + traj_w2c = np.linalg.inv(traj_c2w) + fx, fy, cx, cy = recon["intrinsics"] # (4,) + K = np.array([[fx * sx, 0, cx * sx], [0, fy * sy, cy * sy], [0, 0, 1]]) # (3, 3) + Ks = np.tile(K[None, ...], (len(traj_c2w), 1, 1)) # (N, 3, 3) + kf_tstamps = recon["tstamps"].astype("int") + return ( + torch.from_numpy(traj_w2c).float(), + torch.from_numpy(Ks).float(), + torch.from_numpy(kf_tstamps), + ) + + +def compute_scene_norm( + X: torch.Tensor, w2cs: torch.Tensor +) -> tuple[float, torch.Tensor]: + """ + :param X: [N*T, 3] + :param w2cs: [N, 4, 4] + """ + X = X.reshape(-1, 3) + scene_center = X.mean(dim=0) + X = X - scene_center[None] + min_scale = X.quantile(0.05, dim=0) + max_scale = X.quantile(0.95, dim=0) + scale = (max_scale - min_scale).max().item() / 2.0 + original_up = -F.normalize(w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + return scale, transfm + + +if __name__ == "__main__": + d = CasualDataset("bear", "/shared/vye/datasets/DAVIS", camera_type="droid_recon") diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/colmap.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/colmap.py new file mode 100644 index 0000000000000000000000000000000000000000..bbfc67683bee48496671525aa259a5985b1b1483 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/colmap.py @@ -0,0 +1,369 @@ +import os +import struct +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Union + +import numpy as np + + +def get_colmap_camera_params(colmap_dir, img_files): + cameras = read_cameras_binary(colmap_dir + "/cameras.bin") + images = read_images_binary(colmap_dir + "/images.bin") + colmap_image_idcs = {v.name: k for k, v in images.items()} + img_names = [os.path.basename(img_file) for img_file in img_files] + num_imgs = len(img_names) + K_all = np.zeros((num_imgs, 4, 4)) + extrinsics_all = np.zeros((num_imgs, 4, 4)) + for idx, name in enumerate(img_names): + key = colmap_image_idcs[name] + image = images[key] + assert image.name == name + K, extrinsics = get_intrinsics_extrinsics(image, cameras) + K_all[idx] = K + extrinsics_all[idx] = extrinsics + + return K_all, extrinsics_all + + +@dataclass(frozen=True) +class CameraModel: + model_id: int + model_name: str + num_params: int + + +@dataclass(frozen=True) +class Camera: + id: int + model: str + width: int + height: int + params: np.ndarray + + +@dataclass(frozen=True) +class BaseImage: + id: int + qvec: np.ndarray + tvec: np.ndarray + camera_id: int + name: str + xys: np.ndarray + point3D_ids: np.ndarray + + +@dataclass(frozen=True) +class Point3D: + id: int + xyz: np.ndarray + rgb: np.ndarray + error: Union[float, np.ndarray] + image_ids: np.ndarray + point2D_idxs: np.ndarray + + +class Image(BaseImage): + def qvec2rotmat(self): + return qvec2rotmat(self.qvec) + + +CAMERA_MODELS = { + CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3), + CameraModel(model_id=1, model_name="PINHOLE", num_params=4), + CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4), + CameraModel(model_id=3, model_name="RADIAL", num_params=5), + CameraModel(model_id=4, model_name="OPENCV", num_params=8), + CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8), + CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12), + CameraModel(model_id=7, model_name="FOV", num_params=5), + CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4), + CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5), + CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12), +} +CAMERA_MODEL_IDS = dict( + [(camera_model.model_id, camera_model) for camera_model in CAMERA_MODELS] +) + + +def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"): + """Read and unpack the next bytes from a binary file. + :param fid: + :param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc. + :param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}. + :param endian_character: Any of {@, =, <, >, !} + :return: Tuple of read and unpacked values. + """ + data = fid.read(num_bytes) + return struct.unpack(endian_character + format_char_sequence, data) + + +def read_cameras_text(path: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasText(const std::string& path) + void Reconstruction::ReadCamerasText(const std::string& path) + """ + cameras = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + camera_id = int(elems[0]) + model = elems[1] + width = int(elems[2]) + height = int(elems[3]) + params = np.array(tuple(map(float, elems[4:]))) + cameras[camera_id] = Camera( + id=camera_id, model=model, width=width, height=height, params=params + ) + return cameras + + +def read_cameras_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Camera]: + """ + see: src/base/reconstruction.cc + void Reconstruction::WriteCamerasBinary(const std::string& path) + void Reconstruction::ReadCamerasBinary(const std::string& path) + """ + cameras = {} + with open(path_to_model_file, "rb") as fid: + num_cameras = read_next_bytes(fid, 8, "Q")[0] + for camera_line_index in range(num_cameras): + camera_properties = read_next_bytes( + fid, num_bytes=24, format_char_sequence="iiQQ" + ) + camera_id = camera_properties[0] + model_id = camera_properties[1] + model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name + width = camera_properties[2] + height = camera_properties[3] + num_params = CAMERA_MODEL_IDS[model_id].num_params + params = read_next_bytes( + fid, num_bytes=8 * num_params, format_char_sequence="d" * num_params + ) + cameras[camera_id] = Camera( + id=camera_id, + model=model_name, + width=width, + height=height, + params=np.array(params), + ) + assert len(cameras) == num_cameras + return cameras + + +def read_images_text(path: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesText(const std::string& path) + void Reconstruction::WriteImagesText(const std::string& path) + """ + images = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + image_id = int(elems[0]) + qvec = np.array(tuple(map(float, elems[1:5]))) + tvec = np.array(tuple(map(float, elems[5:8]))) + camera_id = int(elems[8]) + image_name = elems[9] + elems = fid.readline().split() + xys = np.column_stack( + [tuple(map(float, elems[0::3])), tuple(map(float, elems[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, elems[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_images_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Image]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadImagesBinary(const std::string& path) + void Reconstruction::WriteImagesBinary(const std::string& path) + """ + images = {} + with open(path_to_model_file, "rb") as fid: + num_reg_images = read_next_bytes(fid, 8, "Q")[0] + for image_index in range(num_reg_images): + binary_image_properties = read_next_bytes( + fid, num_bytes=64, format_char_sequence="idddddddi" + ) + image_id = binary_image_properties[0] + qvec = np.array(binary_image_properties[1:5]) + tvec = np.array(binary_image_properties[5:8]) + camera_id = binary_image_properties[8] + image_name = "" + current_char = read_next_bytes(fid, 1, "c")[0] + while current_char != b"\x00": # look for the ASCII 0 entry + image_name += current_char.decode("utf-8") + current_char = read_next_bytes(fid, 1, "c")[0] + num_points2D = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + x_y_id_s = read_next_bytes( + fid, + num_bytes=24 * num_points2D, + format_char_sequence="ddq" * num_points2D, + ) + xys = np.column_stack( + [tuple(map(float, x_y_id_s[0::3])), tuple(map(float, x_y_id_s[1::3]))] + ) + point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3]))) + images[image_id] = Image( + id=image_id, + qvec=qvec, + tvec=tvec, + camera_id=camera_id, + name=image_name, + xys=xys, + point3D_ids=point3D_ids, + ) + return images + + +def read_points3D_text(path: Union[str, Path]): + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DText(const std::string& path) + void Reconstruction::WritePoints3DText(const std::string& path) + """ + points3D = {} + with open(path, "r") as fid: + while True: + line = fid.readline() + if not line: + break + line = line.strip() + if len(line) > 0 and line[0] != "#": + elems = line.split() + point3D_id = int(elems[0]) + xyz = np.array(tuple(map(float, elems[1:4]))) + rgb = np.array(tuple(map(int, elems[4:7]))) + error = float(elems[7]) + image_ids = np.array(tuple(map(int, elems[8::2]))) + point2D_idxs = np.array(tuple(map(int, elems[9::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def read_points3d_binary(path_to_model_file: Union[str, Path]) -> Dict[int, Point3D]: + """ + see: src/base/reconstruction.cc + void Reconstruction::ReadPoints3DBinary(const std::string& path) + void Reconstruction::WritePoints3DBinary(const std::string& path) + """ + points3D = {} + with open(path_to_model_file, "rb") as fid: + num_points = read_next_bytes(fid, 8, "Q")[0] + for point_line_index in range(num_points): + binary_point_line_properties = read_next_bytes( + fid, num_bytes=43, format_char_sequence="QdddBBBd" + ) + point3D_id = binary_point_line_properties[0] + xyz = np.array(binary_point_line_properties[1:4]) + rgb = np.array(binary_point_line_properties[4:7]) + error = np.array(binary_point_line_properties[7]) + track_length = read_next_bytes(fid, num_bytes=8, format_char_sequence="Q")[ + 0 + ] + track_elems = read_next_bytes( + fid, + num_bytes=8 * track_length, + format_char_sequence="ii" * track_length, + ) + image_ids = np.array(tuple(map(int, track_elems[0::2]))) + point2D_idxs = np.array(tuple(map(int, track_elems[1::2]))) + points3D[point3D_id] = Point3D( + id=point3D_id, + xyz=xyz, + rgb=rgb, + error=error, + image_ids=image_ids, + point2D_idxs=point2D_idxs, + ) + return points3D + + +def qvec2rotmat(qvec): + return np.array( + [ + [ + 1 - 2 * qvec[2] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3], + 2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2], + ], + [ + 2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[3] ** 2, + 2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1], + ], + [ + 2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2], + 2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1], + 1 - 2 * qvec[1] ** 2 - 2 * qvec[2] ** 2, + ], + ] + ) + + +def get_intrinsics_extrinsics(img, cameras): + # world to cam transformation + R = qvec2rotmat(img.qvec) + # translation + t = img.tvec + cam = cameras[img.camera_id] + + if cam.model in ("SIMPLE_PINHOLE", "SIMPLE_RADIAL", "RADIAL"): + fx = fy = cam.params[0] + cx = cam.params[1] + cy = cam.params[2] + elif cam.model in ( + "PINHOLE", + "OPENCV", + "OPENCV_FISHEYE", + "FULL_OPENCV", + ): + fx = cam.params[0] + fy = cam.params[1] + cx = cam.params[2] + cy = cam.params[3] + else: + raise Exception("Camera model not supported") + + # intrinsics + K = np.identity(4) + K[0, 0] = fx + K[1, 1] = fy + K[0, 2] = cx + K[1, 2] = cy + + extrinsics = np.eye(4) + extrinsics[:3, :3] = R + extrinsics[:3, 3] = t + return K, extrinsics diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/iphone_dataset.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/iphone_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..fb1580040e231f05c1395852842781807f277288 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/iphone_dataset.py @@ -0,0 +1,865 @@ +import json +import os +import os.path as osp +from dataclasses import dataclass +from glob import glob +from itertools import product +from typing import Literal + +import imageio.v3 as iio +import numpy as np +import roma +import torch +import torch.nn.functional as F +import tyro +from loguru import logger as guru +from torch.utils.data import Dataset +from tqdm import tqdm + +from flow3d.data.base_dataset import BaseDataset +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.data.utils import ( + SceneNormDict, + masked_median_blur, + normal_from_depth_image, + normalize_coords, + parse_tapir_track_info, +) +from flow3d.transforms import rt_to_mat4 + + +@dataclass +class iPhoneDataConfig: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_anything_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +@dataclass +class iPhoneDataConfig_Crafter: + data_dir: str + start: int = 0 + end: int = -1 + split: Literal["train", "val"] = "train" + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap" + camera_type: Literal["original", "refined"] = "refined" + use_median_filter: bool = False + num_targets_per_frame: int = 4 + scene_norm_dict: tyro.conf.Suppress[SceneNormDict | None] = None + load_from_cache: bool = False + skip_load_imgs: bool = False + + +class iPhoneDataset(BaseDataset): + def __init__( + self, + data_dir: str, + start: int = 0, + end: int = -1, + factor: int = 1, + split: Literal["train", "val"] = "train", + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + "depth_crafter_colmap", + ] = "depth_crafter_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + scene_norm_dict: SceneNormDict | None = None, + load_from_cache: bool = False, + skip_load_imgs: bool = False, + **_, + ): + super().__init__() + + self.data_dir = data_dir + self.training = split == "train" + self.split = split + self.factor = factor + self.start = start + self.end = end + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.scene_norm_dict = scene_norm_dict + self.load_from_cache = load_from_cache + self.cache_dir = osp.join(data_dir, "flow3d_preprocessed", "cache") + os.makedirs(self.cache_dir, exist_ok=True) + + print("!!!!depth_type!!!", depth_type) + + # Test if the current data has validation set. + with open(osp.join(data_dir, "splits", "val.json")) as f: + split_dict = json.load(f) + self.has_validation = len(split_dict["frame_names"]) > 0 + + # Load metadata. + with open(osp.join(data_dir, "splits", f"{split}.json")) as f: + split_dict = json.load(f) + full_len = len(split_dict["frame_names"]) + end = min(end, full_len) if end > 0 else full_len + self.end = end + self.frame_names = split_dict["frame_names"][start:end] + time_ids = [t for t in split_dict["time_ids"] if t >= start and t < end] + self.time_ids = torch.tensor(time_ids) - start + guru.info(f"{self.time_ids.min()=} {self.time_ids.max()=}") + # with open(osp.join(data_dir, "dataset.json")) as f: + # dataset_dict = json.load(f) + # self.num_frames = dataset_dict["num_exemplars"] + guru.info(f"{self.num_frames=}") + with open(osp.join(data_dir, "extra.json")) as f: + extra_dict = json.load(f) + self.fps = float(extra_dict["fps"]) + + # Load cameras. + if self.camera_type == "original": + Ks, w2cs = [], [] + for frame_name in self.frame_names: + with open(osp.join(data_dir, "camera", f"{frame_name}.json")) as f: + camera_dict = json.load(f) + focal_length = camera_dict["focal_length"] + principal_point = camera_dict["principal_point"] + Ks.append( + [ + [focal_length, 0.0, principal_point[0]], + [0.0, focal_length, principal_point[1]], + [0.0, 0.0, 1.0], + ] + ) + orientation = np.array(camera_dict["orientation"]) + position = np.array(camera_dict["position"]) + w2cs.append( + np.block( + [ + [orientation, -orientation @ position[:, None]], + [np.zeros((1, 3)), np.ones((1, 1))], + ] + ).astype(np.float32) + ) + self.Ks = torch.tensor(Ks) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(np.array(w2cs)) + elif self.camera_type == "refined": + Ks, w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [frame_name + ".png" for frame_name in self.frame_names], + ) + self.Ks = torch.from_numpy(Ks[:, :3, :3].astype(np.float32)) + self.Ks[:, :2] /= factor + self.w2cs = torch.from_numpy(w2cs.astype(np.float32)) + if not skip_load_imgs: + # Load images. + imgs = torch.from_numpy( + np.array( + [ + iio.imread( + osp.join(self.data_dir, f"rgb/{factor}x/{frame_name}.png") + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} images", + leave=False, + ) + ], + ) + ) + self.imgs = imgs[..., :3] / 255.0 + self.valid_masks = imgs[..., 3] / 255.0 + # Load masks. + self.masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/track_anything/", + f"{factor}x/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + if self.training: + # Load depths. + def load_depth(frame_name): + if self.depth_type == "lidar": + depth = np.load( + osp.join( + self.data_dir, + f"depth/{factor}x/{frame_name}.npy", + ) + )[..., 0] + else: + depth = np.load( + osp.join( + self.data_dir, + # f"flow3d_preprocessed/aligned_{self.depth_type}_allrect/", + # TODO: 1023 + f"flow3d_preprocessed/aligned_{self.depth_type}/", + # f"flow3d_preprocessed/noaligned_{self.depth_type}/", + f"{factor}x/{frame_name}.npy", + ) + ) + depth[depth < 1e-3] = 1e-3 + depth = 1.0 / depth + return depth + + self.depths = torch.from_numpy( + np.array( + [ + load_depth(frame_name) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} depths", + leave=False, + ) + ], + np.float32, + ) + ) + max_depth_values_per_frame = self.depths.reshape( + self.num_frames, -1 + ).max(1)[0] + max_depth_value = max_depth_values_per_frame.median() * 2.5 + print("max_depth_value", max_depth_value) + self.depths = torch.clamp(self.depths, 0, max_depth_value) + # Median filter depths. + # NOTE(hangg): This operator is very expensive. + if self.use_median_filter: + for i in tqdm( + range(self.num_frames), desc="Processing depths", leave=False + ): + depth = masked_median_blur( + self.depths[[i]].unsqueeze(1).to("cuda"), + ( + self.masks[[i]] + * self.valid_masks[[i]] + * (self.depths[[i]] > 0) + ) + .unsqueeze(1) + .to("cuda"), + )[0, 0].cpu() + self.depths[i] = depth * self.masks[i] + self.depths[i] * ( + 1 - self.masks[i] + ) + # Load the query pixels from 2D tracks. + self.query_tracks_2d = [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{factor}x/{frame_name}_{frame_name}.npy", + ) + ).astype(np.float32) + ) + for frame_name in self.frame_names + ] + guru.info( + f"{len(self.query_tracks_2d)=} {self.query_tracks_2d[0].shape=}" + ) + + # Load sam features. + # sam_feat_dir = osp.join( + # data_dir, f"flow3d_preprocessed/sam_features/{factor}x" + # ) + # assert osp.exists(sam_feat_dir), f"SAM features not exist!" + # sam_features, original_size, input_size = load_sam_features( + # sam_feat_dir, self.frame_names + # ) + # guru.info(f"{sam_features.shape=} {original_size=} {input_size=}") + # self.sam_features = sam_features + # self.sam_original_size = original_size + # self.sam_input_size = input_size + else: + # Load covisible masks. + self.covisible_masks = ( + torch.from_numpy( + np.array( + [ + iio.imread( + osp.join( + self.data_dir, + "flow3d_preprocessed/covisible/", + f"{factor}x/{split}/{frame_name}.png", + ) + ) + for frame_name in tqdm( + self.frame_names, + desc=f"Loading {self.split} covisible masks", + leave=False, + ) + ], + ) + ) + / 255.0 + ) + + if self.scene_norm_dict is None: + cached_scene_norm_dict_path = osp.join( + self.cache_dir, "scene_norm_dict.pth" + ) + if osp.exists(cached_scene_norm_dict_path) and self.load_from_cache: + print("loading cached scene norm dict...") + self.scene_norm_dict = torch.load( + osp.join(self.cache_dir, "scene_norm_dict.pth") + ) + elif self.training: + # Compute the scene scale and transform for normalization. + # Normalize the scene based on the foreground 3D tracks. + subsampled_tracks_3d = self.get_tracks_3d( + num_samples=10000, step=self.num_frames // 10, show_pbar=False + )[0] + scene_center = subsampled_tracks_3d.mean((0, 1)) + tracks_3d_centered = subsampled_tracks_3d - scene_center + min_scale = tracks_3d_centered.quantile(0.05, dim=0) + max_scale = tracks_3d_centered.quantile(0.95, dim=0) + scale = torch.max(max_scale - min_scale).item() / 2.0 + original_up = -F.normalize(self.w2cs[:, 1, :3].mean(0), dim=-1) + target_up = original_up.new_tensor([0.0, 0.0, 1.0]) + R = roma.rotvec_to_rotmat( + F.normalize(original_up.cross(target_up, dim=-1), dim=-1) + * original_up.dot(target_up).acos_() + ) + transfm = rt_to_mat4(R, torch.einsum("ij,j->i", -R, scene_center)) + self.scene_norm_dict = SceneNormDict(scale=scale, transfm=transfm) + torch.save(self.scene_norm_dict, cached_scene_norm_dict_path) + else: + raise ValueError("scene_norm_dict must be provided for validation.") + + # Normalize the scene. + scale = self.scene_norm_dict["scale"] + transfm = self.scene_norm_dict["transfm"] + self.w2cs = self.w2cs @ torch.linalg.inv(transfm) + self.w2cs[:, :3, 3] /= scale + if self.training and not skip_load_imgs: + self.depths /= scale + + if not skip_load_imgs: + guru.info( + f"{self.imgs.shape=} {self.valid_masks.shape=} {self.masks.shape=}" + ) + + @property + def num_frames(self) -> int: + return len(self.frame_names) + + def __len__(self): + return self.imgs.shape[0] + + def get_w2cs(self) -> torch.Tensor: + return self.w2cs + + def get_Ks(self) -> torch.Tensor: + return self.Ks + + def get_image(self, index: int) -> torch.Tensor: + return self.imgs[index] + + def get_depth(self, index: int) -> torch.Tensor: + return self.depths[index] + + def get_masks(self, index: int) -> torch.Tensor: + return self.masks[index] + + def get_img_wh(self) -> tuple[int, int]: + return iio.imread( + osp.join(self.data_dir, f"rgb/{self.factor}x/{self.frame_names[0]}.png") + ).shape[1::-1] + + # def get_sam_features(self) -> list[torch.Tensor, tuple[int, int], tuple[int, int]]: + # return self.sam_features, self.sam_original_size, self.sam_input_size + + def get_tracks_3d( + self, num_samples: int, step: int = 1, show_pbar: bool = True, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """Get 3D tracks from the dataset. + + Args: + num_samples (int | None): The number of samples to fetch. If None, + fetch all samples. If not None, fetch roughly a same number of + samples across each frame. Note that this might result in + number of samples less than what is specified. + step (int): The step to temporally subsample the track. + """ + assert ( + self.split == "train" + ), "fetch_tracks_3d is only available for the training split." + cached_track_3d_path = osp.join(self.cache_dir, f"tracks_3d_{num_samples}.pth") + if osp.exists(cached_track_3d_path) and step == 1 and self.load_from_cache: + print("loading cached 3d tracks data...") + start, end = self.start, self.end + cached_track_3d_data = torch.load(cached_track_3d_path) + tracks_3d, visibles, invisibles, confidences, track_colors = ( + cached_track_3d_data["tracks_3d"][:, start:end], + cached_track_3d_data["visibles"][:, start:end], + cached_track_3d_data["invisibles"][:, start:end], + cached_track_3d_data["confidences"][:, start:end], + cached_track_3d_data["track_colors"], + ) + return tracks_3d, visibles, invisibles, confidences, track_colors + + # Load 2D tracks. + raw_tracks_2d = [] + candidate_frames = list(range(0, self.num_frames, step)) + num_sampled_frames = len(candidate_frames) + for i in ( + tqdm(candidate_frames, desc="Loading 2D tracks", leave=False) + if show_pbar + else candidate_frames + ): + curr_num_samples = self.query_tracks_2d[i].shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + track_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + track_sels = np.arange(0, curr_num_samples) + curr_tracks_2d = [] + for j in range(0, self.num_frames, step): + if i == j: + target_tracks_2d = self.query_tracks_2d[i] + else: + target_tracks_2d = torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[i]}_" + f"{self.frame_names[j]}.npy", + ) + ).astype(np.float32) + ) + curr_tracks_2d.append(target_tracks_2d[track_sels]) + raw_tracks_2d.append(torch.stack(curr_tracks_2d, dim=1)) + guru.info(f"{step=} {len(raw_tracks_2d)=} {raw_tracks_2d[0].shape=}") + + # Process 3D tracks. + inv_Ks = torch.linalg.inv(self.Ks)[::step] + c2ws = torch.linalg.inv(self.w2cs)[::step] + H, W = self.imgs.shape[1:3] + filtered_tracks_3d, filtered_visibles, filtered_track_colors = [], [], [] + filtered_invisibles, filtered_confidences = [], [] + masks = self.masks * self.valid_masks * (self.depths > 0) + masks = (masks > 0.5).float() + for i, tracks_2d in enumerate(raw_tracks_2d): + tracks_2d = tracks_2d.swapdims(0, 1) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + track_depths = F.grid_sample( + self.depths[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + padding_mode="border", + )[:, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths + ) + tracks_3d = torch.einsum( + "nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0) + )[..., :3] + # Filter out out-of-mask tracks. + is_in_masks = ( + F.grid_sample( + masks[::step, None], + normalize_coords(tracks_2d[..., None, :], H, W), + align_corners=True, + ).squeeze() + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + # Get track's color from the query frame. + track_colors = ( + F.grid_sample( + self.imgs[i * step : i * step + 1].permute(0, 3, 1, 2), + normalize_coords(tracks_2d[i : i + 1, None, :], H, W), + align_corners=True, + padding_mode="border", + ) + .squeeze() + .T + ) + # at least visible 5% of the time, otherwise discard + visible_counts = visibles.sum(0) + valid = visible_counts >= min( + int(0.05 * self.num_frames), + visible_counts.float().quantile(0.1).item(), + ) + + filtered_tracks_3d.append(tracks_3d[:, valid]) + filtered_visibles.append(visibles[:, valid]) + filtered_invisibles.append(invisibles[:, valid]) + filtered_confidences.append(confidences[:, valid]) + filtered_track_colors.append(track_colors[valid]) + + filtered_tracks_3d = torch.cat(filtered_tracks_3d, dim=1).swapdims(0, 1) + filtered_visibles = torch.cat(filtered_visibles, dim=1).swapdims(0, 1) + filtered_invisibles = torch.cat(filtered_invisibles, dim=1).swapdims(0, 1) + filtered_confidences = torch.cat(filtered_confidences, dim=1).swapdims(0, 1) + filtered_track_colors = torch.cat(filtered_track_colors, dim=0) + if step == 1: + torch.save( + { + "tracks_3d": filtered_tracks_3d, + "visibles": filtered_visibles, + "invisibles": filtered_invisibles, + "confidences": filtered_confidences, + "track_colors": filtered_track_colors, + }, + cached_track_3d_path, + ) + return ( + filtered_tracks_3d, + filtered_visibles, + filtered_invisibles, + filtered_confidences, + filtered_track_colors, + ) + + def get_bkgd_points( + self, num_samples: int, **kwargs + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + H, W = self.imgs.shape[1:3] + grid = torch.stack( + torch.meshgrid( + torch.arange(W, dtype=torch.float32), + torch.arange(H, dtype=torch.float32), + indexing="xy", + ), + dim=-1, + ) + candidate_frames = list(range(self.num_frames)) + num_sampled_frames = len(candidate_frames) + bkgd_points, bkgd_point_normals, bkgd_point_colors = [], [], [] + for i in tqdm(candidate_frames, desc="Loading bkgd points", leave=False): + img = self.imgs[i] + depth = self.depths[i] + bool_mask = ((1.0 - self.masks[i]) * self.valid_masks[i] * (depth > 0)).to( + torch.bool + ) + w2c = self.w2cs[i] + K = self.Ks[i] + points = ( + torch.einsum( + "ij,pj->pi", + torch.linalg.inv(K), + F.pad(grid[bool_mask], (0, 1), value=1.0), + ) + * depth[bool_mask][:, None] + ) + points = torch.einsum( + "ij,pj->pi", torch.linalg.inv(w2c)[:3], F.pad(points, (0, 1), value=1.0) + ) + point_normals = normal_from_depth_image(depth, K, w2c)[bool_mask] + point_colors = img[bool_mask] + curr_num_samples = points.shape[0] + num_samples_per_frame = ( + int(np.floor(num_samples / num_sampled_frames)) + if i != candidate_frames[-1] + else num_samples + - (num_sampled_frames - 1) + * int(np.floor(num_samples / num_sampled_frames)) + ) + if num_samples_per_frame < curr_num_samples: + point_sels = np.random.choice( + curr_num_samples, (num_samples_per_frame,), replace=False + ) + else: + point_sels = np.arange(0, curr_num_samples) + bkgd_points.append(points[point_sels]) + bkgd_point_normals.append(point_normals[point_sels]) + bkgd_point_colors.append(point_colors[point_sels]) + bkgd_points = torch.cat(bkgd_points, dim=0) + bkgd_point_normals = torch.cat(bkgd_point_normals, dim=0) + bkgd_point_colors = torch.cat(bkgd_point_colors, dim=0) + return bkgd_points, bkgd_point_normals, bkgd_point_colors + + def get_video_dataset(self) -> Dataset: + return iPhoneDatasetVideoView(self) + + def __getitem__(self, index: int): + if self.training: + index = np.random.randint(0, self.num_frames) + data = { + # (). + "frame_names": self.frame_names[index], + # (). + "ts": self.time_ids[index], + # (4, 4). + "w2cs": self.w2cs[index], + # (3, 3). + "Ks": self.Ks[index], + # (H, W, 3). + "imgs": self.imgs[index], + # (H, W). + "valid_masks": self.valid_masks[index], + # (H, W). + "masks": self.masks[index], + } + if self.training: + # (H, W). + data["depths"] = self.depths[index] + # (P, 2). + data["query_tracks_2d"] = self.query_tracks_2d[index][:, :2] + target_inds = torch.from_numpy( + np.random.choice( + self.num_frames, (self.num_targets_per_frame,), replace=False + ) + ) + # (N, P, 4). + target_tracks_2d = torch.stack( + [ + torch.from_numpy( + np.load( + osp.join( + self.data_dir, + "flow3d_preprocessed/2d_tracks/", + f"{self.factor}x/" + f"{self.frame_names[index]}_" + f"{self.frame_names[target_index.item()]}.npy", + ) + ).astype(np.float32) + ) + for target_index in target_inds + ], + dim=0, + ) + # (N,). + target_ts = self.time_ids[target_inds] + data["target_ts"] = target_ts + # (N, 4, 4). + data["target_w2cs"] = self.w2cs[target_ts] + # (N, 3, 3). + data["target_Ks"] = self.Ks[target_ts] + # (N, P, 2). + data["target_tracks_2d"] = target_tracks_2d[..., :2] + # (N, P). + ( + data["target_visibles"], + data["target_invisibles"], + data["target_confidences"], + ) = parse_tapir_track_info( + target_tracks_2d[..., 2], target_tracks_2d[..., 3] + ) + # (N, P). + data["target_track_depths"] = F.grid_sample( + self.depths[target_inds, None], + normalize_coords( + target_tracks_2d[..., None, :2], + self.imgs.shape[1], + self.imgs.shape[2], + ), + align_corners=True, + padding_mode="border", + )[:, 0, :, 0] + else: + # (H, W). + data["covisible_masks"] = self.covisible_masks[index] + return data + + def preprocess(self, data): + return data + + +class iPhoneDatasetKeypointView(Dataset): + """Return a dataset view of the annotated keypoints.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + assert self.dataset.split == "train" + # Load 2D keypoints. + keypoint_paths = sorted( + glob(osp.join(self.dataset.data_dir, "keypoint/2x/train/0_*.json")) + ) + keypoints = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints.append(json.load(f)) + time_ids = [ + int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths + ] + # only use time ids that are in the dataset. + start = self.dataset.start + time_ids = [t - start for t in time_ids if t - start in self.dataset.time_ids] + self.time_ids = torch.tensor(time_ids) + self.time_pairs = torch.tensor(list(product(self.time_ids, repeat=2))) + self.index_pairs = torch.tensor( + list(product(range(len(self.time_ids)), repeat=2)) + ) + self.keypoints = torch.tensor(keypoints, dtype=torch.float32) + self.keypoints[..., :2] *= 2.0 / self.dataset.factor + + def __len__(self): + return len(self.time_pairs) + + def __getitem__(self, index: int): + ts = self.time_pairs[index] + return { + "ts": ts, + "w2cs": self.dataset.w2cs[ts], + "Ks": self.dataset.Ks[ts], + "imgs": self.dataset.imgs[ts], + "keypoints": self.keypoints[self.index_pairs[index]], + } + + +class iPhoneDatasetVideoView(Dataset): + """Return a dataset view of the video trajectory.""" + + def __init__(self, dataset: iPhoneDataset): + super().__init__() + self.dataset = dataset + self.fps = self.dataset.fps + assert self.dataset.split == "train" + + def __len__(self): + return self.dataset.num_frames + + def __getitem__(self, index): + return { + "frame_names": self.dataset.frame_names[index], + "ts": index, + "w2cs": self.dataset.w2cs[index], + "Ks": self.dataset.Ks[index], + "imgs": self.dataset.imgs[index], + "depths": self.dataset.depths[index], + "masks": self.dataset.masks[index], + } + + +""" +class iPhoneDataModule(BaseDataModule[iPhoneDataset]): + def __init__( + self, + data_dir: str, + factor: int = 1, + start: int = 0, + end: int = -1, + depth_type: Literal[ + "midas", + "depth_anything", + "lidar", + "depth_anything_colmap", + ] = "depth_anything_colmap", + camera_type: Literal["original", "refined"] = "refined", + use_median_filter: bool = False, + num_targets_per_frame: int = 1, + load_from_cache: bool = False, + **kwargs, + ): + super().__init__(dataset_cls=iPhoneDataset, **kwargs) + self.data_dir = data_dir + self.start = start + self.end = end + self.factor = factor + self.depth_type = depth_type + self.camera_type = camera_type + self.use_median_filter = use_median_filter + self.num_targets_per_frame = num_targets_per_frame + self.load_from_cache = load_from_cache + + self.val_loader_tasks = ["img", "keypoint"] + + def setup(self, *_, **__) -> None: + guru.info("Loading train dataset...") + self.train_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=True, + split="train", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + num_targets_per_frame=self.num_targets_per_frame, + max_steps=self.max_steps * self.batch_size, + load_from_cache=self.load_from_cache, + ) + if self.train_dataset.has_validation: + guru.info("Loading val dataset...") + self.val_dataset = self.dataset_cls( + data_dir=self.data_dir, + training=False, + split="val", + start=self.start, + end=self.end, + factor=self.factor, + depth_type=self.depth_type, # type: ignore + camera_type=self.camera_type, # type: ignore + use_median_filter=self.use_median_filter, + scene_norm_dict=self.train_dataset.scene_norm_dict, + load_from_cache=self.load_from_cache, + ) + else: + # Dummy validation set. + self.val_dataset = TensorDataset(torch.zeros(0)) # type: ignore + self.keypoint_dataset = iPhoneDatasetKeypointView(self.train_dataset) + self.video_dataset = self.train_dataset.get_video_dataset() + guru.success("Loading finished!") + + def train_dataloader(self) -> DataLoader: + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=iPhoneDataset.train_collate_fn, + ) + + def val_dataloader(self) -> list[DataLoader]: + return [DataLoader(self.val_dataset), DataLoader(self.keypoint_dataset)] + """ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/data/utils.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..00841ce5236c9a3b98e4a7c7d0ad94a137b9df13 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/data/utils.py @@ -0,0 +1,360 @@ +from typing import List, Optional, Tuple, TypedDict + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.modules.utils import _pair, _quadruple + +UINT16_MAX = 65535 + + +class SceneNormDict(TypedDict): + scale: float + transfm: torch.Tensor + + +def to_device(batch, device): + if isinstance(batch, dict): + return {k: to_device(v, device) for k, v in batch.items()} + if isinstance(batch, (list, tuple)): + return [to_device(v, device) for v in batch] + if isinstance(batch, torch.Tensor): + return batch.to(device) + return batch + + +def normalize_coords(coords, h, w): + assert coords.shape[-1] == 2 + return coords / torch.tensor([w - 1.0, h - 1.0], device=coords.device) * 2 - 1.0 + + +def postprocess_occlusions(occlusions, expected_dist): + """Postprocess occlusions to boolean visible flag. + + Args: + occlusions: [-inf, inf], np.float32 + expected_dist:, [-inf, inf], np.float32 + + Returns: + visibles: bool + """ + + def sigmoid(x): + if x.dtype == np.ndarray: + return 1 / (1 + np.exp(-x)) + else: + return torch.sigmoid(x) + + visibles = (1 - sigmoid(occlusions)) * (1 - sigmoid(expected_dist)) > 0.5 + return visibles + + +def parse_tapir_track_info(occlusions, expected_dist): + """ + return: + valid_visible: mask of visible & confident points + valid_invisible: mask of invisible & confident points + confidence: clamped confidence scores (all < 0.5 -> 0) + """ + visiblility = 1 - F.sigmoid(occlusions) + confidence = 1 - F.sigmoid(expected_dist) + valid_visible = visiblility * confidence > 0.5 + valid_invisible = (1 - visiblility) * confidence > 0.5 + # set all confidence < 0.5 to 0 + confidence = confidence * (valid_visible | valid_invisible).float() + return valid_visible, valid_invisible, confidence + + +def get_tracks_3d_for_query_frame( + query_index: int, + query_img: torch.Tensor, + tracks_2d: torch.Tensor, + depths: torch.Tensor, + masks: torch.Tensor, + inv_Ks: torch.Tensor, + c2ws: torch.Tensor, +): + """ + :param query_index (int) + :param query_img [H, W, 3] + :param tracks_2d [N, T, 4] + :param depths [T, H, W] + :param masks [T, H, W] + :param inv_Ks [T, 3, 3] + :param c2ws [T, 4, 4] + returns ( + tracks_3d [N, T, 3] + track_colors [N, 3] + visibles [N, T] + invisibles [N, T] + confidences [N, T] + ) + """ + T, H, W = depths.shape + query_img = query_img[None].permute(0, 3, 1, 2) # (1, 3, H, W) + tracks_2d = tracks_2d.swapaxes(0, 1) # (T, N, 4) + tracks_2d, occs, dists = ( + tracks_2d[..., :2], + tracks_2d[..., 2], + tracks_2d[..., 3], + ) + # visibles = postprocess_occlusions(occs, dists) + # (T, N), (T, N), (T, N) + visibles, invisibles, confidences = parse_tapir_track_info(occs, dists) + # Unproject 2D tracks to 3D. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + track_depths = F.grid_sample( + depths[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + padding_mode="border", + )[:, 0, 0] + tracks_3d = ( + torch.einsum( + "nij,npj->npi", + inv_Ks, + F.pad(tracks_2d, (0, 1), value=1.0), + ) + * track_depths[..., None] + ) + tracks_3d = torch.einsum("nij,npj->npi", c2ws, F.pad(tracks_3d, (0, 1), value=1.0))[ + ..., :3 + ] + # Filter out out-of-mask tracks. + # (T, 1, H, W), (T, 1, N, 2) -> (T, 1, 1, N) + is_in_masks = ( + F.grid_sample( + masks[:, None], + normalize_coords(tracks_2d[:, None], H, W), + align_corners=True, + )[:, 0, 0] + == 1 + ) + visibles *= is_in_masks + invisibles *= is_in_masks + confidences *= is_in_masks.float() + + # valid if in the fg mask at least 40% of the time + # in_mask_counts = is_in_masks.sum(0) + # t = 0.25 + # thresh = min(t * T, in_mask_counts.float().quantile(t).item()) + # valid = in_mask_counts > thresh + valid = is_in_masks[query_index] + # valid if visible 5% of the time + visible_counts = visibles.sum(0) + valid = valid & ( + visible_counts + >= min( + int(0.05 * T), + visible_counts.float().quantile(0.1).item(), + ) + ) + + # Get track's color from the query frame. + # (1, 3, H, W), (1, 1, N, 2) -> (1, 3, 1, N) -> (N, 3) + track_colors = F.grid_sample( + query_img, + normalize_coords(tracks_2d[query_index : query_index + 1, None], H, W), + align_corners=True, + padding_mode="border", + )[0, :, 0].T + return ( + tracks_3d[:, valid].swapdims(0, 1), + track_colors[valid], + visibles[:, valid].swapdims(0, 1), + invisibles[:, valid].swapdims(0, 1), + confidences[:, valid].swapdims(0, 1), + ) + + +def _get_padding(x, k, stride, padding, same: bool): + if same: + ih, iw = x.size()[2:] + if ih % stride[0] == 0: + ph = max(k[0] - stride[0], 0) + else: + ph = max(k[0] - (ih % stride[0]), 0) + if iw % stride[1] == 0: + pw = max(k[1] - stride[1], 0) + else: + pw = max(k[1] - (iw % stride[1]), 0) + pl = pw // 2 + pr = pw - pl + pt = ph // 2 + pb = ph - pt + padding = (pl, pr, pt, pb) + else: + padding = padding + return padding + + +def median_filter_2d(x, kernel_size=3, stride=1, padding=1, same: bool = True): + """ + :param x [B, C, H, W] + """ + k = _pair(kernel_size) + stride = _pair(stride) # convert to tuple + padding = _quadruple(padding) # convert to l, r, t, b + # using existing pytorch functions and tensor ops so that we get autograd, + # would likely be more efficient to implement from scratch at C/Cuda level + x = F.pad(x, _get_padding(x, k, stride, padding, same), mode="reflect") + x = x.unfold(2, k[0], stride[0]).unfold(3, k[1], stride[1]) + x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0] + return x + + +def masked_median_blur(image, mask, kernel_size=11): + """ + Args: + image: [B, C, H, W] + mask: [B, C, H, W] + kernel_size: int + """ + assert image.shape == mask.shape + if not isinstance(image, torch.Tensor): + raise TypeError(f"Input type is not a torch.Tensor. Got {type(image)}") + + if not len(image.shape) == 4: + raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {image.shape}") + + padding: Tuple[int, int] = _compute_zero_padding((kernel_size, kernel_size)) + + # prepare kernel + kernel: torch.Tensor = get_binary_kernel2d((kernel_size, kernel_size)).to(image) + b, c, h, w = image.shape + + # map the local window to single vector + features: torch.Tensor = F.conv2d( + image.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + masks: torch.Tensor = F.conv2d( + mask.reshape(b * c, 1, h, w), kernel, padding=padding, stride=1 + ) + features = features.view(b, c, -1, h, w).permute( + 0, 1, 3, 4, 2 + ) # BxCxxHxWx(K_h * K_w) + min_value, max_value = features.min(), features.max() + masks = masks.view(b, c, -1, h, w).permute(0, 1, 3, 4, 2) # BxCxHxWx(K_h * K_w) + index_invalid = (1 - masks).nonzero(as_tuple=True) + index_b, index_c, index_h, index_w, index_k = index_invalid + features[(index_b[::2], index_c[::2], index_h[::2], index_w[::2], index_k[::2])] = ( + min_value + ) + features[ + (index_b[1::2], index_c[1::2], index_h[1::2], index_w[1::2], index_k[1::2]) + ] = max_value + # compute the median along the feature axis + median: torch.Tensor = torch.median(features, dim=-1)[0] + + return median + + +def _compute_zero_padding(kernel_size: Tuple[int, int]) -> Tuple[int, int]: + r"""Utility function that computes zero padding tuple.""" + computed: List[int] = [(k - 1) // 2 for k in kernel_size] + return computed[0], computed[1] + + +def get_binary_kernel2d( + window_size: tuple[int, int] | int, + *, + device: Optional[torch.device] = None, + dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + from kornia + Create a binary kernel to extract the patches. + If the window size is HxW will create a (H*W)x1xHxW kernel. + """ + ky, kx = _unpack_2d_ks(window_size) + + window_range = kx * ky + + kernel = torch.zeros((window_range, window_range), device=device, dtype=dtype) + idx = torch.arange(window_range, device=device) + kernel[idx, idx] += 1.0 + return kernel.view(window_range, 1, ky, kx) + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, "2D Kernel size should have a length of 2." + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + + return (ky, kx) + + +## Functions from GaussianShader. +def ndc_2_cam(ndc_xyz, intrinsic, W, H): + inv_scale = torch.tensor([[W - 1, H - 1]], device=ndc_xyz.device) + cam_z = ndc_xyz[..., 2:3] + cam_xy = ndc_xyz[..., :2] * inv_scale * cam_z + cam_xyz = torch.cat([cam_xy, cam_z], dim=-1) + cam_xyz = cam_xyz @ torch.inverse(intrinsic[0, ...].t()) + return cam_xyz + + +def depth2point_cam(sampled_depth, ref_intrinsic): + B, N, C, H, W = sampled_depth.shape + valid_z = sampled_depth + valid_x = torch.arange(W, dtype=torch.float32, device=sampled_depth.device) / ( + W - 1 + ) + valid_y = torch.arange(H, dtype=torch.float32, device=sampled_depth.device) / ( + H - 1 + ) + valid_y, valid_x = torch.meshgrid(valid_y, valid_x, indexing="ij") + # B,N,H,W + valid_x = valid_x[None, None, None, ...].expand(B, N, C, -1, -1) + valid_y = valid_y[None, None, None, ...].expand(B, N, C, -1, -1) + ndc_xyz = torch.stack([valid_x, valid_y, valid_z], dim=-1).view( + B, N, C, H, W, 3 + ) # 1, 1, 5, 512, 640, 3 + cam_xyz = ndc_2_cam(ndc_xyz, ref_intrinsic, W, H) # 1, 1, 5, 512, 640, 3 + return ndc_xyz, cam_xyz + + +def depth2point_world(depth_image, intrinsic_matrix, extrinsic_matrix): + # depth_image: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + _, xyz_cam = depth2point_cam( + depth_image[None, None, None, ...], intrinsic_matrix[None, ...] + ) + xyz_cam = xyz_cam.reshape(-1, 3) + xyz_world = torch.cat( + [xyz_cam, torch.ones_like(xyz_cam[..., 0:1])], dim=-1 + ) @ torch.inverse(extrinsic_matrix).transpose(0, 1) + xyz_world = xyz_world[..., :3] + + return xyz_world + + +def depth_pcd2normal(xyz): + hd, wd, _ = xyz.shape + bottom_point = xyz[..., 2:hd, 1 : wd - 1, :] + top_point = xyz[..., 0 : hd - 2, 1 : wd - 1, :] + right_point = xyz[..., 1 : hd - 1, 2:wd, :] + left_point = xyz[..., 1 : hd - 1, 0 : wd - 2, :] + left_to_right = right_point - left_point + bottom_to_top = top_point - bottom_point + xyz_normal = torch.cross(left_to_right, bottom_to_top, dim=-1) + xyz_normal = torch.nn.functional.normalize(xyz_normal, p=2, dim=-1) + xyz_normal = torch.nn.functional.pad( + xyz_normal.permute(2, 0, 1), (1, 1, 1, 1), mode="constant" + ).permute(1, 2, 0) + return xyz_normal + + +def normal_from_depth_image(depth, intrinsic_matrix, extrinsic_matrix): + # depth: (H, W), intrinsic_matrix: (3, 3), extrinsic_matrix: (4, 4) + # xyz_normal: (H, W, 3) + xyz_world = depth2point_world(depth, intrinsic_matrix, extrinsic_matrix) # (HxW, 3) + xyz_world = xyz_world.reshape(*depth.shape, 3) + xyz_normal = depth_pcd2normal(xyz_world) + + return xyz_normal diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/init_utils.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/init_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..16b8e035761be88f50d38f6e935a3537f8c74dad --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/init_utils.py @@ -0,0 +1,644 @@ +import time +from typing import Literal + +import cupy as cp +import imageio.v3 as iio +import numpy as np + +# from pytorch3d.ops import sample_farthest_points +import roma +import torch +import torch.nn.functional as F +from cuml import HDBSCAN, KMeans +from loguru import logger as guru +from matplotlib.pyplot import get_cmap +from tqdm import tqdm +from viser import ViserServer + +from flow3d.loss_utils import ( + compute_accel_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + get_weights_for_procrustes, + knn, + masked_l1_loss, +) +from flow3d.params import GaussianParams, MotionBases +from flow3d.tensor_dataclass import StaticObservations, TrackObservations +from flow3d.transforms import cont_6d_to_rmat, rt_to_mat4, solve_procrustes +from flow3d.vis.utils import draw_keypoints_video, get_server, project_2d_tracks + + +def init_fg_from_tracks_3d( + cano_t: int, tracks_3d: TrackObservations, motion_coefs: torch.Tensor +) -> GaussianParams: + """ + using dataclasses individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_fg = tracks_3d.xyz.shape[0] + + # Initialize gaussian colors. + colors = torch.logit(tracks_3d.colors) + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(tracks_3d.xyz[:, cano_t], 3) + dists = torch.from_numpy(dists) + scales = dists.mean(dim=-1, keepdim=True) + scales = scales.clamp(torch.quantile(scales, 0.05), torch.quantile(scales, 0.95)) + scales = torch.log(scales.repeat(1, 3)) + # Initialize gaussian means. + means = tracks_3d.xyz[:, cano_t] + # Initialize gaussian orientations as random. + quats = torch.rand(num_fg, 4) + # Initialize gaussian opacities. + opacities = torch.logit(torch.full((num_fg,), 0.7)) + gaussians = GaussianParams(means, quats, scales, colors, opacities, motion_coefs) + return gaussians + + +def init_bg( + points: StaticObservations, +) -> GaussianParams: + """ + using dataclasses instead of individual tensors so we know they're consistent + and are always masked/filtered together + """ + num_init_bg_gaussians = points.xyz.shape[0] + bg_scene_center = points.xyz.mean(0) + bg_points_centered = points.xyz - bg_scene_center + bg_min_scale = bg_points_centered.quantile(0.05, dim=0) + bg_max_scale = bg_points_centered.quantile(0.95, dim=0) + bg_scene_scale = torch.max(bg_max_scale - bg_min_scale).item() / 2.0 + bkdg_colors = torch.logit(points.colors) + + # Initialize gaussian scales: find the average of the three nearest + # neighbors in the first frame for each point and use that as the + # scale. + dists, _ = knn(points.xyz, 3) + dists = torch.from_numpy(dists) + bg_scales = dists.mean(dim=-1, keepdim=True) + bkdg_scales = torch.log(bg_scales.repeat(1, 3)) + + bg_means = points.xyz + + # Initialize gaussian orientations by normals. + local_normals = points.normals.new_tensor([[0.0, 0.0, 1.0]]).expand_as( + points.normals + ) + bg_quats = roma.rotvec_to_unitquat( + F.normalize(local_normals.cross(points.normals), dim=-1) + * (local_normals * points.normals).sum(-1, keepdim=True).acos_() + ).roll(1, dims=-1) + bg_opacities = torch.logit(torch.full((num_init_bg_gaussians,), 0.7)) + gaussians = GaussianParams( + bg_means, + bg_quats, + bkdg_scales, + bkdg_colors, + bg_opacities, + scene_center=bg_scene_center, + scene_scale=bg_scene_scale, + ) + return gaussians + + +def init_motion_params_with_procrustes( + tracks_3d: TrackObservations, + num_bases: int, + rot_type: Literal["quat", "6d"], + cano_t: int, + cluster_init_method: str = "kmeans", + min_mean_weight: float = 0.1, + vis: bool = False, + port: int | None = None, +) -> tuple[MotionBases, torch.Tensor, TrackObservations]: + device = tracks_3d.xyz.device + num_frames = tracks_3d.xyz.shape[1] + # sample centers and get initial se3 motion bases by solving procrustes + means_cano = tracks_3d.xyz[:, cano_t].clone() # [num_gaussians, 3] + + # remove outliers + scene_center = means_cano.median(dim=0).values + print(f"{scene_center=}") + dists = torch.norm(means_cano - scene_center, dim=-1) + dists_th = torch.quantile(dists, 0.95) + valid_mask = dists < dists_th + + # remove tracks that are not visible in any frame + valid_mask = valid_mask & tracks_3d.visibles.any(dim=1) + print(f"{valid_mask.sum()=}") + + tracks_3d = tracks_3d.filter_valid(valid_mask) + + if vis and port is not None: + server = get_server(port) + try: + pts = tracks_3d.xyz.cpu().numpy() + clrs = tracks_3d.colors.cpu().numpy() + while True: + for t in range(num_frames): + server.scene.add_point_cloud("points", pts[:, t], clrs) + time.sleep(0.3) + except KeyboardInterrupt: + pass + + means_cano = means_cano[valid_mask] + + sampled_centers, num_bases, labels = sample_initial_bases_centers( + cluster_init_method, cano_t, tracks_3d, num_bases + ) + + # assign each point to the label to compute the cluster weight + ids, counts = labels.unique(return_counts=True) + ids = ids[counts > 100] + num_bases = len(ids) + sampled_centers = sampled_centers[:, ids] + print(f"{num_bases=} {sampled_centers.shape=}") + + # compute basis weights from the distance to the cluster centers + dists2centers = torch.norm(means_cano[:, None] - sampled_centers, dim=-1) + motion_coefs = 10 * torch.exp(-dists2centers) + + init_rots, init_ts = [], [] + + if rot_type == "quat": + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0], device=device) + rot_dim = 4 + else: + id_rot = torch.tensor([1.0, 0.0, 0.0, 0.0, 1.0, 0.0], device=device) + rot_dim = 6 + + init_rots = id_rot.reshape(1, 1, rot_dim).repeat(num_bases, num_frames, 1) + init_ts = torch.zeros(num_bases, num_frames, 3, device=device) + errs_before = np.full((num_bases, num_frames), -1.0) + errs_after = np.full((num_bases, num_frames), -1.0) + + tgt_ts = list(range(cano_t - 1, -1, -1)) + list(range(cano_t, num_frames)) + print(f"{tgt_ts=}") + skipped_ts = {} + for n, cluster_id in enumerate(ids): + mask_in_cluster = labels == cluster_id + cluster = tracks_3d.xyz[mask_in_cluster].transpose( + 0, 1 + ) # [num_frames, n_pts, 3] + visibilities = tracks_3d.visibles[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + confidences = tracks_3d.confidences[mask_in_cluster].swapaxes( + 0, 1 + ) # [num_frames, n_pts] + weights = get_weights_for_procrustes(cluster, visibilities) + prev_t = cano_t + cluster_skip_ts = [] + for cur_t in tgt_ts: + # compute pairwise transform from cano_t + procrustes_weights = ( + weights[cano_t] + * weights[cur_t] + * (confidences[cano_t] + confidences[cur_t]) + / 2 + ) + if procrustes_weights.sum() < min_mean_weight * num_frames: + init_rots[n, cur_t] = init_rots[n, prev_t] + init_ts[n, cur_t] = init_ts[n, prev_t] + cluster_skip_ts.append(cur_t) + else: + se3, (err, err_before) = solve_procrustes( + cluster[cano_t], + cluster[cur_t], + weights=procrustes_weights, + enforce_se3=True, + rot_type=rot_type, + ) + init_rot, init_t, _ = se3 + assert init_rot.shape[-1] == rot_dim + # double cover + if rot_type == "quat" and torch.linalg.norm( + init_rot - init_rots[n][prev_t] + ) > torch.linalg.norm(-init_rot - init_rots[n][prev_t]): + init_rot = -init_rot + init_rots[n, cur_t] = init_rot + init_ts[n, cur_t] = init_t + if err == np.nan: + print(f"{cur_t=} {err=}") + print(f"{procrustes_weights.isnan().sum()=}") + if err_before == np.nan: + print(f"{cur_t=} {err_before=}") + print(f"{procrustes_weights.isnan().sum()=}") + errs_after[n, cur_t] = err + errs_before[n, cur_t] = err_before + prev_t = cur_t + skipped_ts[cluster_id.item()] = cluster_skip_ts + + guru.info(f"{skipped_ts=}") + guru.info( + "procrustes init median error: {:.5f} => {:.5f}".format( + np.median(errs_before[errs_before > 0]), + np.median(errs_after[errs_after > 0]), + ) + ) + guru.info( + "procrustes init mean error: {:.5f} => {:.5f}".format( + np.mean(errs_before[errs_before > 0]), np.mean(errs_after[errs_after > 0]) + ) + ) + guru.info(f"{init_rots.shape=}, {init_ts.shape=}, {motion_coefs.shape=}") + + if vis: + server = get_server(port) + center_idcs = torch.argmin(dists2centers, dim=0) + print(f"{dists2centers.shape=} {center_idcs.shape=}") + vis_se3_init_3d(server, init_rots, init_ts, means_cano[center_idcs]) + vis_tracks_3d(server, tracks_3d.xyz[center_idcs].numpy(), name="center_tracks") + import ipdb + + ipdb.set_trace() + + bases = MotionBases(init_rots, init_ts) + return bases, motion_coefs, tracks_3d + + +def run_initial_optim( + fg: GaussianParams, + bases: MotionBases, + tracks_3d: TrackObservations, + Ks: torch.Tensor, + w2cs: torch.Tensor, + num_iters: int = 1000, + use_depth_range_loss: bool = False, +): + """ + :param motion_rots: [num_bases, num_frames, 4|6] + :param motion_transls: [num_bases, num_frames, 3] + :param motion_coefs: [num_bases, num_frames] + :param means: [num_gaussians, 3] + """ + optimizer = torch.optim.Adam( + [ + {"params": bases.params["rots"], "lr": 1e-2}, + {"params": bases.params["transls"], "lr": 3e-2}, + {"params": fg.params["motion_coefs"], "lr": 1e-2}, + {"params": fg.params["means"], "lr": 1e-3}, + ], + ) + scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, gamma=0.1 ** (1 / num_iters) + ) + G = fg.params.means.shape[0] + num_frames = bases.num_frames + device = bases.params["rots"].device + + w_smooth_func = lambda i, min_v, max_v, th: ( + min_v if i <= th else (max_v - min_v) * (i - th) / (num_iters - th) + min_v + ) + + gt_2d, gt_depth = project_2d_tracks( + tracks_3d.xyz.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + # (G, T, 2) + gt_2d = gt_2d.swapaxes(0, 1) + # (G, T) + gt_depth = gt_depth.swapaxes(0, 1) + + ts = torch.arange(0, num_frames, device=device) + ts_clamped = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts_clamped - 1, ts_clamped, ts_clamped + 1)) # i (3B,) + + pbar = tqdm(range(0, num_iters)) + for i in pbar: + coefs = fg.get_coefs() + transfms = bases.compute_transforms(ts, coefs) + positions = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + + loss = 0.0 + track_3d_loss = masked_l1_loss( + positions, + tracks_3d.xyz, + (tracks_3d.visibles.float() * tracks_3d.confidences)[..., None], + ) + loss += track_3d_loss * 1.0 + + pred_2d, pred_depth = project_2d_tracks( + positions.swapaxes(0, 1), Ks, w2cs, return_depth=True + ) + pred_2d = pred_2d.swapaxes(0, 1) + pred_depth = pred_depth.swapaxes(0, 1) + + loss_2d = ( + masked_l1_loss( + pred_2d, + gt_2d, + (tracks_3d.invisibles.float() * tracks_3d.confidences)[..., None], + quantile=0.95, + ) + / Ks[0, 0, 0] + ) + loss += 0.5 * loss_2d + + if use_depth_range_loss: + near_depths = torch.quantile(gt_depth, 0.0, dim=0, keepdim=True) + far_depths = torch.quantile(gt_depth, 0.98, dim=0, keepdim=True) + loss_depth_in_range = 0 + if (pred_depth < near_depths).any(): + loss_depth_in_range += (near_depths - pred_depth)[ + pred_depth < near_depths + ].mean() + if (pred_depth > far_depths).any(): + loss_depth_in_range += (pred_depth - far_depths)[ + pred_depth > far_depths + ].mean() + + loss += loss_depth_in_range * w_smooth_func(i, 0.05, 0.5, 400) + + motion_coef_sparse_loss = 1 - (coefs**2).sum(dim=-1).mean() + loss += motion_coef_sparse_loss * 0.01 + + # motion basis should be smooth. + w_smooth = w_smooth_func(i, 0.01, 0.1, 400) + small_acc_loss = compute_se3_smoothness_loss( + bases.params["rots"], bases.params["transls"] + ) + loss += small_acc_loss * w_smooth + + small_acc_loss_tracks = compute_accel_loss(positions) + loss += small_acc_loss_tracks * w_smooth * 0.5 + + transfms_nbs = bases.compute_transforms(ts_neighbors, coefs) + means_nbs = torch.einsum( + "pnij,pj->pni", transfms_nbs, F.pad(fg.params["means"], (0, 1), value=1.0) + ) # (G, 3n, 3) + means_nbs = means_nbs.reshape(means_nbs.shape[0], 3, -1, 3) # [G, 3, n, 3] + z_accel_loss = compute_z_acc_loss(means_nbs, w2cs) + loss += z_accel_loss * 0.1 + + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + pbar.set_description( + f"{loss.item():.3f} " + f"{track_3d_loss.item():.3f} " + f"{motion_coef_sparse_loss.item():.3f} " + f"{small_acc_loss.item():.3f} " + f"{small_acc_loss_tracks.item():.3f} " + f"{z_accel_loss.item():.3f} " + ) + + +def random_quats(N: int) -> torch.Tensor: + u = torch.rand(N, 1) + v = torch.rand(N, 1) + w = torch.rand(N, 1) + quats = torch.cat( + [ + torch.sqrt(1.0 - u) * torch.sin(2.0 * np.pi * v), + torch.sqrt(1.0 - u) * torch.cos(2.0 * np.pi * v), + torch.sqrt(u) * torch.sin(2.0 * np.pi * w), + torch.sqrt(u) * torch.cos(2.0 * np.pi * w), + ], + -1, + ) + return quats + + +def compute_means(ts, fg: GaussianParams, bases: MotionBases): + transfms = bases.compute_transforms(ts, fg.get_coefs()) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(fg.params["means"], (0, 1), value=1.0), + ) + return means + + +def vis_init_params( + server, + fg: GaussianParams, + bases: MotionBases, + name="init_params", + num_vis: int = 100, +): + idcs = np.random.choice(fg.num_gaussians, num_vis) + labels = np.linspace(0, 1, num_vis) + ts = torch.arange(bases.num_frames, device=bases.params["rots"].device) + with torch.no_grad(): + pred_means = compute_means(ts, fg, bases) + vis_means = pred_means[idcs].detach().cpu().numpy() + vis_tracks_3d(server, vis_means, labels, name=name) + + +@torch.no_grad() +def vis_se3_init_3d(server, init_rots, init_ts, basis_centers): + """ + :param init_rots: [num_bases, num_frames, 4|6] + :param init_ts: [num_bases, num_frames, 3] + :param basis_centers: [num_bases, 3] + """ + # visualize the initial centers across time + rot_dim = init_rots.shape[-1] + assert rot_dim in [4, 6] + num_bases = init_rots.shape[0] + assert init_ts.shape[0] == num_bases + assert basis_centers.shape[0] == num_bases + labels = np.linspace(0, 1, num_bases) + if rot_dim == 4: + quats = F.normalize(init_rots, dim=-1, p=2) + rmats = roma.unitquat_to_rotmat(quats.roll(-1, dims=-1)) + else: + rmats = cont_6d_to_rmat(init_rots) + transls = init_ts + transfms = rt_to_mat4(rmats, transls) + center_tracks3d = torch.einsum( + "bnij,bj->bni", transfms, F.pad(basis_centers, (0, 1), value=1.0) + )[..., :3] + vis_tracks_3d(server, center_tracks3d.cpu().numpy(), labels, name="se3_centers") + + +@torch.no_grad() +def vis_tracks_2d_video( + path, + imgs: np.ndarray, + tracks_3d: np.ndarray, + Ks: np.ndarray, + w2cs: np.ndarray, + occs=None, + radius: int = 3, +): + num_tracks = tracks_3d.shape[0] + labels = np.linspace(0, 1, num_tracks) + cmap = get_cmap("gist_rainbow") + colors = cmap(labels)[:, :3] + tracks_2d = ( + project_2d_tracks(tracks_3d.swapaxes(0, 1), Ks, w2cs).cpu().numpy() # type: ignore + ) + frames = np.asarray( + draw_keypoints_video(imgs, tracks_2d, colors, occs, radius=radius) + ) + iio.imwrite(path, frames, fps=15) + + +def vis_tracks_3d( + server: ViserServer, + vis_tracks: np.ndarray, + vis_label: np.ndarray | None = None, + name: str = "tracks", +): + """ + :param vis_tracks (np.ndarray): (N, T, 3) + :param vis_label (np.ndarray): (N) + """ + cmap = get_cmap("gist_rainbow") + if vis_label is None: + vis_label = np.linspace(0, 1, len(vis_tracks)) + colors = cmap(np.asarray(vis_label))[:, :3] + guru.info(f"{colors.shape=}, {vis_tracks.shape=}") + N, T = vis_tracks.shape[:2] + vis_tracks = np.asarray(vis_tracks) + for i in range(N): + server.scene.add_spline_catmull_rom( + f"/{name}/{i}/spline", vis_tracks[i], color=colors[i], segments=T - 1 + ) + server.scene.add_point_cloud( + f"/{name}/{i}/start", + vis_tracks[i, [0]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="circle", + ) + server.scene.add_point_cloud( + f"/{name}/{i}/end", + vis_tracks[i, [-1]], + colors=colors[i : i + 1], + point_size=0.05, + point_shape="diamond", + ) + + +def sample_initial_bases_centers( + mode: str, cano_t: int, tracks_3d: TrackObservations, num_bases: int +): + """ + :param mode: "farthest" | "hdbscan" | "kmeans" + :param tracks_3d: [G, T, 3] + :param cano_t: canonical index + :param num_bases: number of SE3 bases + """ + assert mode in ["farthest", "hdbscan", "kmeans"] + means_canonical = tracks_3d.xyz[:, cano_t].clone() + # if mode == "farthest": + # vis_mask = tracks_3d.visibles[:, cano_t] + # sampled_centers, _ = sample_farthest_points( + # means_canonical[vis_mask][None], + # K=num_bases, + # random_start_point=True, + # ) # [1, num_bases, 3] + # dists2centers = torch.norm(means_canonical[:, None] - sampled_centers, dim=-1).T + # return sampled_centers, num_bases, dists2centers + + # linearly interpolate missing 3d points + xyz = cp.asarray(tracks_3d.xyz) + print(f"{xyz.shape=}") + visibles = cp.asarray(tracks_3d.visibles) + + num_tracks = xyz.shape[0] + xyz_interp = batched_interp_masked(xyz, visibles) + + # num_vis = 50 + # server = get_server(port=8890) + # idcs = np.random.choice(num_tracks, num_vis) + # labels = np.linspace(0, 1, num_vis) + # vis_tracks_3d(server, tracks_3d.xyz[idcs].get(), labels, name="raw_tracks") + # vis_tracks_3d(server, xyz_interp[idcs].get(), labels, name="interp_tracks") + # import ipdb; ipdb.set_trace() + + velocities = xyz_interp[:, 1:] - xyz_interp[:, :-1] + vel_dirs = ( + velocities / (cp.linalg.norm(velocities, axis=-1, keepdims=True) + 1e-5) + ).reshape((num_tracks, -1)) + + # [num_bases, num_gaussians] + if mode == "kmeans": + model = KMeans(n_clusters=num_bases) + else: + model = HDBSCAN(min_cluster_size=20, max_cluster_size=num_tracks // 4) + model.fit(vel_dirs) + labels = model.labels_ + num_bases = labels.max().item() + 1 + sampled_centers = torch.stack( + [ + means_canonical[torch.tensor(labels == i)].median(dim=0).values + for i in range(num_bases) + ] + )[None] + print("number of {} clusters: ".format(mode), num_bases) + return sampled_centers, num_bases, torch.tensor(labels) + + +def interp_masked(vals: cp.ndarray, mask: cp.ndarray, pad: int = 1) -> cp.ndarray: + """ + hacky way to interpolate batched with cupy + by concatenating the batches and pad with dummy values + :param vals: [B, M, *] + :param mask: [B, M] + """ + assert mask.ndim == 2 + assert vals.shape[:2] == mask.shape + + B, M = mask.shape + + # get the first and last valid values for each track + sh = vals.shape[2:] + vals = vals.reshape((B, M, -1)) + D = vals.shape[-1] + first_val_idcs = cp.argmax(mask, axis=-1) + last_val_idcs = M - 1 - cp.argmax(cp.flip(mask, axis=-1), axis=-1) + bidcs = cp.arange(B) + + v0 = vals[bidcs, first_val_idcs][:, None] + v1 = vals[bidcs, last_val_idcs][:, None] + m0 = mask[bidcs, first_val_idcs][:, None] + m1 = mask[bidcs, last_val_idcs][:, None] + if pad > 1: + v0 = cp.tile(v0, [1, pad, 1]) + v1 = cp.tile(v1, [1, pad, 1]) + m0 = cp.tile(m0, [1, pad]) + m1 = cp.tile(m1, [1, pad]) + + vals_pad = cp.concatenate([v0, vals, v1], axis=1) + mask_pad = cp.concatenate([m0, mask, m1], axis=1) + + M_pad = vals_pad.shape[1] + vals_flat = vals_pad.reshape((B * M_pad, -1)) + mask_flat = mask_pad.reshape((B * M_pad,)) + idcs = cp.where(mask_flat)[0] + + cx = cp.arange(B * M_pad) + out = cp.zeros((B * M_pad, D), dtype=vals_flat.dtype) + for d in range(D): + out[:, d] = cp.interp(cx, idcs, vals_flat[idcs, d]) + + out = out.reshape((B, M_pad, *sh))[:, pad:-pad] + return out + + +def batched_interp_masked( + vals: cp.ndarray, mask: cp.ndarray, batch_num: int = 4096, batch_time: int = 64 +): + assert mask.ndim == 2 + B, M = mask.shape + out = cp.zeros_like(vals) + for b in tqdm(range(0, B, batch_num), leave=False): + for m in tqdm(range(0, M, batch_time), leave=False): + x = interp_masked( + vals[b : b + batch_num, m : m + batch_time], + mask[b : b + batch_num, m : m + batch_time], + ) # (batch_num, batch_time, *) + out[b : b + batch_num, m : m + batch_time] = x + return out diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/loss_utils.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/loss_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..244bb4ff7b0896b87721339275ad1cdd42d7fd1a --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/loss_utils.py @@ -0,0 +1,158 @@ +import numpy as np +import torch +import torch.nn.functional as F +from sklearn.neighbors import NearestNeighbors + + +def masked_mse_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_mse_loss(pred, gt, quantile) + else: + sum_loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_l1_loss(pred, gt, mask=None, normalize=True, quantile: float = 1.0): + if mask is None: + return trimmed_l1_loss(pred, gt, quantile) + else: + sum_loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1, keepdim=True) + quantile_mask = ( + (sum_loss < torch.quantile(sum_loss, quantile)).squeeze(-1) + if quantile < 1 + else torch.ones_like(sum_loss, dtype=torch.bool).squeeze(-1) + ) + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum((sum_loss * mask)[quantile_mask]) / ( + ndim * torch.sum(mask[quantile_mask]) + 1e-8 + ) + else: + return torch.mean((sum_loss * mask)[quantile_mask]) + + +def masked_huber_loss(pred, gt, delta, mask=None, normalize=True): + if mask is None: + return F.huber_loss(pred, gt, delta=delta) + else: + sum_loss = F.huber_loss(pred, gt, delta=delta, reduction="none") + ndim = sum_loss.shape[-1] + if normalize: + return torch.sum(sum_loss * mask) / (ndim * torch.sum(mask) + 1e-8) + else: + return torch.mean(sum_loss * mask) + + +def trimmed_mse_loss(pred, gt, quantile=0.9): + loss = F.mse_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def trimmed_l1_loss(pred, gt, quantile=0.9): + loss = F.l1_loss(pred, gt, reduction="none").mean(dim=-1) + loss_at_quantile = torch.quantile(loss, quantile) + trimmed_loss = loss[loss < loss_at_quantile].mean() + return trimmed_loss + + +def compute_gradient_loss(pred, gt, mask, quantile=0.98): + """ + Compute gradient loss + pred: (batch_size, H, W, D) or (batch_size, H, W) + gt: (batch_size, H, W, D) or (batch_size, H, W) + mask: (batch_size, H, W), bool or float + """ + # NOTE: messy need to be cleaned up + mask_x = mask[:, :, 1:] * mask[:, :, :-1] + mask_y = mask[:, 1:, :] * mask[:, :-1, :] + pred_grad_x = pred[:, :, 1:] - pred[:, :, :-1] + pred_grad_y = pred[:, 1:, :] - pred[:, :-1, :] + gt_grad_x = gt[:, :, 1:] - gt[:, :, :-1] + gt_grad_y = gt[:, 1:, :] - gt[:, :-1, :] + loss = masked_l1_loss( + pred_grad_x[mask_x][..., None], gt_grad_x[mask_x][..., None], quantile=quantile + ) + masked_l1_loss( + pred_grad_y[mask_y][..., None], gt_grad_y[mask_y][..., None], quantile=quantile + ) + return loss + + +def knn(x: torch.Tensor, k: int) -> tuple[np.ndarray, np.ndarray]: + x = x.cpu().numpy() + knn_model = NearestNeighbors( + n_neighbors=k + 1, algorithm="auto", metric="euclidean" + ).fit(x) + distances, indices = knn_model.kneighbors(x) + return distances[:, 1:].astype(np.float32), indices[:, 1:].astype(np.float32) + + +def get_weights_for_procrustes(clusters, visibilities=None): + clusters_median = clusters.median(dim=-2, keepdim=True)[0] + dists2clusters_center = torch.norm(clusters - clusters_median, dim=-1) + dists2clusters_center /= dists2clusters_center.median(dim=-1, keepdim=True)[0] + weights = torch.exp(-dists2clusters_center) + weights /= weights.mean(dim=-1, keepdim=True) + 1e-6 + if visibilities is not None: + weights *= visibilities.float() + 1e-6 + invalid = dists2clusters_center > np.quantile( + dists2clusters_center.cpu().numpy(), 0.9 + ) + invalid |= torch.isnan(weights) + weights[invalid] = 0 + return weights + + +def compute_z_acc_loss(means_ts_nb: torch.Tensor, w2cs: torch.Tensor): + """ + :param means_ts (G, 3, B, 3) + :param w2cs (B, 4, 4) + return (float) + """ + camera_center_t = torch.linalg.inv(w2cs)[:, :3, 3] # (B, 3) + ray_dir = F.normalize( + means_ts_nb[:, 1] - camera_center_t, p=2.0, dim=-1 + ) # [G, B, 3] + # acc = 2 * means[:, 1] - means[:, 0] - means[:, 2] # [G, B, 3] + # acc_loss = (acc * ray_dir).sum(dim=-1).abs().mean() + acc_loss = ( + ((means_ts_nb[:, 1] - means_ts_nb[:, 0]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + ( + ((means_ts_nb[:, 2] - means_ts_nb[:, 1]) * ray_dir).sum(dim=-1) ** 2 + ).mean() + return acc_loss + + +def compute_se3_smoothness_loss( + rots: torch.Tensor, + transls: torch.Tensor, + weight_rot: float = 1.0, + weight_transl: float = 2.0, +): + """ + central differences + :param motion_transls (K, T, 3) + :param motion_rots (K, T, 6) + """ + r_accel_loss = compute_accel_loss(rots) + t_accel_loss = compute_accel_loss(transls) + return r_accel_loss * weight_rot + t_accel_loss * weight_transl + + +def compute_accel_loss(transls): + accel = 2 * transls[:, 1:-1] - transls[:, :-2] - transls[:, 2:] + loss = accel.norm(dim=-1).mean() + return loss + diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/metrics.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..26efadea6ce950d1c597dee0d67b46714846ead5 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/metrics.py @@ -0,0 +1,313 @@ +from typing import Literal + +import numpy as np +import torch +import torch.nn.functional as F +from torchmetrics.functional.image.lpips import _NoTrainLpips +from torchmetrics.image import PeakSignalNoiseRatio, StructuralSimilarityIndexMeasure +from torchmetrics.metric import Metric +from torchmetrics.utilities import dim_zero_cat +from torchmetrics.utilities.imports import _TORCHVISION_AVAILABLE + + +def compute_psnr( + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, +) -> float: + """ + Args: + preds (torch.Tensor): (..., 3) predicted images in [0, 1]. + targets (torch.Tensor): (..., 3) target images in [0, 1]. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + + Returns: + psnr (float): Peak signal-to-noise ratio. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + return ( + -10.0 + * torch.log( + F.mse_loss( + preds * masks[..., None], + targets * masks[..., None], + reduction="sum", + ) + / masks.sum().clamp(min=1.0) + / 3.0 + ) + / np.log(10.0) + ).item() + + +def compute_pose_errors( + preds: torch.Tensor, targets: torch.Tensor +) -> tuple[float, float, float]: + """ + Args: + preds: (N, 4, 4) predicted camera poses. + targets: (N, 4, 4) target camera poses. + + Returns: + ate (float): Absolute trajectory error. + rpe_t (float): Relative pose error in translation. + rpe_r (float): Relative pose error in rotation (degree). + """ + # Compute ATE. + ate = torch.linalg.norm(preds[:, :3, -1] - targets[:, :3, -1], dim=-1).mean().item() + # Compute RPE_t and RPE_r. + # NOTE(hangg): It's important to use numpy here for the accuracy of RPE_r. + # torch has numerical issues for acos when the value is close to 1.0, i.e. + # RPE_r is supposed to be very small, and will result in artificially large + # error. + preds = preds.detach().cpu().numpy() + targets = targets.detach().cpu().numpy() + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + pred_rels = np.linalg.inv(preds[:-1]) @ preds[1:] + target_rels = np.linalg.inv(targets[:-1]) @ targets[1:] + error_rels = np.linalg.inv(target_rels) @ pred_rels + traces = error_rels[:, :3, :3].trace(axis1=-2, axis2=-1) + rpe_t = np.linalg.norm(error_rels[:, :3, -1], axis=-1).mean().item() + rpe_r = ( + np.arccos(np.clip((traces - 1.0) / 2.0, -1.0, 1.0)).mean().item() + / np.pi + * 180.0 + ) + return ate, rpe_t, rpe_r + + +class mPSNR(PeakSignalNoiseRatio): + sum_squared_error: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__(self, **kwargs) -> None: + super().__init__( + data_range=1.0, + base=10.0, + dim=None, + reduction="elementwise_mean", + **kwargs, + ) + self.add_state("sum_squared_error", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (..., 3) float32 predicted images. + targets (torch.Tensor): (..., 3) float32 target images. + masks (torch.Tensor | None): (...,) optional binary masks where the + 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + self.sum_squared_error.append( + torch.sum(torch.pow((preds - targets) * masks[..., None], 2)) + ) + self.total.append(masks.sum().to(torch.int64) * 3) + + def compute(self) -> torch.Tensor: + """Compute peak signal-to-noise ratio over state.""" + sum_squared_error = dim_zero_cat(self.sum_squared_error) + total = dim_zero_cat(self.total) + return -10.0 * torch.log(sum_squared_error / total).mean() / np.log(10.0) + + +class mSSIM(StructuralSimilarityIndexMeasure): + similarity: list + + def __init__(self, **kwargs) -> None: + super().__init__( + reduction=None, + data_range=1.0, + return_full_image=False, + **kwargs, + ) + assert isinstance(self.sigma, float) + + def __len__(self) -> int: + return sum([s.shape[0] for s in self.similarity]) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update state with predictions and targets. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional binary masks where + the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + + # Construct a 1D Gaussian blur filter. + assert isinstance(self.kernel_size, int) + hw = self.kernel_size // 2 + shift = (2 * hw - self.kernel_size + 1) / 2 + assert isinstance(self.sigma, float) + f_i = ( + (torch.arange(self.kernel_size, device=preds.device) - hw + shift) + / self.sigma + ) ** 2 + filt = torch.exp(-0.5 * f_i) + filt /= torch.sum(filt) + + # Blur in x and y (faster than the 2D convolution). + def convolve2d(z, m, f): + # z: (B, H, W, C), m: (B, H, W), f: (Hf, Wf). + z = z.permute(0, 3, 1, 2) + m = m[:, None] + f = f[None, None].expand(z.shape[1], -1, -1, -1) + z_ = torch.nn.functional.conv2d( + z * m, f, padding="valid", groups=z.shape[1] + ) + m_ = torch.nn.functional.conv2d(m, torch.ones_like(f[:1]), padding="valid") + return torch.where( + m_ != 0, z_ * torch.ones_like(f).sum() / (m_ * z.shape[1]), 0 + ).permute(0, 2, 3, 1), (m_ != 0)[:, 0].to(z.dtype) + + filt_fn1 = lambda z, m: convolve2d(z, m, filt[:, None]) + filt_fn2 = lambda z, m: convolve2d(z, m, filt[None, :]) + filt_fn = lambda z, m: filt_fn1(*filt_fn2(z, m)) + + mu0 = filt_fn(preds, masks)[0] + mu1 = filt_fn(targets, masks)[0] + mu00 = mu0 * mu0 + mu11 = mu1 * mu1 + mu01 = mu0 * mu1 + sigma00 = filt_fn(preds**2, masks)[0] - mu00 + sigma11 = filt_fn(targets**2, masks)[0] - mu11 + sigma01 = filt_fn(preds * targets, masks)[0] - mu01 + + # Clip the variances and covariances to valid values. + # Variance must be non-negative: + sigma00 = sigma00.clamp(min=0.0) + sigma11 = sigma11.clamp(min=0.0) + sigma01 = torch.sign(sigma01) * torch.minimum( + torch.sqrt(sigma00 * sigma11), torch.abs(sigma01) + ) + + assert isinstance(self.data_range, float) + c1 = (self.k1 * self.data_range) ** 2 + c2 = (self.k2 * self.data_range) ** 2 + numer = (2 * mu01 + c1) * (2 * sigma01 + c2) + denom = (mu00 + mu11 + c1) * (sigma00 + sigma11 + c2) + ssim_map = numer / denom + + self.similarity.append(ssim_map.mean(dim=(1, 2, 3))) + + def compute(self) -> torch.Tensor: + """Compute final SSIM metric.""" + return torch.cat(self.similarity).mean() + + +class mLPIPS(Metric): + sum_scores: list[torch.Tensor] + total: list[torch.Tensor] + + def __init__( + self, + net_type: Literal["vgg", "alex", "squeeze"] = "alex", + **kwargs, + ): + super().__init__(**kwargs) + + if not _TORCHVISION_AVAILABLE: + raise ModuleNotFoundError( + "LPIPS metric requires that torchvision is installed." + " Either install as `pip install torchmetrics[image]` or `pip install torchvision`." + ) + + valid_net_type = ("vgg", "alex", "squeeze") + if net_type not in valid_net_type: + raise ValueError( + f"Argument `net_type` must be one of {valid_net_type}, but got {net_type}." + ) + self.net = _NoTrainLpips(net=net_type, spatial=True) + + self.add_state("sum_scores", [], dist_reduce_fx="cat") + self.add_state("total", [], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update( + self, + preds: torch.Tensor, + targets: torch.Tensor, + masks: torch.Tensor | None = None, + ): + """Update internal states with lpips scores. + + Args: + preds (torch.Tensor): (B, H, W, 3) float32 predicted images. + targets (torch.Tensor): (B, H, W, 3) float32 target images. + masks (torch.Tensor | None): (B, H, W) optional float32 binary + masks where the 1-regions will be taken into account. + """ + if masks is None: + masks = torch.ones_like(preds[..., 0]) + scores = self.net( + (preds * masks[..., None]).permute(0, 3, 1, 2), + (targets * masks[..., None]).permute(0, 3, 1, 2), + normalize=True, + ) + self.sum_scores.append((scores * masks[:, None]).sum()) + self.total.append(masks.sum().to(torch.int64)) + + def compute(self) -> torch.Tensor: + """Compute final perceptual similarity metric.""" + return ( + torch.tensor(self.sum_scores, device=self.device) + / torch.tensor(self.total, device=self.device) + ).mean() + + +class PCK(Metric): + correct: list[torch.Tensor] + total: list[int] + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.add_state("correct", default=[], dist_reduce_fx="cat") + self.add_state("total", default=[], dist_reduce_fx="cat") + + def __len__(self) -> int: + return len(self.total) + + def update(self, preds: torch.Tensor, targets: torch.Tensor, threshold: float): + """Update internal states with PCK scores. + + Args: + preds (torch.Tensor): (N, 2) predicted 2D keypoints. + targets (torch.Tensor): (N, 2) targets 2D keypoints. + threshold (float): PCK threshold. + """ + + self.correct.append( + (torch.linalg.norm(preds - targets, dim=-1) < threshold).sum() + ) + self.total.append(preds.shape[0]) + + def compute(self) -> torch.Tensor: + """Compute PCK over state.""" + return ( + torch.tensor(self.correct, device=self.device) + / torch.clamp(torch.tensor(self.total, device=self.device), min=1e-8) + ).mean() diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/params.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/params.py new file mode 100644 index 0000000000000000000000000000000000000000..db6b26fd64d68a142900267ec83e4c5f1ed8604e --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/params.py @@ -0,0 +1,184 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from flow3d.transforms import cont_6d_to_rmat + + +class GaussianParams(nn.Module): + def __init__( + self, + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, + scene_center: torch.Tensor | None = None, + scene_scale: torch.Tensor | float = 1.0, + ): + super().__init__() + if not check_gaussian_sizes( + means, quats, scales, colors, opacities, motion_coefs + ): + import ipdb + + ipdb.set_trace() + params_dict = { + "means": nn.Parameter(means), + "quats": nn.Parameter(quats), + "scales": nn.Parameter(scales), + "colors": nn.Parameter(colors), + "opacities": nn.Parameter(opacities), + } + if motion_coefs is not None: + params_dict["motion_coefs"] = nn.Parameter(motion_coefs) + self.params = nn.ParameterDict(params_dict) + self.quat_activation = lambda x: F.normalize(x, dim=-1, p=2) + self.color_activation = torch.sigmoid + self.scale_activation = torch.exp + self.opacity_activation = torch.sigmoid + self.motion_coef_activation = lambda x: F.softmax(x, dim=-1) + + if scene_center is None: + scene_center = torch.zeros(3, device=means.device) + self.register_buffer("scene_center", scene_center) + self.register_buffer("scene_scale", torch.as_tensor(scene_scale)) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + req_keys = ["means", "quats", "scales", "colors", "opacities"] + assert all(f"{prefix}{k}" in state_dict for k in req_keys) + args = { + "motion_coefs": None, + "scene_center": torch.zeros(3), + "scene_scale": torch.tensor(1.0), + } + for k in req_keys + list(args.keys()): + if f"{prefix}{k}" in state_dict: + args[k] = state_dict[f"{prefix}{k}"] + return GaussianParams(**args) + + @property + def num_gaussians(self) -> int: + return self.params["means"].shape[0] + + def get_colors(self) -> torch.Tensor: + return self.color_activation(self.params["colors"]) + + def get_scales(self) -> torch.Tensor: + return self.scale_activation(self.params["scales"]) + + def get_opacities(self) -> torch.Tensor: + return self.opacity_activation(self.params["opacities"]) + + def get_quats(self) -> torch.Tensor: + return self.quat_activation(self.params["quats"]) + + def get_coefs(self) -> torch.Tensor: + assert "motion_coefs" in self.params + return self.motion_coef_activation(self.params["motion_coefs"]) + + def densify_params(self, should_split, should_dup): + """ + densify gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_dup = x[should_dup] + x_split = x[should_split].repeat([2] + [1] * (x.ndim - 1)) + if name == "scales": + x_split -= math.log(1.6) + x_new = nn.Parameter(torch.cat([x[~should_split], x_dup, x_split], dim=0)) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def cull_params(self, should_cull): + """ + cull gaussians + """ + updated_params = {} + for name, x in self.params.items(): + x_new = nn.Parameter(x[~should_cull]) + updated_params[name] = x_new + self.params[name] = x_new + return updated_params + + def reset_opacities(self, new_val): + """ + reset all opacities to new_val + """ + self.params["opacities"].data.fill_(new_val) + updated_params = {"opacities": self.params["opacities"]} + return updated_params + + +class MotionBases(nn.Module): + def __init__(self, rots, transls): + super().__init__() + self.num_frames = rots.shape[1] + self.num_bases = rots.shape[0] + assert check_bases_sizes(rots, transls) + self.params = nn.ParameterDict( + { + "rots": nn.Parameter(rots), + "transls": nn.Parameter(transls), + } + ) + + @staticmethod + def init_from_state_dict(state_dict, prefix="params."): + param_keys = ["rots", "transls"] + assert all(f"{prefix}{k}" in state_dict for k in param_keys) + args = {k: state_dict[f"{prefix}{k}"] for k in param_keys} + return MotionBases(**args) + + def compute_transforms(self, ts: torch.Tensor, coefs: torch.Tensor) -> torch.Tensor: + """ + :param ts (B) + :param coefs (G, K) + returns transforms (G, B, 3, 4) + """ + transls = self.params["transls"][:, ts] # (K, B, 3) + rots = self.params["rots"][:, ts] # (K, B, 6) + transls = torch.einsum("pk,kni->pni", coefs, transls) + rots = torch.einsum("pk,kni->pni", coefs, rots) # (G, B, 6) + rotmats = cont_6d_to_rmat(rots) # (K, B, 3, 3) + return torch.cat([rotmats, transls[..., None]], dim=-1) + + +def check_gaussian_sizes( + means: torch.Tensor, + quats: torch.Tensor, + scales: torch.Tensor, + colors: torch.Tensor, + opacities: torch.Tensor, + motion_coefs: torch.Tensor | None = None, +) -> bool: + dims = means.shape[:-1] + leading_dims_match = ( + quats.shape[:-1] == dims + and scales.shape[:-1] == dims + and colors.shape[:-1] == dims + and opacities.shape == dims + ) + if motion_coefs is not None and motion_coefs.numel() > 0: + leading_dims_match &= motion_coefs.shape[:-1] == dims + dims_correct = ( + means.shape[-1] == 3 + and (quats.shape[-1] == 4) + and (scales.shape[-1] == 3) + and (colors.shape[-1] == 3) + ) + return leading_dims_match and dims_correct + + +def check_bases_sizes(motion_rots: torch.Tensor, motion_transls: torch.Tensor) -> bool: + return ( + motion_rots.shape[-1] == 6 + and motion_transls.shape[-1] == 3 + and motion_rots.shape[:-2] == motion_transls.shape[:-2] + ) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/renderer.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..974a1a630b18a9392a545c44ec4c981277354f1b --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/renderer.py @@ -0,0 +1,89 @@ +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState + +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import draw_tracks_2d_th, get_server +from flow3d.vis.viewer import DynamicViewer + + +class Renderer: + def __init__( + self, + model: SceneModel, + device: torch.device, + # Logging. + work_dir: str, + port: int | None = None, + ): + self.device = device + + self.model = model + self.num_frames = model.num_frames + + self.work_dir = work_dir + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="rendering" + ) + + self.tracks_3d = self.model.compute_poses_fg( + # torch.arange(max(0, t - 20), max(1, t), device=self.device), + torch.arange(self.num_frames, device=self.device), + inds=torch.arange(10, device=self.device), + )[0] + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> "Renderer": + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + renderer = Renderer(model, device, *args, **kwargs) + renderer.global_step = ckpt.get("global_step", 0) + renderer.epoch = ckpt.get("epoch", 0) + return renderer + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + if self.viewer is None: + return np.full((img_wh[1], img_wh[0], 3), 255, dtype=np.uint8) + + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + if not self.viewer._render_track_checkbox.value: + img = (img.cpu().numpy() * 255.0).astype(np.uint8) + else: + assert t is not None + tracks_3d = self.tracks_3d[:, max(0, t - 20) : max(1, t)] + tracks_2d = torch.einsum( + "ij,jk,nbk->nbi", K, w2c[:3], F.pad(tracks_3d, (0, 1), value=1.0) + ) + tracks_2d = tracks_2d[..., :2] / tracks_2d[..., 2:] + img = draw_tracks_2d_th(img, tracks_2d) + return img diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/scene_model.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/scene_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7bd685b691153b62234a8084dffe33b11b83b327 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/scene_model.py @@ -0,0 +1,292 @@ +import roma +import torch +import torch.nn as nn +import torch.nn.functional as F +from gsplat.rendering import rasterization +from torch import Tensor + +from flow3d.params import GaussianParams, MotionBases + + +class SceneModel(nn.Module): + def __init__( + self, + Ks: Tensor, + w2cs: Tensor, + fg_params: GaussianParams, + motion_bases: MotionBases, + bg_params: GaussianParams | None = None, + ): + super().__init__() + self.num_frames = motion_bases.num_frames + self.fg = fg_params + self.motion_bases = motion_bases + self.bg = bg_params + scene_scale = 1.0 if bg_params is None else bg_params.scene_scale + self.register_buffer("bg_scene_scale", torch.as_tensor(scene_scale)) + self.register_buffer("Ks", Ks) + self.register_buffer("w2cs", w2cs) + + self._current_xys = None + self._current_radii = None + self._current_img_wh = None + + @property + def num_gaussians(self) -> int: + return self.num_bg_gaussians + self.num_fg_gaussians + + @property + def num_bg_gaussians(self) -> int: + return self.bg.num_gaussians if self.bg is not None else 0 + + @property + def num_fg_gaussians(self) -> int: + return self.fg.num_gaussians + + @property + def num_motion_bases(self) -> int: + return self.motion_bases.num_bases + + @property + def has_bg(self) -> bool: + return self.bg is not None + + def compute_poses_bg(self) -> tuple[torch.Tensor, torch.Tensor]: + """ + Returns: + means: (G, B, 3) + quats: (G, B, 4) + """ + assert self.bg is not None + return self.bg.params["means"], self.bg.get_quats() + + def compute_transforms( + self, ts: torch.Tensor, inds: torch.Tensor | None = None + ) -> torch.Tensor: + coefs = self.fg.get_coefs() # (G, K) + if inds is not None: + coefs = coefs[inds] + transfms = self.motion_bases.compute_transforms(ts, coefs) # (G, B, 3, 4) + return transfms + + def compute_poses_fg( + self, ts: torch.Tensor | None, inds: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + :returns means: (G, B, 3), quats: (G, B, 4) + """ + means = self.fg.params["means"] # (G, 3) + quats = self.fg.get_quats() # (G, 4) + if inds is not None: + means = means[inds] + quats = quats[inds] + if ts is not None: + transfms = self.compute_transforms(ts, inds) # (G, B, 3, 4) + means = torch.einsum( + "pnij,pj->pni", + transfms, + F.pad(means, (0, 1), value=1.0), + ) + quats = roma.quat_xyzw_to_wxyz( + ( + roma.quat_product( + roma.rotmat_to_unitquat(transfms[..., :3, :3]), + roma.quat_wxyz_to_xyzw(quats[:, None]), + ) + ) + ) + quats = F.normalize(quats, p=2, dim=-1) + else: + means = means[:, None] + quats = quats[:, None] + return means, quats + + def compute_poses_all( + self, ts: torch.Tensor | None + ) -> tuple[torch.Tensor, torch.Tensor]: + means, quats = self.compute_poses_fg(ts) + if self.has_bg: + bg_means, bg_quats = self.compute_poses_bg() + means = torch.cat( + [means, bg_means[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + quats = torch.cat( + [quats, bg_quats[:, None].expand(-1, means.shape[1], -1)], dim=0 + ).contiguous() + return means, quats + + def get_colors_all(self) -> torch.Tensor: + colors = self.fg.get_colors() + if self.bg is not None: + colors = torch.cat([colors, self.bg.get_colors()], dim=0).contiguous() + return colors + + def get_scales_all(self) -> torch.Tensor: + scales = self.fg.get_scales() + if self.bg is not None: + scales = torch.cat([scales, self.bg.get_scales()], dim=0).contiguous() + return scales + + def get_opacities_all(self) -> torch.Tensor: + """ + :returns colors: (G, 3), scales: (G, 3), opacities: (G, 1) + """ + opacities = self.fg.get_opacities() + if self.bg is not None: + opacities = torch.cat( + [opacities, self.bg.get_opacities()], dim=0 + ).contiguous() + return opacities + + @staticmethod + def init_from_state_dict(state_dict, prefix=""): + fg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}fg.params." + ) + bg = None + if any("bg." in k for k in state_dict): + bg = GaussianParams.init_from_state_dict( + state_dict, prefix=f"{prefix}bg.params." + ) + motion_bases = MotionBases.init_from_state_dict( + state_dict, prefix=f"{prefix}motion_bases.params." + ) + Ks = state_dict[f"{prefix}Ks"] + w2cs = state_dict[f"{prefix}w2cs"] + return SceneModel(Ks, w2cs, fg, motion_bases, bg) + + def render( + self, + # A single time instance for view rendering. + t: int | None, + w2cs: torch.Tensor, # (C, 4, 4) + Ks: torch.Tensor, # (C, 3, 3) + img_wh: tuple[int, int], + # Multiple time instances for track rendering: (B,). + target_ts: torch.Tensor | None = None, # (B) + target_w2cs: torch.Tensor | None = None, # (B, 4, 4) + bg_color: torch.Tensor | float = 1.0, + colors_override: torch.Tensor | None = None, + means: torch.Tensor | None = None, + quats: torch.Tensor | None = None, + target_means: torch.Tensor | None = None, + return_color: bool = True, + return_depth: bool = False, + return_mask: bool = False, + fg_only: bool = False, + filter_mask: torch.Tensor | None = None, + ) -> dict: + device = w2cs.device + C = w2cs.shape[0] + + W, H = img_wh + pose_fnc = self.compute_poses_fg if fg_only else self.compute_poses_all + N = self.num_fg_gaussians if fg_only else self.num_gaussians + + if means is None or quats is None: + means, quats = pose_fnc( + torch.tensor([t], device=device) if t is not None else None + ) + means = means[:, 0] + quats = quats[:, 0] + + if colors_override is None: + if return_color: + colors_override = ( + self.fg.get_colors() if fg_only else self.get_colors_all() + ) + else: + colors_override = torch.zeros(N, 0, device=device) + + D = colors_override.shape[-1] + + scales = self.fg.get_scales() if fg_only else self.get_scales_all() + opacities = self.fg.get_opacities() if fg_only else self.get_opacities_all() + + if isinstance(bg_color, float): + bg_color = torch.full((C, D), bg_color, device=device) + assert isinstance(bg_color, torch.Tensor) + + mode = "RGB" + ds_expected = {"img": D} + + if return_mask: + if self.has_bg and not fg_only: + mask_values = torch.zeros((self.num_gaussians, 1), device=device) + mask_values[: self.num_fg_gaussians] = 1.0 + else: + mask_values = torch.ones((self.num_fg_gaussians, 1), device=device) + colors_override = torch.cat([colors_override, mask_values], dim=-1) + bg_color = torch.cat([bg_color, torch.zeros(C, 1, device=device)], dim=-1) + ds_expected["mask"] = 1 + + B = 0 + if target_ts is not None: + B = target_ts.shape[0] + if target_means is None: + target_means, _ = pose_fnc(target_ts) # [G, B, 3] + if target_w2cs is not None: + target_means = torch.einsum( + "bij,pbj->pbi", + target_w2cs[:, :3], + F.pad(target_means, (0, 1), value=1.0), + ) + track_3d_vals = target_means.flatten(-2) # (G, B * 3) + d_track = track_3d_vals.shape[-1] + colors_override = torch.cat([colors_override, track_3d_vals], dim=-1) + bg_color = torch.cat( + [bg_color, torch.zeros(C, track_3d_vals.shape[-1], device=device)], + dim=-1, + ) + ds_expected["tracks_3d"] = d_track + + assert colors_override.shape[-1] == sum(ds_expected.values()) + assert bg_color.shape[-1] == sum(ds_expected.values()) + + if return_depth: + mode = "RGB+ED" + ds_expected["depth"] = 1 + + if filter_mask is not None: + assert filter_mask.shape == (N,) + means = means[filter_mask] + quats = quats[filter_mask] + scales = scales[filter_mask] + opacities = opacities[filter_mask] + colors_override = colors_override[filter_mask] + + render_colors, alphas, info = rasterization( + means=means, + quats=quats, + scales=scales, + opacities=opacities, + colors=colors_override, + backgrounds=bg_color, + viewmats=w2cs, # [C, 4, 4] + Ks=Ks, # [C, 3, 3] + width=W, + height=H, + packed=False, + render_mode=mode, + ) + + # Populate the current data for adaptive gaussian control. + if self.training and info["means2d"].requires_grad: + self._current_xys = info["means2d"] + self._current_radii = info["radii"] + self._current_img_wh = img_wh + # We want to be able to access to xys' gradients later in a + # torch.no_grad context. + self._current_xys.retain_grad() + + assert render_colors.shape[-1] == sum(ds_expected.values()) + outputs = torch.split(render_colors, list(ds_expected.values()), dim=-1) + out_dict = {} + for i, (name, dim) in enumerate(ds_expected.items()): + x = outputs[i] + assert x.shape[-1] == dim, f"{x.shape[-1]=} != {dim=}" + if name == "tracks_3d": + x = x.reshape(C, H, W, B, 3) + out_dict[name] = x + out_dict["acc"] = alphas + return out_dict diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/tensor_dataclass.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/tensor_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..1a55edaad8d96f0e535c923f47fdb8d310073c52 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/tensor_dataclass.py @@ -0,0 +1,96 @@ +from dataclasses import dataclass +from typing import Callable, TypeVar + +import torch +from typing_extensions import Self + +TensorDataclassT = TypeVar("T", bound="TensorDataclass") + + +class TensorDataclass: + """A lighter version of nerfstudio's TensorDataclass: + https://github.com/nerfstudio-project/nerfstudio/blob/main/nerfstudio/utils/tensor_dataclass.py + """ + + def __getitem__(self, key) -> Self: + return self.map(lambda x: x[key]) + + def to(self, device: torch.device | str) -> Self: + """Move the tensors in the dataclass to the given device. + + Args: + device: The device to move to. + + Returns: + A new dataclass. + """ + return self.map(lambda x: x.to(device)) + + def map(self, fn: Callable[[torch.Tensor], torch.Tensor]) -> Self: + """Apply a function to all tensors in the dataclass. + + Also recurses into lists, tuples, and dictionaries. + + Args: + fn: The function to apply to each tensor. + + Returns: + A new dataclass. + """ + + MapT = TypeVar("MapT") + + def _map_impl( + fn: Callable[[torch.Tensor], torch.Tensor], + val: MapT, + ) -> MapT: + if isinstance(val, torch.Tensor): + return fn(val) + elif isinstance(val, TensorDataclass): + return type(val)(**_map_impl(fn, vars(val))) + elif isinstance(val, (list, tuple)): + return type(val)(_map_impl(fn, v) for v in val) + elif isinstance(val, dict): + assert type(val) is dict # No subclass support. + return {k: _map_impl(fn, v) for k, v in val.items()} # type: ignore + else: + return val + + return _map_impl(fn, self) + + +@dataclass +class TrackObservations(TensorDataclass): + xyz: torch.Tensor + visibles: torch.Tensor + invisibles: torch.Tensor + confidences: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape[:-1] + return ( + self.visibles.shape == dims + and self.invisibles.shape == dims + and self.confidences.shape == dims + and self.colors.shape[:-1] == dims[:-1] + and self.xyz.shape[-1] == 3 + and self.colors.shape[-1] == 3 + ) + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) + + +@dataclass +class StaticObservations(TensorDataclass): + xyz: torch.Tensor + normals: torch.Tensor + colors: torch.Tensor + + def check_sizes(self) -> bool: + dims = self.xyz.shape + return self.normals.shape == dims and self.colors.shape == dims + + def filter_valid(self, valid_mask: torch.Tensor) -> Self: + return self.map(lambda x: x[valid_mask]) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/trainer.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa0677f91de70a5a3678cba970600e4ae5b1def --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/trainer.py @@ -0,0 +1,805 @@ +import functools +import time +from dataclasses import asdict +from typing import cast + +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState +from pytorch_msssim import SSIM +from torch.utils.tensorboard import SummaryWriter # type: ignore + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.loss_utils import ( + compute_gradient_loss, + compute_se3_smoothness_loss, + compute_z_acc_loss, + masked_l1_loss, +) +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import get_server +from flow3d.vis.viewer import DynamicViewer + + +class Trainer: + def __init__( + self, + model: SceneModel, + device: torch.device, + lr_cfg: SceneLRConfig, + losses_cfg: LossesConfig, + optim_cfg: OptimizerConfig, + # Logging. + work_dir: str, + port: int | None = None, + log_every: int = 10, + checkpoint_every: int = 200, + validate_every: int = 500, + validate_video_every: int = 1000, + validate_viewer_assets_every: int = 100, + ): + + self.device = device + self.log_every = log_every + self.checkpoint_every = checkpoint_every + self.validate_every = validate_every + self.validate_video_every = validate_video_every + self.validate_viewer_assets_every = validate_viewer_assets_every + + self.model = model + self.num_frames = model.num_frames + + self.lr_cfg = lr_cfg + self.losses_cfg = losses_cfg + self.optim_cfg = optim_cfg + + self.reset_opacity_every = ( + self.optim_cfg.reset_opacity_every_n_controls * self.optim_cfg.control_every + ) + self.optimizers, self.scheduler = self.configure_optimizers() + + # running stats for adaptive density control + self.running_stats = { + "xys_grad_norm_acc": torch.zeros(self.model.num_gaussians, device=device), + "vis_count": torch.zeros( + self.model.num_gaussians, device=device, dtype=torch.int64 + ), + "max_radii": torch.zeros(self.model.num_gaussians, device=device), + } + + self.work_dir = work_dir + self.writer = SummaryWriter(log_dir=work_dir) + self.global_step = 0 + self.epoch = 0 + + self.viewer = None + if port is not None: + server = get_server(port=port) + self.viewer = DynamicViewer( + server, self.render_fn, model.num_frames, work_dir, mode="training" + ) + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS() + self.pck_metric = PCK() + self.bg_psnr_metric = mPSNR() + self.fg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.fg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS() + self.fg_lpips_metric = mLPIPS() + + def set_epoch(self, epoch: int): + self.epoch = epoch + + def save_checkpoint(self, path: str): + model_dict = self.model.state_dict() + optimizer_dict = {k: v.state_dict() for k, v in self.optimizers.items()} + scheduler_dict = {k: v.state_dict() for k, v in self.scheduler.items()} + ckpt = { + "model": model_dict, + "optimizers": optimizer_dict, + "schedulers": scheduler_dict, + "global_step": self.global_step, + "epoch": self.epoch, + } + torch.save(ckpt, path) + guru.info(f"Saved checkpoint at {self.global_step=} to {path}") + + @staticmethod + def init_from_checkpoint( + path: str, device: torch.device, *args, **kwargs + ) -> tuple["Trainer", int]: + guru.info(f"Loading checkpoint from {path}") + ckpt = torch.load(path) + state_dict = ckpt["model"] + model = SceneModel.init_from_state_dict(state_dict) + model = model.to(device) + trainer = Trainer(model, device, *args, **kwargs) + if "optimizers" in ckpt: + trainer.load_checkpoint_optimizers(ckpt["optimizers"]) + if "schedulers" in ckpt: + trainer.load_checkpoint_schedulers(ckpt["schedulers"]) + trainer.global_step = ckpt.get("global_step", 0) + start_epoch = ckpt.get("epoch", 0) + trainer.set_epoch(start_epoch) + return trainer, start_epoch + + def load_checkpoint_optimizers(self, opt_ckpt): + for k, v in self.optimizers.items(): + v.load_state_dict(opt_ckpt[k]) + + def load_checkpoint_schedulers(self, sched_ckpt): + for k, v in self.scheduler.items(): + v.load_state_dict(sched_ckpt[k]) + + @torch.inference_mode() + def render_fn(self, camera_state: CameraState, img_wh: tuple[int, int]): + W, H = img_wh + + focal = 0.5 * H / np.tan(0.5 * camera_state.fov).item() + K = torch.tensor( + [[focal, 0.0, W / 2.0], [0.0, focal, H / 2.0], [0.0, 0.0, 1.0]], + device=self.device, + ) + w2c = torch.linalg.inv( + torch.from_numpy(camera_state.c2w.astype(np.float32)).to(self.device) + ) + t = 0 + if self.viewer is not None: + t = ( + int(self.viewer._playback_guis[0].value) + if not self.viewer._canonical_checkbox.value + else None + ) + self.model.training = False + img = self.model.render(t, w2c[None], K[None], img_wh)["img"][0] + return (img.cpu().numpy() * 255.0).astype(np.uint8) + + def train_step(self, batch): + if self.viewer is not None: + while self.viewer.state.status == "paused": + time.sleep(0.1) + self.viewer.lock.acquire() + + loss, stats, num_rays_per_step, num_rays_per_sec = self.compute_losses(batch) + if loss.isnan(): + guru.info(f"Loss is NaN at step {self.global_step}!!") + import ipdb + + ipdb.set_trace() + loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad(set_to_none=True) + for sched in self.scheduler.values(): + sched.step() + + self.log_dict(stats) + self.global_step += 1 + self.run_control_steps() + + if self.viewer is not None: + self.viewer.lock.release() + self.viewer.state.num_train_rays_per_sec = num_rays_per_sec + if self.viewer.mode == "training": + self.viewer.update(self.global_step, num_rays_per_step) + + if self.global_step % self.checkpoint_every == 0: + self.save_checkpoint(f"{self.work_dir}/checkpoints/last.ckpt") + + return loss.item() + + def compute_losses(self, batch): + self.model.training = True + B = batch["imgs"].shape[0] + W, H = img_wh = batch["imgs"].shape[2:0:-1] + N = batch["target_ts"][0].shape[0] + + # (B,). + ts = batch["ts"] + # (B, 4, 4). + w2cs = batch["w2cs"] + # (B, 3, 3). + Ks = batch["Ks"] + # (B, H, W, 3). + imgs = batch["imgs"] + # (B, H, W). + valid_masks = batch.get("valid_masks", torch.ones_like(batch["imgs"][..., 0])) + # (B, H, W). + masks = batch["masks"] + masks *= valid_masks + # (B, H, W). + depths = batch["depths"] + # [(P, 2), ...]. + query_tracks_2d = batch["query_tracks_2d"] + # [(N,), ...]. + target_ts = batch["target_ts"] + # [(N, 4, 4), ...]. + target_w2cs = batch["target_w2cs"] + # [(N, 3, 3), ...]. + target_Ks = batch["target_Ks"] + # [(N, P, 2), ...]. + target_tracks_2d = batch["target_tracks_2d"] + # [(N, P), ...]. + target_visibles = batch["target_visibles"] + # [(N, P), ...]. + target_invisibles = batch["target_invisibles"] + # [(N, P), ...]. + target_confidences = batch["target_confidences"] + # [(N, P), ...]. + target_track_depths = batch["target_track_depths"] + + _tic = time.time() + # (B, G, 3). + means, quats = self.model.compute_poses_all(ts) # (G, B, 3), (G, B, 4) + device = means.device + means = means.transpose(0, 1) + quats = quats.transpose(0, 1) + # [(N, G, 3), ...]. + target_ts_vec = torch.cat(target_ts) + # (B * N, G, 3). + target_means, _ = self.model.compute_poses_all(target_ts_vec) + target_means = target_means.transpose(0, 1) + target_mean_list = target_means.split(N) + num_frames = self.model.num_frames + + loss = 0.0 + + bg_colors = [] + rendered_all = [] + self._batched_xys = [] + self._batched_radii = [] + self._batched_img_wh = [] + for i in range(B): + bg_color = torch.ones(1, 3, device=device) + rendered = self.model.render( + ts[i].item(), + w2cs[None, i], + Ks[None, i], + img_wh, + target_ts=target_ts[i], + target_w2cs=target_w2cs[i], + bg_color=bg_color, + means=means[i], + quats=quats[i], + target_means=target_mean_list[i].transpose(0, 1), + return_depth=True, + return_mask=self.model.has_bg, + ) + rendered_all.append(rendered) + bg_colors.append(bg_color) + if ( + self.model._current_xys is not None + and self.model._current_radii is not None + and self.model._current_img_wh is not None + ): + self._batched_xys.append(self.model._current_xys) + self._batched_radii.append(self.model._current_radii) + self._batched_img_wh.append(self.model._current_img_wh) + + # Necessary to make viewer work. + num_rays_per_step = H * W * B + num_rays_per_sec = num_rays_per_step / (time.time() - _tic) + + # (B, H, W, N, *). + rendered_all = { + key: ( + torch.cat([out_dict[key] for out_dict in rendered_all], dim=0) + if rendered_all[0][key] is not None + else None + ) + for key in rendered_all[0] + } + bg_colors = torch.cat(bg_colors, dim=0) + + # Compute losses. + # (B * N). + frame_intervals = (ts.repeat_interleave(N) - target_ts_vec).abs() + if not self.model.has_bg: + imgs = ( + imgs * masks[..., None] + + (1.0 - masks[..., None]) * bg_colors[:, None, None] + ) + else: + imgs = ( + imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + # (P_all, 2). + tracks_2d = torch.cat([x.reshape(-1, 2) for x in target_tracks_2d], dim=0) + # (P_all,) + visibles = torch.cat([x.reshape(-1) for x in target_visibles], dim=0) + # (P_all,) + confidences = torch.cat([x.reshape(-1) for x in target_confidences], dim=0) + + # RGB loss. + rendered_imgs = cast(torch.Tensor, rendered_all["img"]) + if self.model.has_bg: + rendered_imgs = ( + rendered_imgs * valid_masks[..., None] + + (1.0 - valid_masks[..., None]) * bg_colors[:, None, None] + ) + rgb_loss = 0.8 * F.l1_loss(rendered_imgs, imgs) + 0.2 * ( + 1 - self.ssim(rendered_imgs.permute(0, 3, 1, 2), imgs.permute(0, 3, 1, 2)) + ) + loss += rgb_loss * self.losses_cfg.w_rgb + + # Mask loss. + if not self.model.has_bg: + mask_loss = F.mse_loss(rendered_all["acc"], masks[..., None]) # type: ignore + else: + mask_loss = F.mse_loss( + rendered_all["acc"], torch.ones_like(rendered_all["acc"]) # type: ignore + ) + masked_l1_loss( + rendered_all["mask"], + masks[..., None], + quantile=0.98, # type: ignore + ) + loss += mask_loss * self.losses_cfg.w_mask + + # (B * N, H * W, 3). + pred_tracks_3d = ( + rendered_all["tracks_3d"].permute(0, 3, 1, 2, 4).reshape(-1, H * W, 3) # type: ignore + ) + pred_tracks_2d = torch.einsum( + "bij,bpj->bpi", torch.cat(target_Ks), pred_tracks_3d + ) + # (B * N, H * W, 1). + mapped_depth = torch.clamp(pred_tracks_2d[..., 2:], min=1e-6) + # (B * N, H * W, 2). + pred_tracks_2d = pred_tracks_2d[..., :2] / mapped_depth + + # (B * N). + w_interval = torch.exp(-2 * frame_intervals / num_frames) + # w_track_loss = min(1, (self.max_steps - self.global_step) / 6000) + track_weights = confidences[..., None] * w_interval + + # (B, H, W). + masks_flatten = torch.zeros_like(masks) + for i in range(B): + # This takes advantage of the fact that the query 2D tracks are + # always on the grid. + query_pixels = query_tracks_2d[i].to(torch.int64) + masks_flatten[i, query_pixels[:, 1], query_pixels[:, 0]] = 1.0 + # (B * N, H * W). + masks_flatten = ( + masks_flatten.reshape(-1, H * W).tile(1, N).reshape(-1, H * W) > 0.5 + ) + + track_2d_loss = masked_l1_loss( + pred_tracks_2d[masks_flatten][visibles], + tracks_2d[visibles], + mask=track_weights[visibles], + quantile=0.98, + ) / max(H, W) + loss += track_2d_loss * self.losses_cfg.w_track + + depth_masks = ( + masks[..., None] if not self.model.has_bg else valid_masks[..., None] + ) + + pred_depth = cast(torch.Tensor, rendered_all["depth"]) + pred_disp = 1.0 / (pred_depth + 1e-5) + tgt_disp = 1.0 / (depths[..., None] + 1e-5) + depth_loss = masked_l1_loss( + pred_disp, + tgt_disp, + mask=depth_masks, + quantile=0.98, + ) + # depth_loss = cauchy_loss_with_uncertainty( + # pred_disp.squeeze(-1), + # tgt_disp.squeeze(-1), + # depth_masks.squeeze(-1), + # self.depth_uncertainty_activation(self.depth_uncertainties)[ts], + # bias=1e-3, + # ) + loss += depth_loss * self.losses_cfg.w_depth_reg + + # mapped depth loss (using cached depth with EMA) + # mapped_depth_loss = 0.0 + mapped_depth_gt = torch.cat([x.reshape(-1) for x in target_track_depths], dim=0) + mapped_depth_loss = masked_l1_loss( + 1 / (mapped_depth[masks_flatten][visibles] + 1e-5), + 1 / (mapped_depth_gt[visibles, None] + 1e-5), + track_weights[visibles], + ) + + loss += mapped_depth_loss * self.losses_cfg.w_depth_const + + # depth_gradient_loss = 0.0 + depth_gradient_loss = compute_gradient_loss( + pred_disp, + tgt_disp, + mask=depth_masks > 0.5, + quantile=0.95, + ) + # depth_gradient_loss = compute_gradient_loss( + # pred_disps, + # ref_disps, + # mask=depth_masks.squeeze(-1) > 0.5, + # c=depth_uncertainty.detach(), + # mode="l1", + # bias=1e-3, + # ) + loss += depth_gradient_loss * self.losses_cfg.w_depth_grad + + # bases should be smooth. + small_accel_loss = compute_se3_smoothness_loss( + self.model.motion_bases.params["rots"], + self.model.motion_bases.params["transls"], + ) + loss += small_accel_loss * self.losses_cfg.w_smooth_bases + + # tracks should be smooth + ts = torch.clamp(ts, min=1, max=num_frames - 2) + ts_neighbors = torch.cat((ts - 1, ts, ts + 1)) + transfms_nbs = self.model.compute_transforms(ts_neighbors) # (G, 3n, 3, 4) + means_fg_nbs = torch.einsum( + "pnij,pj->pni", + transfms_nbs, + F.pad(self.model.fg.params["means"], (0, 1), value=1.0), + ) + means_fg_nbs = means_fg_nbs.reshape( + means_fg_nbs.shape[0], 3, -1, 3 + ) # [G, 3, n, 3] + if self.losses_cfg.w_smooth_tracks > 0: + small_accel_loss_tracks = 0.5 * ( + (2 * means_fg_nbs[:, 1:-1] - means_fg_nbs[:, :-2] - means_fg_nbs[:, 2:]) + .norm(dim=-1) + .mean() + ) + loss += small_accel_loss_tracks * self.losses_cfg.w_smooth_tracks + + # Constrain the std of scales. + # TODO: do we want to penalize before or after exp? + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.fg.params["scales"], dim=-1).mean() + ) + if self.model.bg is not None: + loss += ( + self.losses_cfg.w_scale_var + * torch.var(self.model.bg.params["scales"], dim=-1).mean() + ) + + # # sparsity loss + # loss += 0.01 * self.opacity_activation(self.opacities).abs().mean() + + # Acceleration along ray direction should be small. + z_accel_loss = compute_z_acc_loss(means_fg_nbs, w2cs) + loss += self.losses_cfg.w_z_accel * z_accel_loss + + # Prepare stats for logging. + stats = { + "train/loss": loss.item(), + "train/rgb_loss": rgb_loss.item(), + "train/mask_loss": mask_loss.item(), + "train/depth_loss": depth_loss.item(), + "train/depth_gradient_loss": depth_gradient_loss.item(), + "train/mapped_depth_loss": mapped_depth_loss.item(), + "train/track_2d_loss": track_2d_loss.item(), + "train/small_accel_loss": small_accel_loss.item(), + "train/z_acc_loss": z_accel_loss.item(), + "train/num_gaussians": self.model.num_gaussians, + "train/num_fg_gaussians": self.model.num_fg_gaussians, + "train/num_bg_gaussians": self.model.num_bg_gaussians, + } + + # Compute metrics. + with torch.no_grad(): + psnr = self.psnr_metric( + rendered_imgs, imgs, masks if not self.model.has_bg else valid_masks + ) + self.psnr_metric.reset() + stats["train/psnr"] = psnr + if self.model.has_bg: + bg_psnr = self.bg_psnr_metric(rendered_imgs, imgs, 1.0 - masks) + fg_psnr = self.fg_psnr_metric(rendered_imgs, imgs, masks) + self.bg_psnr_metric.reset() + self.fg_psnr_metric.reset() + stats["train/bg_psnr"] = bg_psnr + stats["train/fg_psnr"] = fg_psnr + + stats.update( + **{ + "train/num_rays_per_sec": num_rays_per_sec, + "train/num_rays_per_step": float(num_rays_per_step), + } + ) + + return loss, stats, num_rays_per_step, num_rays_per_sec + + def log_dict(self, stats: dict): + for k, v in stats.items(): + self.writer.add_scalar(k, v, self.global_step) + + def run_control_steps(self): + global_step = self.global_step + # Adaptive gaussian control. + cfg = self.optim_cfg + num_frames = self.model.num_frames + ready = self._prepare_control_step() + if ( + ready + and global_step > cfg.warmup_steps + and global_step % cfg.control_every == 0 + and global_step < cfg.stop_control_steps + ): + if ( + global_step < cfg.stop_densify_steps + and global_step % self.reset_opacity_every > num_frames + ): + self._densify_control_step(global_step) + if global_step % self.reset_opacity_every > min(3 * num_frames, 1000): + self._cull_control_step(global_step) + if global_step % self.reset_opacity_every == 0: + self._reset_opacity_control_step() + + # Reset stats after every control. + for k in self.running_stats: + self.running_stats[k].zero_() + + @torch.no_grad() + def _prepare_control_step(self) -> bool: + # Prepare for adaptive gaussian control based on the current stats. + if not ( + self.model._current_radii is not None + and self.model._current_xys is not None + ): + guru.warning("Model not training, skipping control step preparation") + return False + + batch_size = len(self._batched_xys) + # these quantities are for each rendered view and have shapes (C, G, *) + # must be aggregated over all views + for _current_xys, _current_radii, _current_img_wh in zip( + self._batched_xys, self._batched_radii, self._batched_img_wh + ): + sel = _current_radii > 0 + gidcs = torch.where(sel)[1] + # normalize grads to [-1, 1] screen space + xys_grad = _current_xys.grad.clone() + xys_grad[..., 0] *= _current_img_wh[0] / 2.0 * batch_size + xys_grad[..., 1] *= _current_img_wh[1] / 2.0 * batch_size + self.running_stats["xys_grad_norm_acc"].index_add_( + 0, gidcs, xys_grad[sel].norm(dim=-1) + ) + self.running_stats["vis_count"].index_add_( + 0, gidcs, torch.ones_like(gidcs, dtype=torch.int64) + ) + max_radii = torch.maximum( + self.running_stats["max_radii"].index_select(0, gidcs), + _current_radii[sel] / max(_current_img_wh), + ) + self.running_stats["max_radii"].index_put((gidcs,), max_radii) + return True + + @torch.no_grad() + def _densify_control_step(self, global_step): + assert (self.running_stats["vis_count"] > 0).any() + + cfg = self.optim_cfg + xys_grad_avg = self.running_stats["xys_grad_norm_acc"] / self.running_stats[ + "vis_count" + ].clamp_min(1) + is_grad_too_high = xys_grad_avg > cfg.densify_xys_grad_threshold + # Split gaussians. + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cfg.densify_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.densify_screen_threshold + ) + else: + is_radius_too_big = torch.zeros_like(is_grad_too_high, dtype=torch.bool) + + should_split = is_grad_too_high & (is_scale_too_big | is_radius_too_big) + should_dup = is_grad_too_high & ~is_scale_too_big + + num_fg = self.model.num_fg_gaussians + should_fg_split = should_split[:num_fg] + num_fg_splits = int(should_fg_split.sum().item()) + should_fg_dup = should_dup[:num_fg] + num_fg_dups = int(should_fg_dup.sum().item()) + + should_bg_split = should_split[num_fg:] + num_bg_splits = int(should_bg_split.sum().item()) + should_bg_dup = should_dup[num_fg:] + num_bg_dups = int(should_bg_dup.sum().item()) + + fg_param_map = self.model.fg.densify_params(should_fg_split, should_fg_dup) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_fg_split, + num_fg_splits * 2 + num_fg_dups, + ) + + if self.model.bg is not None: + bg_param_map = self.model.bg.densify_params(should_bg_split, should_bg_dup) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + dup_in_optim( + optimizer, + [new_params], + should_bg_split, + num_bg_splits * 2 + num_bg_dups, + ) + + # update running stats + for k, v in self.running_stats.items(): + v_fg, v_bg = v[:num_fg], v[num_fg:] + new_v = torch.cat( + [ + v_fg[~should_fg_split], + v_fg[should_fg_dup], + v_fg[should_fg_split].repeat(2), + v_bg[~should_bg_split], + v_bg[should_bg_dup], + v_bg[should_bg_split].repeat(2), + ], + dim=0, + ) + self.running_stats[k] = new_v + guru.info( + f"Split {should_split.sum().item()} gaussians, " + f"Duplicated {should_dup.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _cull_control_step(self, global_step): + # Cull gaussians. + cfg = self.optim_cfg + opacities = self.model.get_opacities_all() + device = opacities.device + is_opacity_too_small = opacities < cfg.cull_opacity_threshold + is_radius_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + is_scale_too_big = torch.zeros_like(is_opacity_too_small, dtype=torch.bool) + cull_scale_threshold = ( + torch.ones(len(is_scale_too_big), device=device) * cfg.cull_scale_threshold + ) + num_fg = self.model.num_fg_gaussians + cull_scale_threshold[num_fg:] *= self.model.bg_scene_scale + if global_step > self.reset_opacity_every: + scales = self.model.get_scales_all() + is_scale_too_big = scales.amax(dim=-1) > cull_scale_threshold + if global_step < cfg.stop_control_by_screen_steps: + is_radius_too_big = ( + self.running_stats["max_radii"] > cfg.cull_screen_threshold + ) + should_cull = is_opacity_too_small | is_radius_too_big | is_scale_too_big + should_fg_cull = should_cull[:num_fg] + should_bg_cull = should_cull[num_fg:] + + fg_param_map = self.model.fg.cull_params(should_fg_cull) + for param_name, new_params in fg_param_map.items(): + full_param_name = f"fg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_fg_cull) + + if self.model.bg is not None: + bg_param_map = self.model.bg.cull_params(should_bg_cull) + for param_name, new_params in bg_param_map.items(): + full_param_name = f"bg.params.{param_name}" + optimizer = self.optimizers[full_param_name] + remove_from_optim(optimizer, [new_params], should_bg_cull) + + # update running stats + for k, v in self.running_stats.items(): + self.running_stats[k] = v[~should_cull] + + guru.info( + f"Culled {should_cull.sum().item()} gaussians, " + f"{self.model.num_gaussians} gaussians left" + ) + + @torch.no_grad() + def _reset_opacity_control_step(self): + # Reset gaussian opacities. + new_val = torch.logit(torch.tensor(0.8 * self.optim_cfg.cull_opacity_threshold)) + for part in ["fg", "bg"]: + part_params = getattr(self.model, part).reset_opacities(new_val) + # Modify optimizer states by new assignment. + for param_name, new_params in part_params.items(): + full_param_name = f"{part}.params.{param_name}" + optimizer = self.optimizers[full_param_name] + reset_in_optim(optimizer, [new_params]) + guru.info("Reset opacities") + + def configure_optimizers(self): + def _exponential_decay(step, *, lr_init, lr_final): + t = np.clip(step / self.optim_cfg.max_steps, 0.0, 1.0) + lr = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) + return lr / lr_init + + lr_dict = asdict(self.lr_cfg) + optimizers = {} + schedulers = {} + # named parameters will be [part].params.[field] + # e.g. fg.params.means + # lr config is a nested dict for each fg/bg part + for name, params in self.model.named_parameters(): + part, _, field = name.split(".") + lr = lr_dict[part][field] + optim = torch.optim.Adam([{"params": params, "lr": lr, "name": name}]) + + if "scales" in name: + fnc = functools.partial(_exponential_decay, lr_final=0.1 * lr) + else: + fnc = lambda _, **__: 1.0 + + optimizers[name] = optim + schedulers[name] = torch.optim.lr_scheduler.LambdaLR( + optim, functools.partial(fnc, lr_init=lr) + ) + return optimizers, schedulers + + +def dup_in_optim(optimizer, new_params: list, should_dup: torch.Tensor, num_dups: int): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + p = param_state[key] + param_state[key] = torch.cat( + [p[~should_dup], p.new_zeros(num_dups, *p.shape[1:])], + dim=0, + ) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def remove_from_optim(optimizer, new_params: list, _should_cull: torch.Tensor): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + if key == "step": + continue + param_state[key] = param_state[key][~_should_cull] + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() + + +def reset_in_optim(optimizer, new_params: list): + assert len(optimizer.param_groups) == len(new_params) + for i, p_new in enumerate(new_params): + old_params = optimizer.param_groups[i]["params"][0] + param_state = optimizer.state[old_params] + if len(param_state) == 0: + return + for key in param_state: + param_state[key] = torch.zeros_like(param_state[key]) + del optimizer.state[old_params] + optimizer.state[p_new] = param_state + optimizer.param_groups[i]["params"] = [p_new] + del old_params + torch.cuda.empty_cache() diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/trajectories.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/trajectories.py new file mode 100644 index 0000000000000000000000000000000000000000..98eb9350fd368c5d6e1a1621fa068d79fc5fc5c7 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/trajectories.py @@ -0,0 +1,200 @@ +import numpy as np +import roma +import torch +import torch.nn.functional as F + +from .transforms import rt_to_mat4 + + +def get_avg_w2c(w2cs: torch.Tensor): + c2ws = torch.linalg.inv(w2cs) + # 1. Compute the center + center = c2ws[:, :3, -1].mean(0) + # 2. Compute the z axis + z = F.normalize(c2ws[:, :3, 2].mean(0), dim=-1) + # 3. Compute axis y' (no need to normalize as it's not the final output) + y_ = c2ws[:, :3, 1].mean(0) # (3) + # 4. Compute the x axis + x = F.normalize(torch.cross(y_, z, dim=-1), dim=-1) # (3) + # 5. Compute the y axis (as z and x are normalized, y is already of norm 1) + y = torch.cross(z, x, dim=-1) # (3) + avg_c2w = rt_to_mat4(torch.stack([x, y, z], 1), center) + avg_w2c = torch.linalg.inv(avg_c2w) + return avg_w2c + + +def get_lookat(origins: torch.Tensor, viewdirs: torch.Tensor) -> torch.Tensor: + """Triangulate a set of rays to find a single lookat point. + + Args: + origins (torch.Tensor): A (N, 3) array of ray origins. + viewdirs (torch.Tensor): A (N, 3) array of ray view directions. + + Returns: + torch.Tensor: A (3,) lookat point. + """ + + viewdirs = torch.nn.functional.normalize(viewdirs, dim=-1) + eye = torch.eye(3, device=origins.device, dtype=origins.dtype)[None] + # Calculate projection matrix I - rr^T + I_min_cov = eye - (viewdirs[..., None] * viewdirs[..., None, :]) + # Compute sum of projections + sum_proj = I_min_cov.matmul(origins[..., None]).sum(dim=-3) + # Solve for the intersection point using least squares + lookat = torch.linalg.lstsq(I_min_cov.sum(dim=-3), sum_proj).solution[..., 0] + # Check NaNs. + assert not torch.any(torch.isnan(lookat)) + return lookat + + +def get_lookat_w2cs(positions: torch.Tensor, lookat: torch.Tensor, up: torch.Tensor): + """ + Args: + positions: (N, 3) tensor of camera positions + lookat: (3,) tensor of lookat point + up: (3,) tensor of up vector + + Returns: + w2cs: (N, 3, 3) tensor of world to camera rotation matrices + """ + forward_vectors = F.normalize(lookat - positions, dim=-1) + right_vectors = F.normalize(torch.cross(forward_vectors, up[None], dim=-1), dim=-1) + down_vectors = F.normalize( + torch.cross(forward_vectors, right_vectors, dim=-1), dim=-1 + ) + Rs = torch.stack([right_vectors, down_vectors, forward_vectors], dim=-1) + w2cs = torch.linalg.inv(rt_to_mat4(Rs, positions)) + return w2cs + + +def get_arc_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_position = torch.linalg.inv(ref_w2c)[:3, 3] + thetas = ( + torch.sin( + torch.linspace(0.0, torch.pi * 2.0, num_frames + 1, device=ref_w2c.device)[ + :-1 + ] + ) + * (degree / 2.0) + / 180.0 + * torch.pi + ) + positions = torch.einsum( + "nij,j->ni", + roma.rotvec_to_rotmat(thetas[:, None] * up[None]), + ref_position - lookat, + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_lemniscate_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + degree: float, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + a = torch.linalg.norm(ref_c2w[:3, 3] - lookat) * np.tan(degree / 360 * np.pi) + # Lemniscate curve in camera space. Starting at the origin. + thetas = ( + torch.linspace(0, 2 * torch.pi, num_frames + 1, device=ref_w2c.device)[:-1] + + torch.pi / 2 + ) + positions = torch.stack( + [ + a * torch.cos(thetas) / (1 + torch.sin(thetas) ** 2), + a * torch.cos(thetas) * torch.sin(thetas) / (1 + torch.sin(thetas) ** 2), + torch.zeros(num_frames, device=ref_w2c.device), + ], + dim=-1, + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_spiral_w2cs( + ref_w2c: torch.Tensor, + lookat: torch.Tensor, + up: torch.Tensor, + num_frames: int, + rads: float | torch.Tensor, + zrate: float, + rots: int, + **_, +) -> torch.Tensor: + ref_c2w = torch.linalg.inv(ref_w2c) + thetas = torch.linspace( + 0, 2 * torch.pi * rots, num_frames + 1, device=ref_w2c.device + )[:-1] + # Spiral curve in camera space. Starting at the origin. + if isinstance(rads, torch.Tensor): + rads = rads.reshape(-1, 3).to(ref_w2c.device) + positions = ( + torch.stack( + [ + torch.cos(thetas), + -torch.sin(thetas), + -torch.sin(thetas * zrate), + ], + dim=-1, + ) + * rads + ) + # Transform to world space. + positions = torch.einsum( + "ij,nj->ni", ref_c2w[:3], F.pad(positions, (0, 1), value=1.0) + ) + return get_lookat_w2cs(positions, lookat, up) + + +def get_wander_w2cs(ref_w2c, focal_length, num_frames, **_): + device = ref_w2c.device + c2w = np.linalg.inv(ref_w2c.detach().cpu().numpy()) + max_disp = 48.0 + + max_trans = max_disp / focal_length + output_poses = [] + + for i in range(num_frames): + x_trans = max_trans * np.sin(2.0 * np.pi * float(i) / float(num_frames)) + y_trans = 0.0 + z_trans = max_trans * np.cos(2.0 * np.pi * float(i) / float(num_frames)) / 2.0 + + i_pose = np.concatenate( + [ + np.concatenate( + [ + np.eye(3), + np.array([x_trans, y_trans, z_trans])[:, np.newaxis], + ], + axis=1, + ), + np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :], + ], + axis=0, + ) + + i_pose = np.linalg.inv(i_pose) + + ref_pose = np.concatenate( + [c2w[:3, :4], np.array([0.0, 0.0, 0.0, 1.0])[np.newaxis, :]], axis=0 + ) + + render_pose = np.dot(ref_pose, i_pose) + output_poses.append(render_pose) + output_poses = torch.from_numpy(np.array(output_poses, dtype=np.float32)).to(device) + w2cs = torch.linalg.inv(output_poses) + + return w2cs diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/transforms.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff7cc260c586d6da054729c7cda2ce8c57cecd9 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/transforms.py @@ -0,0 +1,129 @@ +from typing import Literal + +import roma +import torch +import torch.nn.functional as F + + +def rt_to_mat4( + R: torch.Tensor, t: torch.Tensor, s: torch.Tensor | None = None +) -> torch.Tensor: + """ + Args: + R (torch.Tensor): (..., 3, 3). + t (torch.Tensor): (..., 3). + s (torch.Tensor): (...,). + + Returns: + torch.Tensor: (..., 4, 4) + """ + mat34 = torch.cat([R, t[..., None]], dim=-1) + if s is None: + bottom = ( + mat34.new_tensor([[0.0, 0.0, 0.0, 1.0]]) + .reshape((1,) * (mat34.dim() - 2) + (1, 4)) + .expand(mat34.shape[:-2] + (1, 4)) + ) + else: + bottom = F.pad(1.0 / s[..., None, None], (3, 0), value=0.0) + mat4 = torch.cat([mat34, bottom], dim=-2) + return mat4 + + +def rmat_to_cont_6d(matrix): + """ + :param matrix (*, 3, 3) + :returns 6d vector (*, 6) + """ + return torch.cat([matrix[..., 0], matrix[..., 1]], dim=-1) + + +def cont_6d_to_rmat(cont_6d): + """ + :param 6d vector (*, 6) + :returns matrix (*, 3, 3) + """ + x1 = cont_6d[..., 0:3] + y1 = cont_6d[..., 3:6] + + x = F.normalize(x1, dim=-1) + y = F.normalize(y1 - (y1 * x).sum(dim=-1, keepdim=True) * x, dim=-1) + z = torch.linalg.cross(x, y, dim=-1) + + return torch.stack([x, y, z], dim=-1) + + +def solve_procrustes( + src: torch.Tensor, + dst: torch.Tensor, + weights: torch.Tensor | None = None, + enforce_se3: bool = False, + rot_type: Literal["quat", "mat", "6d"] = "quat", +): + """ + Solve the Procrustes problem to align two point clouds, by solving the + following problem: + + min_{s, R, t} || s * (src @ R.T + t) - dst ||_2, s.t. R.T @ R = I and det(R) = 1. + + Args: + src (torch.Tensor): (N, 3). + dst (torch.Tensor): (N, 3). + weights (torch.Tensor | None): (N,), optional weights for alignment. + enforce_se3 (bool): Whether to enforce the transfm to be SE3. + + Returns: + sim3 (tuple[torch.Tensor, torch.Tensor, torch.Tensor]): + q (torch.Tensor): (4,), rotation component in quaternion of WXYZ + format. + t (torch.Tensor): (3,), translation component. + s (torch.Tensor): (), scale component. + error (torch.Tensor): (), average L2 distance after alignment. + """ + # Compute weights. + if weights is None: + weights = src.new_ones(src.shape[0]) + weights = weights[:, None] / weights.sum() + # Normalize point positions. + src_mean = (src * weights).sum(dim=0) + dst_mean = (dst * weights).sum(dim=0) + src_cent = src - src_mean + dst_cent = dst - dst_mean + # Normalize point scales. + if not enforce_se3: + src_scale = (src_cent**2 * weights).sum(dim=-1).mean().sqrt() + dst_scale = (dst_cent**2 * weights).sum(dim=-1).mean().sqrt() + else: + src_scale = dst_scale = src.new_tensor(1.0) + src_scaled = src_cent / src_scale + dst_scaled = dst_cent / dst_scale + # Compute the matrix for the singular value decomposition (SVD). + matrix = (weights * dst_scaled).T @ src_scaled + U, _, Vh = torch.linalg.svd(matrix) + # Special reflection case. + S = torch.eye(3, device=src.device) + if torch.det(U) * torch.det(Vh) < 0: + S[2, 2] = -1 + R = U @ S @ Vh + # Compute the transformation. + if rot_type == "quat": + rot = roma.rotmat_to_unitquat(R).roll(1, dims=-1) + elif rot_type == "6d": + rot = rmat_to_cont_6d(R) + else: + rot = R + s = dst_scale / src_scale + t = dst_mean / s - src_mean @ R.T + sim3 = rot, t, s + # Debug: error. + procrustes_dst = torch.einsum( + "ij,nj->ni", rt_to_mat4(R, t, s), F.pad(src, (0, 1), value=1.0) + ) + procrustes_dst = procrustes_dst[:, :3] / procrustes_dst[:, 3:] + error_before = (torch.linalg.norm(dst - src, dim=-1) * weights[:, 0]).sum() + error = (torch.linalg.norm(dst - procrustes_dst, dim=-1) * weights[:, 0]).sum() + # print(f"Procrustes error: {error_before} -> {error}") + # if error_before < error: + # print("Something is wrong.") + # __import__("ipdb").set_trace() + return sim3, (error.item(), error_before.item()) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/validator.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..2dde198e86fac558e886b4fcac910dad3615430c --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/validator.py @@ -0,0 +1,421 @@ +import functools +import os +import os.path as osp +import time +from dataclasses import asdict +from typing import cast + +import imageio as iio +import numpy as np +import torch +import torch.nn.functional as F +from loguru import logger as guru +from nerfview import CameraState, Viewer +from pytorch_msssim import SSIM +from torch.utils.data import DataLoader, Dataset +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +from flow3d.configs import LossesConfig, OptimizerConfig, SceneLRConfig +from flow3d.data.utils import normalize_coords, to_device +from flow3d.metrics import PCK, mLPIPS, mPSNR, mSSIM +from flow3d.scene_model import SceneModel +from flow3d.vis.utils import ( + apply_depth_colormap, + make_video_divisble, + plot_correspondences, +) + + +class Validator: + def __init__( + self, + model: SceneModel, + device: torch.device, + train_loader: DataLoader | None, + val_img_loader: DataLoader | None, + val_kpt_loader: DataLoader | None, + save_dir: str, + ): + self.model = model + self.device = device + self.train_loader = train_loader + self.val_img_loader = val_img_loader + self.val_kpt_loader = val_kpt_loader + self.save_dir = save_dir + self.has_bg = self.model.has_bg + + # metrics + self.ssim = SSIM(data_range=1.0, size_average=True, channel=3) + self.psnr_metric = mPSNR() + self.ssim_metric = mSSIM() + self.lpips_metric = mLPIPS().to(device) + self.fg_psnr_metric = mPSNR() + self.fg_ssim_metric = mSSIM() + self.fg_lpips_metric = mLPIPS().to(device) + self.bg_psnr_metric = mPSNR() + self.bg_ssim_metric = mSSIM() + self.bg_lpips_metric = mLPIPS().to(device) + self.pck_metric = PCK() + + def reset_metrics(self): + self.psnr_metric.reset() + self.ssim_metric.reset() + self.lpips_metric.reset() + self.fg_psnr_metric.reset() + self.fg_ssim_metric.reset() + self.fg_lpips_metric.reset() + self.bg_psnr_metric.reset() + self.bg_ssim_metric.reset() + self.bg_lpips_metric.reset() + self.pck_metric.reset() + + @torch.no_grad() + def validate(self): + self.reset_metrics() + metric_imgs = self.validate_imgs() or {} + metric_kpts = self.validate_keypoints() or {} + return {**metric_imgs, **metric_kpts} + + @torch.no_grad() + def validate_imgs(self): + guru.info("rendering validation images...") + if self.val_img_loader is None: + return + + for batch in tqdm(self.val_img_loader, desc="render val images"): + batch = to_device(batch, self.device) + frame_name = batch["frame_names"][0] + t = batch["ts"][0] + # (1, 4, 4). + w2c = batch["w2cs"] + # (1, 3, 3). + K = batch["Ks"] + # (1, H, W, 3). + img = batch["imgs"] + # (1, H, W). + valid_mask = batch.get( + "valid_masks", torch.ones_like(batch["imgs"][..., 0]) + ) + # (1, H, W). + fg_mask = batch["masks"] + + # (H, W). + covisible_mask = batch.get( + "covisible_masks", + torch.ones_like(fg_mask)[None], + ) + W, H = img_wh = img[0].shape[-2::-1] + rendered = self.model.render(t, w2c, K, img_wh, return_depth=True) + + # Compute metrics. + valid_mask *= covisible_mask + fg_valid_mask = fg_mask * valid_mask + bg_valid_mask = (1 - fg_mask) * valid_mask + main_valid_mask = valid_mask if self.has_bg else fg_valid_mask + + self.psnr_metric.update(rendered["img"], img, main_valid_mask) + self.ssim_metric.update(rendered["img"], img, main_valid_mask) + self.lpips_metric.update(rendered["img"], img, main_valid_mask) + + if self.has_bg: + self.fg_psnr_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_ssim_metric.update(rendered["img"], img, fg_valid_mask) + self.fg_lpips_metric.update(rendered["img"], img, fg_valid_mask) + + self.bg_psnr_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_ssim_metric.update(rendered["img"], img, bg_valid_mask) + self.bg_lpips_metric.update(rendered["img"], img, bg_valid_mask) + + # Dump results. + results_dir = osp.join(self.save_dir, "results", "rgb") + os.makedirs(results_dir, exist_ok=True) + iio.imwrite( + osp.join(results_dir, f"{frame_name}.png"), + (rendered["img"][0].cpu().numpy() * 255).astype(np.uint8), + ) + + return { + "val/psnr": self.psnr_metric.compute(), + "val/ssim": self.ssim_metric.compute(), + "val/lpips": self.lpips_metric.compute(), + "val/fg_psnr": self.fg_psnr_metric.compute(), + "val/fg_ssim": self.fg_ssim_metric.compute(), + "val/fg_lpips": self.fg_lpips_metric.compute(), + "val/bg_psnr": self.bg_psnr_metric.compute(), + "val/bg_ssim": self.bg_ssim_metric.compute(), + "val/bg_lpips": self.bg_lpips_metric.compute(), + } + + @torch.no_grad() + def validate_keypoints(self): + if self.val_kpt_loader is None: + return + pred_keypoints_3d_all = [] + time_ids = self.val_kpt_loader.dataset.time_ids.tolist() + h, w = self.val_kpt_loader.dataset.dataset.imgs.shape[1:3] + pred_train_depths = np.zeros((len(time_ids), h, w)) + + for batch in tqdm(self.val_kpt_loader, desc="render val keypoints"): + batch = to_device(batch, self.device) + # (2,). + ts = batch["ts"][0] + # (2, 4, 4). + w2cs = batch["w2cs"][0] + # (2, 3, 3). + Ks = batch["Ks"][0] + # (2, H, W, 3). + imgs = batch["imgs"][0] + # (2, P, 3). + keypoints = batch["keypoints"][0] + # (P,) + keypoint_masks = (keypoints[..., -1] > 0.5).all(dim=0) + src_keypoints, target_keypoints = keypoints[:, keypoint_masks, :2] + W, H = img_wh = imgs.shape[-2:0:-1] + rendered = self.model.render( + ts[0].item(), + w2cs[:1], + Ks[:1], + img_wh, + target_ts=ts[1:], + target_w2cs=w2cs[1:], + return_depth=True, + ) + pred_tracks_3d = rendered["tracks_3d"][0, ..., 0, :] + pred_tracks_2d = torch.einsum("ij,hwj->hwi", Ks[1], pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., -1:], min=1e-6 + ) + pred_keypoints = F.grid_sample( + pred_tracks_2d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + + # Compute metrics. + self.pck_metric.update(pred_keypoints, target_keypoints, max(img_wh) * 0.05) + + padded_keypoints_3d = torch.zeros_like(keypoints[0]) + pred_keypoints_3d = F.grid_sample( + pred_tracks_3d[None].permute(0, 3, 1, 2), + normalize_coords(src_keypoints, H, W)[None, None], + align_corners=True, + ).permute(0, 2, 3, 1)[0, 0] + # Transform 3D keypoints back to world space. + pred_keypoints_3d = torch.einsum( + "ij,pj->pi", + torch.linalg.inv(w2cs[1])[:3], + F.pad(pred_keypoints_3d, (0, 1), value=1.0), + ) + padded_keypoints_3d[keypoint_masks] = pred_keypoints_3d + # Cache predicted keypoints. + pred_keypoints_3d_all.append(padded_keypoints_3d.cpu().numpy()) + pred_train_depths[time_ids.index(ts[0].item())] = ( + rendered["depth"][0, ..., 0].cpu().numpy() + ) + + # Dump unified results. + all_Ks = self.val_kpt_loader.dataset.dataset.Ks + all_w2cs = self.val_kpt_loader.dataset.dataset.w2cs + + keypoint_result_dict = { + "Ks": all_Ks[time_ids].cpu().numpy(), + "w2cs": all_w2cs[time_ids].cpu().numpy(), + "pred_keypoints_3d": np.stack(pred_keypoints_3d_all, 0), + "pred_train_depths": pred_train_depths, + } + + results_dir = osp.join(self.save_dir, "results") + os.makedirs(results_dir, exist_ok=True) + np.savez( + osp.join(results_dir, "keypoints.npz"), + **keypoint_result_dict, + ) + guru.info( + f"Dumped keypoint results to {results_dir=} {keypoint_result_dict['pred_keypoints_3d'].shape=}" + ) + + return {"val/pck": self.pck_metric.compute()} + + @torch.no_grad() + def save_train_videos(self, epoch: int): + if self.train_loader is None: + return + video_dir = osp.join(self.save_dir, "videos", f"epoch_{epoch:04d}") + os.makedirs(video_dir, exist_ok=True) + fps = getattr(self.train_loader.dataset.dataset, "fps", 15.0) + # Render video. + video = [] + ref_pred_depths = [] + masks = [] + depth_min, depth_max = 1e6, 0 + for batch_idx, batch in enumerate( + tqdm(self.train_loader, desc="Rendering video", leave=False) + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (H, W, 3). + img = batch["imgs"][0] + # (H, W). + depth = batch["depths"][0] + + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, return_depth=True, return_mask=True + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + ref_pred_depth = torch.cat( + (depth[..., None], rendered["depth"][0]), dim=1 + ).cpu() + ref_pred_depths.append(ref_pred_depth) + depth_min = min(depth_min, ref_pred_depth.min().item()) + depth_max = max(depth_max, ref_pred_depth.quantile(0.99).item()) + if rendered["mask"] is not None: + masks.append(rendered["mask"][0].cpu().squeeze(-1)) + + # rgb video + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "rgbs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + # depth video + depth_video = torch.stack( + [ + apply_depth_colormap( + ref_pred_depth, near_plane=depth_min, far_plane=depth_max + ) + for ref_pred_depth in ref_pred_depths + ], + dim=0, + ) + iio.mimwrite( + osp.join(video_dir, "depths.mp4"), + make_video_divisble((depth_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + if len(masks) > 0: + # mask video + mask_video = torch.stack(masks, dim=0) + iio.mimwrite( + osp.join(video_dir, "masks.mp4"), + make_video_divisble((mask_video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) + + # Render 2D track video. + tracks_2d, target_imgs = [], [] + sample_interval = 10 + batch0 = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in self.train_loader.dataset[0].items() + } + # (). + t = batch0["ts"] + # (4, 4). + w2c = batch0["w2cs"] + # (3, 3). + K = batch0["Ks"] + # (H, W, 3). + img = batch0["imgs"] + # (H, W). + bool_mask = batch0["masks"] > 0.5 + img_wh = img.shape[-2::-1] + for batch in tqdm( + self.train_loader, desc="Rendering 2D track video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + # (1, H, W, 3). + target_imgs.append(batch["imgs"].cpu()) + # (1,). + target_ts = batch["ts"] + # (1, 4, 4). + target_w2cs = batch["w2cs"] + # (1, 3, 3). + target_Ks = batch["Ks"] + rendered = self.model.render( + t, + w2c[None], + K[None], + img_wh, + target_ts=target_ts, + target_w2cs=target_w2cs, + ) + pred_tracks_3d = rendered["tracks_3d"][0][ + ::sample_interval, ::sample_interval + ][bool_mask[::sample_interval, ::sample_interval]].swapaxes(0, 1) + pred_tracks_2d = torch.einsum("bij,bpj->bpi", target_Ks, pred_tracks_3d) + pred_tracks_2d = pred_tracks_2d[..., :2] / torch.clamp( + pred_tracks_2d[..., 2:], min=1e-6 + ) + tracks_2d.append(pred_tracks_2d.cpu()) + tracks_2d = torch.cat(tracks_2d, dim=0) + target_imgs = torch.cat(target_imgs, dim=0) + track_2d_video = plot_correspondences( + target_imgs.numpy(), + tracks_2d.numpy(), + query_id=cast(int, t), + ) + iio.mimwrite( + osp.join(video_dir, "tracks_2d.mp4"), + make_video_divisble(np.stack(track_2d_video, 0)), + fps=fps, + ) + # Render motion coefficient video. + with torch.random.fork_rng(): + torch.random.manual_seed(0) + motion_coef_colors = torch.pca_lowrank( + self.model.fg.get_coefs()[None], + q=3, + )[0][0] + motion_coef_colors = (motion_coef_colors - motion_coef_colors.min(0)[0]) / ( + motion_coef_colors.max(0)[0] - motion_coef_colors.min(0)[0] + ) + motion_coef_colors = F.pad( + motion_coef_colors, (0, 0, 0, self.model.bg.num_gaussians), value=0.5 + ) + video = [] + for batch in tqdm( + self.train_loader, desc="Rendering motion coefficient video", leave=False + ): + batch = { + k: v.to(self.device) if isinstance(v, torch.Tensor) else v + for k, v in batch.items() + } + # (). + t = batch["ts"][0] + # (4, 4). + w2c = batch["w2cs"][0] + # (3, 3). + K = batch["Ks"][0] + # (3, 3). + img = batch["imgs"][0] + img_wh = img.shape[-2::-1] + rendered = self.model.render( + t, w2c[None], K[None], img_wh, colors_override=motion_coef_colors + ) + # Putting results onto CPU since it will consume unnecessarily + # large GPU memory for long sequence OW. + video.append(torch.cat([img, rendered["img"][0]], dim=1).cpu()) + video = torch.stack(video, dim=0) + iio.mimwrite( + osp.join(video_dir, "motion_coefs.mp4"), + make_video_divisble((video.numpy() * 255).astype(np.uint8)), + fps=fps, + ) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__init__.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/__init__.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7212361a31ebf2bfb8af64e9bd8996b3a76aeb2b Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/__init__.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a93c45ff5ae1195bbe5bd9672786fad1809e135 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/playback_panel.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/render_panel.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/render_panel.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ec68ba905c7d346ffcf1b79a7f4f9b5d6d32a9d Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/render_panel.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/utils.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a13de83df1946336fd531a28f63a855bddcd5609 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/utils.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/viewer.cpython-310.pyc b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/viewer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c876505c80b0a96e975e2c5597207095ae563d70 Binary files /dev/null and b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/__pycache__/viewer.cpython-310.pyc differ diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/playback_panel.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/playback_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..20e6185b2dbf2b33aedf8da4ffc56ec35f2641f2 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/playback_panel.py @@ -0,0 +1,68 @@ +import threading +import time + +import viser + + +def add_gui_playback_group( + server: viser.ViserServer, + num_frames: int, + min_fps: float = 1.0, + max_fps: float = 60.0, + fps_step: float = 0.1, + initial_fps: float = 10.0, +): + gui_timestep = server.gui.add_slider( + "Timestep", + min=0, + max=num_frames - 1, + step=1, + initial_value=0, + disabled=True, + ) + gui_next_frame = server.gui.add_button("Next Frame") + gui_prev_frame = server.gui.add_button("Prev Frame") + gui_playing_pause = server.gui.add_button("Pause") + gui_playing_pause.visible = False + gui_playing_resume = server.gui.add_button("Resume") + gui_framerate = server.gui.add_slider( + "FPS", min=min_fps, max=max_fps, step=fps_step, initial_value=initial_fps + ) + + # Frame step buttons. + @gui_next_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + + @gui_prev_frame.on_click + def _(_) -> None: + gui_timestep.value = (gui_timestep.value - 1) % num_frames + + # Disable frame controls when we're playing. + def _toggle_gui_playing(_): + gui_playing_pause.visible = not gui_playing_pause.visible + gui_playing_resume.visible = not gui_playing_resume.visible + gui_timestep.disabled = gui_playing_pause.visible + gui_next_frame.disabled = gui_playing_pause.visible + gui_prev_frame.disabled = gui_playing_pause.visible + + gui_playing_pause.on_click(_toggle_gui_playing) + gui_playing_resume.on_click(_toggle_gui_playing) + + # Create a thread to update the timestep indefinitely. + def _update_timestep(): + while True: + if gui_playing_pause.visible: + gui_timestep.value = (gui_timestep.value + 1) % num_frames + time.sleep(1 / gui_framerate.value) + + threading.Thread(target=_update_timestep, daemon=True).start() + + return ( + gui_timestep, + gui_next_frame, + gui_prev_frame, + gui_playing_pause, + gui_playing_resume, + gui_framerate, + ) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/render_panel.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/render_panel.py new file mode 100644 index 0000000000000000000000000000000000000000..7f581a35cd71117d84a021bfb7bc05fe99eca3ae --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/render_panel.py @@ -0,0 +1,1165 @@ +# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import colorsys +import dataclasses +import datetime +import json +import threading +import time +from pathlib import Path +from typing import Dict, List, Literal, Optional, Tuple + +import numpy as np +import scipy +import splines +import splines.quaternion +import viser +import viser.transforms as tf + +VISER_SCALE_RATIO = 10.0 + + +@dataclasses.dataclass +class Keyframe: + time: float + position: np.ndarray + wxyz: np.ndarray + override_fov_enabled: bool + override_fov_rad: float + aspect: float + override_transition_enabled: bool + override_transition_sec: Optional[float] + + @staticmethod + def from_camera(time: float, camera: viser.CameraHandle, aspect: float) -> Keyframe: + return Keyframe( + time, + camera.position, + camera.wxyz, + override_fov_enabled=False, + override_fov_rad=camera.fov, + aspect=aspect, + override_transition_enabled=False, + override_transition_sec=None, + ) + + +class CameraPath: + def __init__( + self, server: viser.ViserServer, duration_element: viser.GuiInputHandle[float] + ): + self._server = server + self._keyframes: Dict[int, Tuple[Keyframe, viser.CameraFrustumHandle]] = {} + self._keyframe_counter: int = 0 + self._spline_nodes: List[viser.SceneNodeHandle] = [] + self._camera_edit_panel: Optional[viser.Gui3dContainerHandle] = None + + self._orientation_spline: Optional[splines.quaternion.KochanekBartels] = None + self._position_spline: Optional[splines.KochanekBartels] = None + self._fov_spline: Optional[splines.KochanekBartels] = None + self._time_spline: Optional[splines.KochanekBartels] = None + + self._keyframes_visible: bool = True + + self._duration_element = duration_element + + # These parameters should be overridden externally. + self.loop: bool = False + self.framerate: float = 30.0 + self.tension: float = 0.5 # Tension / alpha term. + self.default_fov: float = 0.0 + self.default_transition_sec: float = 0.0 + self.show_spline: bool = True + + def set_keyframes_visible(self, visible: bool) -> None: + self._keyframes_visible = visible + for keyframe in self._keyframes.values(): + keyframe[1].visible = visible + + def add_camera( + self, keyframe: Keyframe, keyframe_index: Optional[int] = None + ) -> None: + """Add a new camera, or replace an old one if `keyframe_index` is passed in.""" + server = self._server + + # Add a keyframe if we aren't replacing an existing one. + if keyframe_index is None: + keyframe_index = self._keyframe_counter + self._keyframe_counter += 1 + + print( + f"{keyframe.wxyz=} {keyframe.position=} {keyframe_index=} {keyframe.aspect=}" + ) + frustum_handle = server.scene.add_camera_frustum( + f"/render_cameras/{keyframe_index}", + fov=( + keyframe.override_fov_rad + if keyframe.override_fov_enabled + else self.default_fov + ), + aspect=keyframe.aspect, + scale=0.1, + color=(200, 10, 30), + wxyz=keyframe.wxyz, + position=keyframe.position, + visible=self._keyframes_visible, + ) + self._server.scene.add_icosphere( + f"/render_cameras/{keyframe_index}/sphere", + radius=0.03, + color=(200, 10, 30), + ) + + @frustum_handle.on_click + def _(_) -> None: + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=keyframe.position, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_fov = server.gui.add_checkbox( + "Override FOV", initial_value=keyframe.override_fov_enabled + ) + override_fov_degrees = server.gui.add_slider( + "Override FOV (degrees)", + 5.0, + 175.0, + step=0.1, + initial_value=keyframe.override_fov_rad * 180.0 / np.pi, + disabled=not keyframe.override_fov_enabled, + ) + delete_button = server.gui.add_button( + "Delete", color="red", icon=viser.Icon.TRASH + ) + go_to_button = server.gui.add_button("Go to") + close_button = server.gui.add_button("Close") + + @override_fov.on_update + def _(_) -> None: + keyframe.override_fov_enabled = override_fov.value + override_fov_degrees.disabled = not override_fov.value + self.add_camera(keyframe, keyframe_index) + + @override_fov_degrees.on_update + def _(_) -> None: + keyframe.override_fov_rad = override_fov_degrees.value / 180.0 * np.pi + self.add_camera(keyframe, keyframe_index) + + @delete_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + with event.client.gui.add_modal("Confirm") as modal: + event.client.gui.add_markdown("Delete keyframe?") + confirm_button = event.client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = event.client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + + keyframe_id = None + for i, keyframe_tuple in self._keyframes.items(): + if keyframe_tuple[1] is frustum_handle: + keyframe_id = i + break + assert keyframe_id is not None + + self._keyframes.pop(keyframe_id) + frustum_handle.remove() + camera_edit_panel.remove() + self._camera_edit_panel = None + modal.close() + self.update_spline() + + @exit_button.on_click + def _(_) -> None: + modal.close() + + @go_to_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + client = event.client + T_world_current = tf.SE3.from_rotation_and_translation( + tf.SO3(client.camera.wxyz), client.camera.position + ) + T_world_target = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5])) + + T_current_target = T_world_current.inverse() @ T_world_target + + for j in range(10): + T_world_set = T_world_current @ tf.SE3.exp( + T_current_target.log() * j / 9.0 + ) + + # Important bit: we atomically set both the orientation and the position + # of the camera. + with client.atomic(): + client.camera.wxyz = T_world_set.rotation().wxyz + client.camera.position = T_world_set.translation() + time.sleep(1.0 / 30.0) + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + self._keyframes[keyframe_index] = (keyframe, frustum_handle) + + def update_aspect(self, aspect: float) -> None: + for keyframe_index, frame in self._keyframes.items(): + frame = dataclasses.replace(frame[0], aspect=aspect) + self.add_camera(frame, keyframe_index=keyframe_index) + + def get_aspect(self) -> float: + """Get W/H aspect ratio, which is shared across all keyframes.""" + assert len(self._keyframes) > 0 + return next(iter(self._keyframes.values()))[0].aspect + + def reset(self) -> None: + for frame in self._keyframes.values(): + print(f"removing {frame[1]}") + frame[1].remove() + self._keyframes.clear() + self.update_spline() + print("camera path reset") + + def spline_t_from_t_sec(self, time: np.ndarray) -> np.ndarray: + """From a time value in seconds, compute a t value for our geometric + spline interpolation. An increment of 1 for the latter will move the + camera forward by one keyframe. + + We use a PCHIP spline here to guarantee monotonicity. + """ + transition_times_cumsum = self.compute_transition_times_cumsum() + spline_indices = np.arange(transition_times_cumsum.shape[0]) + + if self.loop: + # In the case of a loop, we pad the spline to match the start/end + # slopes. + interpolator = scipy.interpolate.PchipInterpolator( + x=np.concatenate( + [ + [-(transition_times_cumsum[-1] - transition_times_cumsum[-2])], + transition_times_cumsum, + transition_times_cumsum[-1:] + transition_times_cumsum[1:2], + ], + axis=0, + ), + y=np.concatenate( + [[-1], spline_indices, [spline_indices[-1] + 1]], axis=0 + ), + ) + else: + interpolator = scipy.interpolate.PchipInterpolator( + x=transition_times_cumsum, y=spline_indices + ) + + # Clip to account for floating point error. + return np.clip(interpolator(time), 0, spline_indices[-1]) + + def interpolate_pose_and_fov_rad( + self, normalized_t: float + ) -> Optional[Tuple[tf.SE3, float, float]]: + if len(self._keyframes) < 2: + return None + + self._time_spline = splines.KochanekBartels( + [keyframe[0].time for keyframe in self._keyframes.values()], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + self._fov_spline = splines.KochanekBartels( + [ + ( + keyframe[0].override_fov_rad + if keyframe[0].override_fov_enabled + else self.default_fov + ) + for keyframe in self._keyframes.values() + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + assert self._orientation_spline is not None + assert self._position_spline is not None + assert self._fov_spline is not None + assert self._time_spline is not None + + max_t = self.compute_duration() + t = max_t * normalized_t + spline_t = float(self.spline_t_from_t_sec(np.array(t))) + + quat = self._orientation_spline.evaluate(spline_t) + assert isinstance(quat, splines.quaternion.UnitQuaternion) + return ( + tf.SE3.from_rotation_and_translation( + tf.SO3(np.array([quat.scalar, *quat.vector])), + self._position_spline.evaluate(spline_t), + ), + float(self._fov_spline.evaluate(spline_t)), + float(self._time_spline.evaluate(spline_t)), + ) + + def update_spline(self) -> None: + num_frames = int(self.compute_duration() * self.framerate) + keyframes = list(self._keyframes.values()) + + if num_frames <= 0 or not self.show_spline or len(keyframes) < 2: + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + return + + transition_times_cumsum = self.compute_transition_times_cumsum() + + self._orientation_spline = splines.quaternion.KochanekBartels( + [ + splines.quaternion.UnitQuaternion.from_unit_xyzw( + np.roll(keyframe[0].wxyz, shift=-1) + ) + for keyframe in keyframes + ], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + self._position_spline = splines.KochanekBartels( + [keyframe[0].position for keyframe in keyframes], + tcb=(self.tension, 0.0, 0.0), + endconditions="closed" if self.loop else "natural", + ) + + # Update visualized spline. + points_array = self._position_spline.evaluate( + self.spline_t_from_t_sec( + np.linspace(0, transition_times_cumsum[-1], num_frames) + ) + ) + colors_array = np.array( + [ + colorsys.hls_to_rgb(h, 0.5, 1.0) + for h in np.linspace(0.0, 1.0, len(points_array)) + ] + ) + + # Clear prior spline nodes. + for node in self._spline_nodes: + node.remove() + self._spline_nodes.clear() + + self._spline_nodes.append( + self._server.scene.add_spline_catmull_rom( + "/render_camera_spline", + positions=points_array, + color=(220, 220, 220), + closed=self.loop, + line_width=1.0, + segments=points_array.shape[0] + 1, + ) + ) + self._spline_nodes.append( + self._server.scene.add_point_cloud( + "/render_camera_spline/points", + points=points_array, + colors=colors_array, + point_size=0.04, + ) + ) + + def make_transition_handle(i: int) -> None: + assert self._position_spline is not None + transition_pos = self._position_spline.evaluate( + float( + self.spline_t_from_t_sec( + (transition_times_cumsum[i] + transition_times_cumsum[i + 1]) + / 2.0, + ) + ) + ) + transition_sphere = self._server.scene.add_icosphere( + f"/render_camera_spline/transition_{i}", + radius=0.04, + color=(255, 0, 0), + position=transition_pos, + ) + self._spline_nodes.append(transition_sphere) + + @transition_sphere.on_click + def _(_) -> None: + server = self._server + + if self._camera_edit_panel is not None: + self._camera_edit_panel.remove() + self._camera_edit_panel = None + + keyframe_index = (i + 1) % len(self._keyframes) + keyframe = keyframes[keyframe_index][0] + + with server.scene.add_3d_gui_container( + "/camera_edit_panel", + position=transition_pos, + ) as camera_edit_panel: + self._camera_edit_panel = camera_edit_panel + override_transition_enabled = server.gui.add_checkbox( + "Override transition", + initial_value=keyframe.override_transition_enabled, + ) + override_transition_sec = server.gui.add_number( + "Override transition (sec)", + initial_value=( + keyframe.override_transition_sec + if keyframe.override_transition_sec is not None + else self.default_transition_sec + ), + min=0.001, + max=30.0, + step=0.001, + disabled=not override_transition_enabled.value, + ) + close_button = server.gui.add_button("Close") + + @override_transition_enabled.on_update + def _(_) -> None: + keyframe.override_transition_enabled = ( + override_transition_enabled.value + ) + override_transition_sec.disabled = ( + not override_transition_enabled.value + ) + self._duration_element.value = self.compute_duration() + + @override_transition_sec.on_update + def _(_) -> None: + keyframe.override_transition_sec = override_transition_sec.value + self._duration_element.value = self.compute_duration() + + @close_button.on_click + def _(_) -> None: + assert camera_edit_panel is not None + camera_edit_panel.remove() + self._camera_edit_panel = None + + (num_transitions_plus_1,) = transition_times_cumsum.shape + for i in range(num_transitions_plus_1 - 1): + make_transition_handle(i) + + # for i in range(transition_times.shape[0]) + + def compute_duration(self) -> float: + """Compute the total duration of the trajectory.""" + total = 0.0 + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0 and not self.loop: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + return total + + def compute_transition_times_cumsum(self) -> np.ndarray: + """Compute the total duration of the trajectory.""" + total = 0.0 + out = [0.0] + for i, (keyframe, frustum) in enumerate(self._keyframes.values()): + if i == 0: + continue + del frustum + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + if self.loop: + keyframe = next(iter(self._keyframes.values()))[0] + total += ( + keyframe.override_transition_sec + if keyframe.override_transition_enabled + and keyframe.override_transition_sec is not None + else self.default_transition_sec + ) + out.append(total) + + return np.array(out) + + +@dataclasses.dataclass +class RenderTabState: + """Useful GUI handles exposed by the render tab.""" + + preview_render: bool + preview_fov: float + preview_aspect: float + preview_camera_type: Literal["Perspective", "Fisheye", "Equirectangular"] + + +def populate_render_tab( + server: viser.ViserServer, + datapath: Path, + gui_timestep_handle: viser.GuiInputHandle[int] | None, +) -> RenderTabState: + + render_tab_state = RenderTabState( + preview_render=False, + preview_fov=0.0, + preview_aspect=1.0, + preview_camera_type="Perspective", + ) + + fov_degrees = server.gui.add_slider( + "Default FOV", + initial_value=75.0, + min=0.1, + max=175.0, + step=0.01, + hint="Field-of-view for rendering, which can also be overridden on a per-keyframe basis.", + ) + + @fov_degrees.on_update + def _(_) -> None: + fov_radians = fov_degrees.value / 180.0 * np.pi + for client in server.get_clients().values(): + client.camera.fov = fov_radians + camera_path.default_fov = fov_radians + + # Updating the aspect ratio will also re-render the camera frustums. + # Could rethink this. + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + resolution = server.gui.add_vector2( + "Resolution", + initial_value=(1920, 1080), + min=(50, 50), + max=(10_000, 10_000), + step=1, + hint="Render output resolution in pixels.", + ) + + @resolution.on_update + def _(_) -> None: + camera_path.update_aspect(resolution.value[0] / resolution.value[1]) + compute_and_update_preview_camera_state() + + camera_type = server.gui.add_dropdown( + "Camera type", + ("Perspective", "Fisheye", "Equirectangular"), + initial_value="Perspective", + hint="Camera model to render with. This is applied to all keyframes.", + ) + add_button = server.gui.add_button( + "Add Keyframe", + icon=viser.Icon.PLUS, + hint="Add a new keyframe at the current pose.", + ) + + @add_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + camera = server.get_clients()[event.client_id].camera + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(camera.wxyz), camera.position + ) + print(f"client {event.client_id} at {camera.position} {camera.wxyz}") + print(f"camera pose {pose.as_matrix()}") + if gui_timestep_handle is not None: + print(f"timestep {gui_timestep_handle.value}") + + # Add this camera to the path. + time = 0 + if gui_timestep_handle is not None: + time = gui_timestep_handle.value + camera_path.add_camera( + Keyframe.from_camera( + time, + camera, + aspect=resolution.value[0] / resolution.value[1], + ), + ) + duration_number.value = camera_path.compute_duration() + camera_path.update_spline() + + clear_keyframes_button = server.gui.add_button( + "Clear Keyframes", + icon=viser.Icon.TRASH, + hint="Remove all keyframes from the render path.", + ) + + @clear_keyframes_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client_id is not None + client = server.get_clients()[event.client_id] + with client.atomic(), client.gui.add_modal("Confirm") as modal: + client.gui.add_markdown("Clear all keyframes?") + confirm_button = client.gui.add_button( + "Yes", color="red", icon=viser.Icon.TRASH + ) + exit_button = client.gui.add_button("Cancel") + + @confirm_button.on_click + def _(_) -> None: + camera_path.reset() + modal.close() + + duration_number.value = camera_path.compute_duration() + + # Clear move handles. + if len(transform_controls) > 0: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + @exit_button.on_click + def _(_) -> None: + modal.close() + + loop = server.gui.add_checkbox( + "Loop", False, hint="Add a segment between the first and last keyframes." + ) + + @loop.on_update + def _(_) -> None: + camera_path.loop = loop.value + duration_number.value = camera_path.compute_duration() + + tension_slider = server.gui.add_slider( + "Spline tension", + min=0.0, + max=1.0, + initial_value=0.0, + step=0.01, + hint="Tension parameter for adjusting smoothness of spline interpolation.", + ) + + @tension_slider.on_update + def _(_) -> None: + camera_path.tension = tension_slider.value + camera_path.update_spline() + + move_checkbox = server.gui.add_checkbox( + "Move keyframes", + initial_value=False, + hint="Toggle move handles for keyframes in the scene.", + ) + + transform_controls: List[viser.SceneNodeHandle] = [] + + @move_checkbox.on_update + def _(event: viser.GuiEvent) -> None: + # Clear move handles when toggled off. + if move_checkbox.value is False: + for t in transform_controls: + t.remove() + transform_controls.clear() + return + + def _make_transform_controls_callback( + keyframe: Tuple[Keyframe, viser.SceneNodeHandle], + controls: viser.TransformControlsHandle, + ) -> None: + @controls.on_update + def _(_) -> None: + keyframe[0].wxyz = controls.wxyz + keyframe[0].position = controls.position + + keyframe[1].wxyz = controls.wxyz + keyframe[1].position = controls.position + + camera_path.update_spline() + + # Show move handles. + assert event.client is not None + for keyframe_index, keyframe in camera_path._keyframes.items(): + controls = event.client.scene.add_transform_controls( + f"/keyframe_move/{keyframe_index}", + scale=0.4, + wxyz=keyframe[0].wxyz, + position=keyframe[0].position, + ) + transform_controls.append(controls) + _make_transform_controls_callback(keyframe, controls) + + show_keyframe_checkbox = server.gui.add_checkbox( + "Show keyframes", + initial_value=True, + hint="Show keyframes in the scene.", + ) + + @show_keyframe_checkbox.on_update + def _(_: viser.GuiEvent) -> None: + camera_path.set_keyframes_visible(show_keyframe_checkbox.value) + + show_spline_checkbox = server.gui.add_checkbox( + "Show spline", + initial_value=True, + hint="Show camera path spline in the scene.", + ) + + @show_spline_checkbox.on_update + def _(_) -> None: + camera_path.show_spline = show_spline_checkbox.value + camera_path.update_spline() + + playback_folder = server.gui.add_folder("Playback") + with playback_folder: + play_button = server.gui.add_button("Play", icon=viser.Icon.PLAYER_PLAY) + pause_button = server.gui.add_button( + "Pause", icon=viser.Icon.PLAYER_PAUSE, visible=False + ) + preview_render_button = server.gui.add_button( + "Preview Render", hint="Show a preview of the render in the viewport." + ) + preview_render_stop_button = server.gui.add_button( + "Exit Render Preview", color="red", visible=False + ) + + transition_sec_number = server.gui.add_number( + "Transition (sec)", + min=0.001, + max=30.0, + step=0.001, + initial_value=2.0, + hint="Time in seconds between each keyframe, which can also be overridden on a per-transition basis.", + ) + framerate_number = server.gui.add_number( + "FPS", min=0.1, max=240.0, step=1e-2, initial_value=30.0 + ) + framerate_buttons = server.gui.add_button_group("", ("24", "30", "60")) + duration_number = server.gui.add_number( + "Duration (sec)", + min=0.0, + max=1e8, + step=0.001, + initial_value=0.0, + disabled=True, + ) + + @framerate_buttons.on_click + def _(_) -> None: + framerate_number.value = float(framerate_buttons.value) + + @transition_sec_number.on_update + def _(_) -> None: + camera_path.default_transition_sec = transition_sec_number.value + duration_number.value = camera_path.compute_duration() + + def get_max_frame_index() -> int: + return max(1, int(framerate_number.value * duration_number.value) - 1) + + preview_camera_handle: Optional[viser.SceneNodeHandle] = None + + def remove_preview_camera() -> None: + nonlocal preview_camera_handle + if preview_camera_handle is not None: + preview_camera_handle.remove() + preview_camera_handle = None + + def compute_and_update_preview_camera_state() -> ( + Optional[Tuple[tf.SE3, float, float]] + ): + """Update the render tab state with the current preview camera pose. + Returns current camera pose + FOV if available.""" + + if preview_frame_slider is None: + return + maybe_pose_and_fov_rad_and_time = camera_path.interpolate_pose_and_fov_rad( + preview_frame_slider.value / get_max_frame_index() + ) + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + render_tab_state.preview_fov = fov_rad + render_tab_state.preview_aspect = camera_path.get_aspect() + render_tab_state.preview_camera_type = camera_type.value + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + return pose, fov_rad, time + + def add_preview_frame_slider() -> Optional[viser.GuiInputHandle[int]]: + """Helper for creating the current frame # slider. This is removed and + re-added anytime the `max` value changes.""" + + with playback_folder: + preview_frame_slider = server.gui.add_slider( + "Preview frame", + min=0, + max=get_max_frame_index(), + step=1, + initial_value=0, + # Place right after the pause button. + order=preview_render_stop_button.order + 0.01, + disabled=get_max_frame_index() == 1, + ) + play_button.disabled = preview_frame_slider.disabled + preview_render_button.disabled = preview_frame_slider.disabled + + @preview_frame_slider.on_update + def _(_) -> None: + nonlocal preview_camera_handle + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + return + pose, fov_rad, time = maybe_pose_and_fov_rad_and_time + + preview_camera_handle = server.scene.add_camera_frustum( + "/preview_camera", + fov=fov_rad, + aspect=resolution.value[0] / resolution.value[1], + scale=0.35, + wxyz=pose.rotation().wxyz, + position=pose.translation(), + color=(10, 200, 30), + ) + if render_tab_state.preview_render: + for client in server.get_clients().values(): + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + return preview_frame_slider + + # We back up the camera poses before and after we start previewing renders. + camera_pose_backup_from_id: Dict[int, tuple] = {} + + @preview_render_button.on_click + def _(_) -> None: + render_tab_state.preview_render = True + preview_render_button.visible = False + preview_render_stop_button.visible = True + + maybe_pose_and_fov_rad_and_time = compute_and_update_preview_camera_state() + if maybe_pose_and_fov_rad_and_time is None: + remove_preview_camera() + return + pose, fov, time = maybe_pose_and_fov_rad_and_time + del fov + + # Hide all scene nodes when we're previewing the render. + server.scene.set_global_visibility(True) + + # Back up and then set camera poses. + for client in server.get_clients().values(): + camera_pose_backup_from_id[client.client_id] = ( + client.camera.position, + client.camera.look_at, + client.camera.up_direction, + ) + client.camera.wxyz = pose.rotation().wxyz + client.camera.position = pose.translation() + if gui_timestep_handle is not None: + gui_timestep_handle.value = int(time) + + @preview_render_stop_button.on_click + def _(_) -> None: + render_tab_state.preview_render = False + preview_render_button.visible = True + preview_render_stop_button.visible = False + + # Revert camera poses. + for client in server.get_clients().values(): + if client.client_id not in camera_pose_backup_from_id: + continue + cam_position, cam_look_at, cam_up = camera_pose_backup_from_id.pop( + client.client_id + ) + client.camera.position = cam_position + client.camera.look_at = cam_look_at + client.camera.up_direction = cam_up + client.flush() + + # Un-hide scene nodes. + server.scene.set_global_visibility(True) + + preview_frame_slider = add_preview_frame_slider() + + # Update the # of frames. + @duration_number.on_update + @framerate_number.on_update + def _(_) -> None: + remove_preview_camera() # Will be re-added when slider is updated. + + nonlocal preview_frame_slider + old = preview_frame_slider + assert old is not None + + preview_frame_slider = add_preview_frame_slider() + if preview_frame_slider is not None: + old.remove() + else: + preview_frame_slider = old + + camera_path.framerate = framerate_number.value + camera_path.update_spline() + + # Play the camera trajectory when the play button is pressed. + @play_button.on_click + def _(_) -> None: + play_button.visible = False + pause_button.visible = True + + def play() -> None: + while not play_button.visible: + max_frame = int(framerate_number.value * duration_number.value) + if max_frame > 0: + assert preview_frame_slider is not None + preview_frame_slider.value = ( + preview_frame_slider.value + 1 + ) % max_frame + time.sleep(1.0 / framerate_number.value) + + threading.Thread(target=play).start() + + # Play the camera trajectory when the play button is pressed. + @pause_button.on_click + def _(_) -> None: + play_button.visible = True + pause_button.visible = False + + # add button for loading existing path + load_camera_path_button = server.gui.add_button( + "Load Path", icon=viser.Icon.FOLDER_OPEN, hint="Load an existing camera path." + ) + + @load_camera_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + camera_path_dir = datapath.parent + camera_path_dir.mkdir(parents=True, exist_ok=True) + preexisting_camera_paths = list(camera_path_dir.glob("*.json")) + preexisting_camera_filenames = [p.name for p in preexisting_camera_paths] + + with event.client.gui.add_modal("Load Path") as modal: + if len(preexisting_camera_filenames) == 0: + event.client.gui.add_markdown("No existing paths found") + else: + event.client.gui.add_markdown("Select existing camera path:") + camera_path_dropdown = event.client.gui.add_dropdown( + label="Camera Path", + options=[str(p) for p in preexisting_camera_filenames], + initial_value=str(preexisting_camera_filenames[0]), + ) + load_button = event.client.gui.add_button("Load") + + @load_button.on_click + def _(_) -> None: + # load the json file + json_path = datapath / camera_path_dropdown.value + with open(json_path, "r") as f: + json_data = json.load(f) + + keyframes = json_data["keyframes"] + camera_path.reset() + for i in range(len(keyframes)): + frame = keyframes[i] + pose = tf.SE3.from_matrix( + np.array(frame["matrix"]).reshape(4, 4) + ) + # apply the x rotation by 180 deg + pose = tf.SE3.from_rotation_and_translation( + pose.rotation() @ tf.SO3.from_x_radians(np.pi), + pose.translation(), + ) + + camera_path.add_camera( + Keyframe( + frame["time"], + position=pose.translation(), + wxyz=pose.rotation().wxyz, + # There are some floating point conversions between degrees and radians, so the fov and + # default_Fov values will not be exactly matched. + override_fov_enabled=abs( + frame["fov"] - json_data.get("default_fov", 0.0) + ) + > 1e-3, + override_fov_rad=frame["fov"] / 180.0 * np.pi, + aspect=frame["aspect"], + override_transition_enabled=frame.get( + "override_transition_enabled", None + ), + override_transition_sec=frame.get( + "override_transition_sec", None + ), + ) + ) + + transition_sec_number.value = json_data.get( + "default_transition_sec", 0.5 + ) + + # update the render name + camera_path_name.value = json_path.stem + camera_path.update_spline() + modal.close() + + cancel_button = event.client.gui.add_button("Cancel") + + @cancel_button.on_click + def _(_) -> None: + modal.close() + + # set the initial value to the current date-time string + now = datetime.datetime.now() + camera_path_name = server.gui.add_text( + "Camera path name", + initial_value=now.strftime("%Y-%m-%d %H:%M:%S"), + hint="Name of the render", + ) + + save_path_button = server.gui.add_button( + "Save Camera Path", + color="green", + icon=viser.Icon.FILE_EXPORT, + hint="Save the camera path to json.", + ) + + reset_up_button = server.gui.add_button( + "Reset Up Direction", + icon=viser.Icon.ARROW_BIG_UP_LINES, + color="gray", + hint="Set the up direction of the camera orbit controls to the camera's current up direction.", + ) + + @reset_up_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + event.client.camera.up_direction = tf.SO3(event.client.camera.wxyz) @ np.array( + [0.0, -1.0, 0.0] + ) + + @save_path_button.on_click + def _(event: viser.GuiEvent) -> None: + assert event.client is not None + num_frames = int(framerate_number.value * duration_number.value) + json_data = {} + # json data has the properties: + # keyframes: list of keyframes with + # matrix : flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # camera_type: string of camera type + # render_height: int + # render_width: int + # fps: int + # seconds: float + # is_cycle: bool + # smoothness_value: float + # camera_path: list of frames with properties + # camera_to_world: flattened 4x4 matrix + # fov: float in degrees + # aspect: float + # first populate the keyframes: + keyframes = [] + for keyframe, dummy in camera_path._keyframes.values(): + pose = tf.SE3.from_rotation_and_translation( + tf.SO3(keyframe.wxyz), keyframe.position + ) + keyframes.append( + { + "matrix": pose.as_matrix().flatten().tolist(), + "fov": ( + np.rad2deg(keyframe.override_fov_rad) + if keyframe.override_fov_enabled + else fov_degrees.value + ), + "aspect": keyframe.aspect, + "override_transition_enabled": keyframe.override_transition_enabled, + "override_transition_sec": keyframe.override_transition_sec, + } + ) + json_data["default_fov"] = fov_degrees.value + json_data["default_transition_sec"] = transition_sec_number.value + json_data["keyframes"] = keyframes + json_data["camera_type"] = camera_type.value.lower() + json_data["render_height"] = resolution.value[1] + json_data["render_width"] = resolution.value[0] + json_data["fps"] = framerate_number.value + json_data["seconds"] = duration_number.value + json_data["is_cycle"] = loop.value + json_data["smoothness_value"] = tension_slider.value + + def get_intrinsics(W, H, fov): + focal = 0.5 * H / np.tan(0.5 * fov) + return np.array( + [[focal, 0.0, 0.5 * W], [0.0, focal, 0.5 * H], [0.0, 0.0, 1.0]] + ) + + # now populate the camera path: + camera_path_list = [] + for i in range(num_frames): + maybe_pose_and_fov_and_time = camera_path.interpolate_pose_and_fov_rad( + i / num_frames + ) + if maybe_pose_and_fov_and_time is None: + return + pose, fov, time = maybe_pose_and_fov_and_time + H = resolution.value[1] + W = resolution.value[0] + K = get_intrinsics(W, H, fov) + # rotate the axis of the camera 180 about x axis + w2c = pose.inverse().as_matrix() + camera_path_list.append( + { + "time": time, + "w2c": w2c.flatten().tolist(), + "K": K.flatten().tolist(), + "img_wh": (W, H), + } + ) + json_data["camera_path"] = camera_path_list + + # now write the json file + out_name = camera_path_name.value + json_outfile = datapath / f"{out_name}.json" + datapath.mkdir(parents=True, exist_ok=True) + print(f"writing to {json_outfile}") + with open(json_outfile.absolute(), "w") as outfile: + json.dump(json_data, outfile) + + camera_path = CameraPath(server, duration_number) + camera_path.default_fov = fov_degrees.value / 180.0 * np.pi + camera_path.default_transition_sec = transition_sec_number.value + + return render_tab_state + + +if __name__ == "__main__": + populate_render_tab( + server=viser.ViserServer(), + datapath=Path("."), + gui_timestep_handle=None, + ) + while True: + time.sleep(10.0) diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/utils.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e5d6df8596ccf91b19e63d6b26c268336486fb3 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/utils.py @@ -0,0 +1,544 @@ +import colorsys +from typing import cast + +import cv2 +import numpy as np + +# import nvdiffrast.torch as dr +import torch +import torch.nn.functional as F +from matplotlib import colormaps +from viser import ViserServer + + +class Singleton(type): + _instances = {} + + def __call__(cls, *args, **kwargs): + if cls not in cls._instances: + cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + return cls._instances[cls] + + +class VisManager(metaclass=Singleton): + _servers = {} + + +def get_server(port: int | None = None) -> ViserServer: + manager = VisManager() + if port is None: + avail_ports = list(manager._servers.keys()) + port = avail_ports[0] if len(avail_ports) > 0 else 8890 + if port not in manager._servers: + manager._servers[port] = ViserServer(port=port, verbose=False) + return manager._servers[port] + + +def project_2d_tracks(tracks_3d_w, Ks, T_cw, return_depth=False): + """ + :param tracks_3d_w (torch.Tensor): (T, N, 3) + :param Ks (torch.Tensor): (T, 3, 3) + :param T_cw (torch.Tensor): (T, 4, 4) + :returns tracks_2d (torch.Tensor): (T, N, 2) + """ + tracks_3d_c = torch.einsum( + "tij,tnj->tni", T_cw, F.pad(tracks_3d_w, (0, 1), value=1) + )[..., :3] + tracks_3d_v = torch.einsum("tij,tnj->tni", Ks, tracks_3d_c) + if return_depth: + return ( + tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5), + tracks_3d_v[..., 2], + ) + return tracks_3d_v[..., :2] / torch.clamp(tracks_3d_v[..., 2:], min=1e-5) + + +def draw_keypoints_video( + imgs, kps, colors=None, occs=None, cmap: str = "gist_rainbow", radius: int = 3 +): + """ + :param imgs (np.ndarray): (T, H, W, 3) uint8 [0, 255] + :param kps (np.ndarray): (N, T, 2) + :param colors (np.ndarray): (N, 3) float [0, 1] + :param occ (np.ndarray): (N, T) bool + return out_frames (T, H, W, 3) + """ + if colors is None: + label = np.linspace(0, 1, kps.shape[0]) + colors = np.asarray(colormaps.get_cmap(cmap)(label))[..., :3] + out_frames = [] + for t in range(len(imgs)): + occ = occs[:, t] if occs is not None else None + vis = draw_keypoints_cv2(imgs[t], kps[:, t], colors, occ, radius=radius) + out_frames.append(vis) + return out_frames + + +def draw_keypoints_cv2(img, kps, colors=None, occs=None, radius=3): + """ + :param img (H, W, 3) + :param kps (N, 2) + :param occs (N) + :param colors (N, 3) from 0 to 1 + """ + out_img = img.copy() + kps = kps.round().astype("int").tolist() + if colors is not None: + colors = (255 * colors).astype("int").tolist() + for n in range(len(kps)): + kp = kps[n] + color = colors[n] if colors is not None else (255, 0, 0) + thickness = -1 if occs is None or occs[n] == 0 else 1 + out_img = cv2.circle(out_img, kp, radius, color, thickness, cv2.LINE_AA) + return out_img + + +def draw_tracks_2d( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 2, + track_line_width: int = 1, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + # (H, W, 3). + img_np = (img.cpu().numpy() * 255.0).astype(np.uint8) + # (P, N, 2). + tracks_2d_np = tracks_2d.cpu().numpy() + + num_tracks, num_frames = tracks_2d_np.shape[:2] + + canvas = img_np.copy() + for i in range(num_frames - 1): + alpha = max(1 - 0.9 * ((num_frames - 1 - i) / (num_frames * 0.99)), 0.1) + img_curr = canvas.copy() + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + color_alpha = 1 + hsv = colorsys.rgb_to_hsv(color[0], color[1], color[2]) + color = colorsys.hsv_to_rgb(hsv[0], hsv[1] * color_alpha, hsv[2]) + pt1 = tracks_2d_np[j, i] + pt2 = tracks_2d_np[j, i + 1] + p1 = (int(round(pt1[0])), int(round(pt1[1]))) + p2 = (int(round(pt2[0])), int(round(pt2[1]))) + img_curr = cv2.line( + img_curr, + p1, + p2, + color, + thickness=track_line_width, + lineType=cv2.LINE_AA, + ) + canvas = cv2.addWeighted(img_curr, alpha, canvas, 1 - alpha, 0) + + for j in range(num_tracks): + color = tuple(np.array(cmap(j / max(1, float(num_tracks - 1)))[:3]) * 255) + pt = tracks_2d_np[j, -1] + pt = (int(round(pt[0])), int(round(pt[1]))) + canvas = cv2.circle( + canvas, + pt, + track_point_size, + color, + thickness=-1, + lineType=cv2.LINE_AA, + ) + + return canvas + + +def generate_line_verts_faces(starts, ends, line_width): + """ + Args: + starts: (P, N, 2). + ends: (P, N, 2). + line_width: int. + + Returns: + verts: (P * N * 4, 2). + faces: (P * N * 2, 3). + """ + P, N, _ = starts.shape + + directions = F.normalize(ends - starts, dim=-1) + deltas = ( + torch.cat([-directions[..., 1:], directions[..., :1]], dim=-1) + * line_width + / 2.0 + ) + v0 = starts + deltas + v1 = starts - deltas + v2 = ends + deltas + v3 = ends - deltas + verts = torch.stack([v0, v1, v2, v3], dim=-2) + verts = verts.reshape(-1, 2) + + faces = [] + for p in range(P): + for n in range(N): + base_index = p * N * 4 + n * 4 + # Two triangles per rectangle: (0, 1, 2) and (2, 1, 3) + faces.append([base_index, base_index + 1, base_index + 2]) + faces.append([base_index + 2, base_index + 1, base_index + 3]) + faces = torch.as_tensor(faces, device=starts.device) + + return verts, faces + + +def generate_point_verts_faces(points, point_size, num_segments=10): + """ + Args: + points: (P, 2). + point_size: int. + num_segments: int. + + Returns: + verts: (P * (num_segments + 1), 2). + faces: (P * num_segments, 3). + """ + P, _ = points.shape + + angles = torch.linspace(0, 2 * torch.pi, num_segments + 1, device=points.device)[ + ..., :-1 + ] + unit_circle = torch.stack([torch.cos(angles), torch.sin(angles)], dim=1) + scaled_circles = (point_size / 2.0) * unit_circle + scaled_circles = scaled_circles[None].repeat(P, 1, 1) + verts = points[:, None] + scaled_circles + verts = torch.cat([verts, points[:, None]], dim=1) + verts = verts.reshape(-1, 2) + + faces = F.pad( + torch.as_tensor( + [[i, (i + 1) % num_segments] for i in range(num_segments)], + device=points.device, + ), + (0, 1), + value=num_segments, + ) + faces = faces[None, :] + torch.arange(P, device=points.device)[:, None, None] * ( + num_segments + 1 + ) + faces = faces.reshape(-1, 3) + + return verts, faces + + +def pixel_to_verts_clip(pixels, img_wh, z: float | torch.Tensor = 0.0, w=1.0): + verts_clip = pixels / pixels.new_tensor(img_wh) * 2.0 - 1.0 + w = torch.full_like(verts_clip[..., :1], w) + verts_clip = torch.cat([verts_clip, z * w, w], dim=-1) + return verts_clip + + +def draw_tracks_2d_th( + img: torch.Tensor, + tracks_2d: torch.Tensor, + track_point_size: int = 5, + track_point_segments: int = 16, + track_line_width: int = 2, + cmap_name: str = "gist_rainbow", +): + cmap = colormaps.get_cmap(cmap_name) + CTX = dr.RasterizeCudaContext() + + W, H = img.shape[1], img.shape[0] + if W % 8 != 0 or H % 8 != 0: + # Make sure img is divisible by 8. + img = F.pad( + img, + ( + 0, + 0, + 0, + 8 - W % 8 if W % 8 != 0 else 0, + 0, + 8 - H % 8 if H % 8 != 0 else 0, + ), + value=0.0, + ) + num_tracks, num_frames = tracks_2d.shape[:2] + + track_colors = torch.tensor( + [cmap(j / max(1, float(num_tracks - 1)))[:3] for j in range(num_tracks)], + device=img.device, + ).float() + + # Generate line verts. + verts_l, faces_l = generate_line_verts_faces( + tracks_2d[:, :-1], tracks_2d[:, 1:], track_line_width + ) + # Generate point verts. + verts_p, faces_p = generate_point_verts_faces( + tracks_2d[:, -1], track_point_size, track_point_segments + ) + + verts = torch.cat([verts_l, verts_p], dim=0) + faces = torch.cat([faces_l, faces_p + len(verts_l)], dim=0) + vert_colors = torch.cat( + [ + ( + track_colors[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 3) + ), + ( + track_colors[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 3) + ), + ], + dim=0, + ) + track_zs = torch.linspace(0.0, 1.0, num_tracks, device=img.device)[:, None] + vert_zs = torch.cat( + [ + ( + track_zs[:, None] + .repeat_interleave(4 * (num_frames - 1), dim=1) + .reshape(-1, 1) + ), + ( + track_zs[:, None] + .repeat_interleave(track_point_segments + 1, dim=1) + .reshape(-1, 1) + ), + ], + dim=0, + ) + track_alphas = torch.linspace( + max(0.1, 1.0 - (num_frames - 1) * 0.1), 1.0, num_frames, device=img.device + ) + vert_alphas = torch.cat( + [ + ( + track_alphas[None, :-1, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(4, dim=-2) + .reshape(-1, 1) + ), + ( + track_alphas[None, -1:, None] + .repeat_interleave(num_tracks, dim=0) + .repeat_interleave(track_point_segments + 1, dim=-2) + .reshape(-1, 1) + ), + ], + dim=0, + ) + + # Small trick to always render one track in front of the other. + verts_clip = pixel_to_verts_clip(verts, (img.shape[1], img.shape[0]), vert_zs) + faces_int32 = faces.to(torch.int32) + + rast, _ = cast( + tuple, + dr.rasterize(CTX, verts_clip[None], faces_int32, (img.shape[0], img.shape[1])), + ) + rgba = cast( + torch.Tensor, + dr.interpolate( + torch.cat([vert_colors, vert_alphas], dim=-1).contiguous(), + rast, + faces_int32, + ), + )[0] + rgba = cast(torch.Tensor, dr.antialias(rgba, rast, verts_clip, faces_int32))[ + 0 + ].clamp(0, 1) + # Compose. + color = rgba[..., :-1] * rgba[..., -1:] + (1.0 - rgba[..., -1:]) * img + + # Unpad. + color = color[:H, :W] + + return (color.cpu().numpy() * 255.0).astype(np.uint8) + + +def make_video_divisble( + video: torch.Tensor | np.ndarray, block_size=16 +) -> torch.Tensor | np.ndarray: + H, W = video.shape[1:3] + H_new = H - H % block_size + W_new = W - W % block_size + return video[:, :H_new, :W_new] + + +def apply_float_colormap(img: torch.Tensor, colormap: str = "turbo") -> torch.Tensor: + """Convert single channel to a color img. + + Args: + img (torch.Tensor): (..., 1) float32 single channel image. + colormap (str): Colormap for img. + + Returns: + (..., 3) colored img with colors in [0, 1]. + """ + img = torch.nan_to_num(img, 0) + if colormap == "gray": + return img.repeat(1, 1, 3) + img_long = (img * 255).long() + img_long_min = torch.min(img_long) + img_long_max = torch.max(img_long) + assert img_long_min >= 0, f"the min value is {img_long_min}" + assert img_long_max <= 255, f"the max value is {img_long_max}" + return torch.tensor( + colormaps[colormap].colors, # type: ignore + device=img.device, + )[img_long[..., 0]] + + +def apply_depth_colormap( + depth: torch.Tensor, + acc: torch.Tensor | None = None, + near_plane: float | None = None, + far_plane: float | None = None, +) -> torch.Tensor: + """Converts a depth image to color for easier analysis. + + Args: + depth (torch.Tensor): (..., 1) float32 depth. + acc (torch.Tensor | None): (..., 1) optional accumulation mask. + near_plane: Closest depth to consider. If None, use min image value. + far_plane: Furthest depth to consider. If None, use max image value. + + Returns: + (..., 3) colored depth image with colors in [0, 1]. + """ + near_plane = near_plane or float(torch.min(depth)) + far_plane = far_plane or float(torch.max(depth)) + depth = (depth - near_plane) / (far_plane - near_plane + 1e-10) + depth = torch.clip(depth, 0.0, 1.0) + img = apply_float_colormap(depth, colormap="turbo") + if acc is not None: + img = img * acc + (1.0 - acc) + return img + + +def float2uint8(x): + return (255.0 * x).astype(np.uint8) + + +def uint82float(img): + return np.ascontiguousarray(img) / 255.0 + + +def drawMatches( + img1, + img2, + kp1, + kp2, + num_vis=200, + center=None, + idx_vis=None, + radius=2, + seed=1234, + mask=None, +): + num_pts = len(kp1) + if idx_vis is None: + if num_vis < num_pts: + rng = np.random.RandomState(seed) + idx_vis = rng.choice(num_pts, num_vis, replace=False) + else: + idx_vis = np.arange(num_pts) + + kp1_vis = kp1[idx_vis] + kp2_vis = kp2[idx_vis] + + h1, w1 = img1.shape[:2] + h2, w2 = img2.shape[:2] + + kp1_vis[:, 0] = np.clip(kp1_vis[:, 0], a_min=0, a_max=w1 - 1) + kp1_vis[:, 1] = np.clip(kp1_vis[:, 1], a_min=0, a_max=h1 - 1) + + kp2_vis[:, 0] = np.clip(kp2_vis[:, 0], a_min=0, a_max=w2 - 1) + kp2_vis[:, 1] = np.clip(kp2_vis[:, 1], a_min=0, a_max=h2 - 1) + + img1 = float2uint8(img1) + img2 = float2uint8(img2) + + if center is None: + center = np.median(kp1, axis=0) + + set_max = range(128) + colors = {m: i for i, m in enumerate(set_max)} + hsv = colormaps.get_cmap("hsv") + colors = { + m: (255 * np.array(hsv(i / float(len(colors))))[:3][::-1]).astype(np.int32) + for m, i in colors.items() + } + + if mask is not None: + ind = np.argsort(mask)[::-1] + kp1_vis = kp1_vis[ind] + kp2_vis = kp2_vis[ind] + mask = mask[ind] + + for i, (pt1, pt2) in enumerate(zip(kp1_vis, kp2_vis)): + # random_color = tuple(np.random.randint(low=0, high=255, size=(3,)).tolist()) + coord_angle = np.arctan2(pt1[1] - center[1], pt1[0] - center[0]) + corr_color = np.int32(64 * coord_angle / np.pi) % 128 + color = tuple(colors[corr_color].tolist()) + + if ( + (pt1[0] <= w1 - 1) + and (pt1[0] >= 0) + and (pt1[1] <= h1 - 1) + and (pt1[1] >= 0) + ): + img1 = cv2.circle( + img1, (int(pt1[0]), int(pt1[1])), radius, color, -1, cv2.LINE_AA + ) + if ( + (pt2[0] <= w2 - 1) + and (pt2[0] >= 0) + and (pt2[1] <= h2 - 1) + and (pt2[1] >= 0) + ): + if mask is not None and mask[i]: + continue + # img2 = cv2.drawMarker(img2, (int(pt2[0]), int(pt2[1])), color, markerType=cv2.MARKER_CROSS, + # markerSize=int(5*radius), thickness=int(radius/2), line_type=cv2.LINE_AA) + else: + img2 = cv2.circle( + img2, (int(pt2[0]), int(pt2[1])), radius, color, -1, cv2.LINE_AA + ) + + out = np.concatenate([img1, img2], axis=1) + return out + + +def plot_correspondences( + rgbs, kpts, query_id=0, masks=None, num_vis=1000000, radius=3, seed=1234 +): + num_rgbs = len(rgbs) + rng = np.random.RandomState(seed) + permutation = rng.permutation(kpts.shape[1]) + kpts = kpts[:, permutation, :][:, :num_vis] + if masks is not None: + masks = masks[:, permutation][:, :num_vis] + + rgbq = rgbs[query_id] # [h, w, 3] + kptsq = kpts[query_id] # [n, 2] + + frames = [] + for i in range(num_rgbs): + rgbi = rgbs[i] + kptsi = kpts[i] + if masks is not None: + maski = masks[i] + else: + maski = None + frame = drawMatches( + rgbq, + rgbi, + kptsq, + kptsi, + mask=maski, + num_vis=num_vis, + radius=radius, + seed=seed, + ) + frames.append(frame) + return frames diff --git a/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/viewer.py b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/viewer.py new file mode 100644 index 0000000000000000000000000000000000000000..3f786110e1bd0dafcf71fc05c3048a4b8b2dc5cc --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/flow3d/vis/viewer.py @@ -0,0 +1,69 @@ +from pathlib import Path +from typing import Callable, Literal, Optional, Tuple, Union + +import numpy as np +from jaxtyping import Float32, UInt8 +from nerfview import CameraState, Viewer +from viser import Icon, ViserServer + +from flow3d.vis.playback_panel import add_gui_playback_group +from flow3d.vis.render_panel import populate_render_tab + + +class DynamicViewer(Viewer): + def __init__( + self, + server: ViserServer, + render_fn: Callable[ + [CameraState, Tuple[int, int]], + Union[ + UInt8[np.ndarray, "H W 3"], + Tuple[UInt8[np.ndarray, "H W 3"], Optional[Float32[np.ndarray, "H W"]]], + ], + ], + num_frames: int, + work_dir: str, + mode: Literal["rendering", "training"] = "rendering", + ): + self.num_frames = num_frames + self.work_dir = Path(work_dir) + super().__init__(server, render_fn, mode) + + def _define_guis(self): + super()._define_guis() + server = self.server + self._time_folder = server.gui.add_folder("Time") + with self._time_folder: + self._playback_guis = add_gui_playback_group( + server, + num_frames=self.num_frames, + initial_fps=15.0, + ) + self._playback_guis[0].on_update(self.rerender) + self._canonical_checkbox = server.gui.add_checkbox("Canonical", False) + self._canonical_checkbox.on_update(self.rerender) + + _cached_playback_disabled = [] + + def _toggle_gui_playing(event): + if event.target.value: + nonlocal _cached_playback_disabled + _cached_playback_disabled = [ + gui.disabled for gui in self._playback_guis + ] + target_disabled = [True] * len(self._playback_guis) + else: + target_disabled = _cached_playback_disabled + for gui, disabled in zip(self._playback_guis, target_disabled): + gui.disabled = disabled + + self._canonical_checkbox.on_update(_toggle_gui_playing) + + self._render_track_checkbox = server.gui.add_checkbox("Render tracks", False) + self._render_track_checkbox.on_update(self.rerender) + + tabs = server.gui.add_tab_group() + with tabs.add_tab("Render", Icon.CAMERA): + self.render_tab_state = populate_render_tab( + server, Path(self.work_dir) / "camera_paths", self._playback_guis[0] + ) diff --git a/Davis_out/swing/code/2024-10-26-020013/scripts/batch_eval_ours_iphone_gcp.sh b/Davis_out/swing/code/2024-10-26-020013/scripts/batch_eval_ours_iphone_gcp.sh new file mode 100644 index 0000000000000000000000000000000000000000..94a7f379bb36b5e4b9c4be70d8905b3563df3c93 --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/scripts/batch_eval_ours_iphone_gcp.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +EXPNAME=$1 + +seq_names=("apple" "backpack" "block" "creeper" "handwavy" "haru-sit" "mochi-high-five" "paper-windmill" "pillow" "spin" "sriracha-tree" "teddy") +out_dir="/mnt/out/$EXPNAME" +for seq_name in "${seq_names[@]}"; do + seq_dir="$out_dir/$seq_name" + mkdir -p $seq_dir + gsutil -mq cp -r "gs://xcloud-shared/qianqianwang/flow3d/ours/iphone/$EXPNAME/${seq_name}/results" $seq_dir + done + +python scripts/evaluate_iphone.py --data_dir /home/qianqianwang_google_com/datasets/iphone/dycheck --result_dir /mnt/out/$EXPNAME \ No newline at end of file diff --git a/Davis_out/swing/code/2024-10-26-020013/scripts/evaluate_iphone.py b/Davis_out/swing/code/2024-10-26-020013/scripts/evaluate_iphone.py new file mode 100644 index 0000000000000000000000000000000000000000..f7649f2c61629da5ad193f7484abdac243eb5dde --- /dev/null +++ b/Davis_out/swing/code/2024-10-26-020013/scripts/evaluate_iphone.py @@ -0,0 +1,447 @@ +import argparse +import json +import os.path as osp +from glob import glob +from itertools import product + +import cv2 +import imageio.v3 as iio +import numpy as np +import roma +import torch +from tqdm import tqdm + +from flow3d.data.colmap import get_colmap_camera_params +from flow3d.metrics import mLPIPS, mPSNR, mSSIM +from flow3d.transforms import rt_to_mat4, solve_procrustes + +parser = argparse.ArgumentParser() +parser.add_argument( + "--data_dir", + type=str, + help="Path to the data directory that contains all the sequences.", +) +parser.add_argument( + "--result_dir", + type=str, + help="Path to the result directory that contains the results." + "for batch evaluation, result_dir should contain subdirectories for each sequence. (result_dir/seq_name/results)" + "for single sequence evaluation, result_dir should contain results directly (result_dir/results)", +) +parser.add_argument( + "--seq_names", + type=str, + nargs="+", + default=[ + "apple", + "backpack", + "block", + "creeper", + "handwavy", + "haru-sit", + "mochi-high-five", + "paper-windmill", + "pillow", + "spin", + "sriracha-tree", + "teddy", + ], + help="Sequence names to evaluate.", +) +args = parser.parse_args() + + +def load_data_dict(data_dir, train_names, val_names): + val_imgs = np.array( + [iio.imread(osp.join(data_dir, "rgb/1x", f"{name}.png")) for name in val_names] + ) + val_covisibles = np.array( + [ + iio.imread( + osp.join( + data_dir, "flow3d_preprocessed/covisible/1x/val/", f"{name}.png" + ) + ) + for name in tqdm(val_names, desc="Loading val covisibles") + ] + ) + train_depths = np.array( + [ + np.load(osp.join(data_dir, "depth/1x", f"{name}.npy"))[..., 0] + for name in train_names + ] + ) + train_Ks, train_w2cs = get_colmap_camera_params( + osp.join(data_dir, "flow3d_preprocessed/colmap/sparse/"), + [name + ".png" for name in train_names], + ) + train_Ks = train_Ks[:, :3, :3] + scale = np.load(osp.join(data_dir, "flow3d_preprocessed/colmap/scale.npy")).item() + train_c2ws = np.linalg.inv(train_w2cs) + train_c2ws[:, :3, -1] *= scale + train_w2cs = np.linalg.inv(train_c2ws) + keypoint_paths = sorted(glob(osp.join(data_dir, "keypoint/2x/train/0_*.json"))) + keypoints_2d = [] + for keypoint_path in keypoint_paths: + with open(keypoint_path) as f: + keypoints_2d.append(json.load(f)) + keypoints_2d = np.array(keypoints_2d) + keypoints_2d[..., :2] *= 2.0 + time_ids = np.array( + [int(osp.basename(p).split("_")[1].split(".")[0]) for p in keypoint_paths] + ) + time_pairs = np.array(list(product(time_ids, repeat=2))) + index_pairs = np.array(list(product(range(len(time_ids)), repeat=2))) + keypoints_3d = [] + for i, kps_2d in zip(time_ids, keypoints_2d): + K = train_Ks[i] + w2c = train_w2cs[i] + depth = train_depths[i] + is_kp_visible = kps_2d[:, 2] == 1 + is_depth_valid = ( + cv2.remap( + (depth != 0).astype(np.float32), + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + == 1 + ) + kp_depths = cv2.remap( + depth, # type: ignore + kps_2d[None, :, :2].astype(np.float32), + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + kps_3d = ( + np.einsum( + "ij,pj->pi", + np.linalg.inv(K), + np.pad(kps_2d[:, :2], ((0, 0), (0, 1)), constant_values=1), + ) + * kp_depths[:, None] + ) + kps_3d = np.einsum( + "ij,pj->pi", + np.linalg.inv(w2c)[:3], + np.pad(kps_3d, ((0, 0), (0, 1)), constant_values=1), + ) + kps_3d = np.concatenate( + [kps_3d, (is_kp_visible & is_depth_valid)[:, None]], axis=1 + ) + kps_3d[kps_3d[:, -1] != 1] = 0.0 + keypoints_3d.append(kps_3d) + keypoints_3d = np.array(keypoints_3d) + return { + "val_imgs": val_imgs, + "val_covisibles": val_covisibles, + "train_depths": train_depths, + "train_Ks": train_Ks, + "train_w2cs": train_w2cs, + "keypoints_2d": keypoints_2d, + "keypoints_3d": keypoints_3d, + "time_ids": time_ids, + "time_pairs": time_pairs, + "index_pairs": index_pairs, + } + + +def load_result_dict(result_dir, val_names): + try: + pred_val_imgs = np.array( + [ + iio.imread(osp.join(result_dir, "rgb", f"{name}.png")) + for name in val_names + ] + ) + except: + pred_val_imgs = None + try: + keypoints_dict = np.load( + osp.join(result_dir, "keypoints.npz"), allow_pickle=True + ) + if len(keypoints_dict) == 1 and "arr_0" in keypoints_dict: + keypoints_dict = keypoints_dict["arr_0"].item() + pred_keypoint_Ks = keypoints_dict["Ks"] + pred_keypoint_w2cs = keypoints_dict["w2cs"] + pred_keypoints_3d = keypoints_dict["pred_keypoints_3d"] + pred_train_depths = keypoints_dict["pred_train_depths"] + except: + print( + "No keypoints.npz found, make sure that it's the method itself cannot produce keypoints." + ) + keypoints_dict = {} + pred_keypoint_Ks = None + pred_keypoint_w2cs = None + pred_keypoints_3d = None + pred_train_depths = None + + if "visibilities" in list(keypoints_dict.keys()): + pred_visibilities = keypoints_dict["visibilities"] + else: + pred_visibilities = None + + return { + "pred_val_imgs": pred_val_imgs, + "pred_train_depths": pred_train_depths, + "pred_keypoint_Ks": pred_keypoint_Ks, + "pred_keypoint_w2cs": pred_keypoint_w2cs, + "pred_keypoints_3d": pred_keypoints_3d, + "pred_visibilities": pred_visibilities, + } + + +def evaluate_3d_tracking(data_dict, result_dict): + train_Ks = data_dict["train_Ks"] + train_w2cs = data_dict["train_w2cs"] + keypoints_3d = data_dict["keypoints_3d"] + time_ids = data_dict["time_ids"] + time_pairs = data_dict["time_pairs"] + index_pairs = data_dict["index_pairs"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"] + if not np.allclose(train_Ks[time_ids], pred_keypoint_Ks): + print("Inconsistent camera intrinsics.") + print(train_Ks[time_ids][0], pred_keypoint_Ks[0]) + keypoint_w2cs = train_w2cs[time_ids] + q, t, s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0] + R = roma.unitquat_to_rotmat(q.roll(-1, dims=-1)) + pred_keypoints_3d = np.einsum( + "ij,...j->...i", + rt_to_mat4(R, t, s).numpy().astype(np.float64), + np.pad(pred_keypoints_3d, ((0, 0), (0, 0), (0, 1)), constant_values=1), + ) + pred_keypoints_3d = pred_keypoints_3d[..., :3] / pred_keypoints_3d[..., 3:] + # Compute 3D tracking metrics. + pair_keypoints_3d = keypoints_3d[index_pairs] + is_covisible = (pair_keypoints_3d[:, :, :, -1] == 1).all(axis=1) + target_keypoints_3d = pair_keypoints_3d[:, 1, :, :3] + epes = [] + for i in range(len(time_pairs)): + epes.append( + np.linalg.norm( + target_keypoints_3d[i][is_covisible[i]] + - pred_keypoints_3d[i][is_covisible[i]], + axis=-1, + ) + ) + epe = np.mean( + [frame_epes.mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_10cm = np.mean( + [(frame_epes < 0.1).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + pck_3d_5cm = np.mean( + [(frame_epes < 0.05).mean() for frame_epes in epes if len(frame_epes) > 0] + ).item() + print(f"3D tracking EPE: {epe:.4f}") + print(f"3D tracking PCK (10cm): {pck_3d_10cm:.4f}") + print(f"3D tracking PCK (5cm): {pck_3d_5cm:.4f}") + print("-----------------------------") + return epe, pck_3d_10cm, pck_3d_5cm + + +def project(Ks, w2cs, pts): + """ + Args: + Ks: (N, 3, 3) camera intrinsics. + w2cs: (N, 4, 4) camera extrinsics. + pts: (N, N, M, 3) 3D points. + """ + N = Ks.shape[0] + pts = pts.swapaxes(0, 1).reshape(N, -1, 3) + + pts_homogeneous = np.concatenate([pts, np.ones_like(pts[..., -1:])], axis=-1) + + # Apply world-to-camera transformation + pts_homogeneous = np.matmul(w2cs[:, :3], pts_homogeneous.swapaxes(1, 2)).swapaxes( + 1, 2 + ) + # Project to image plane using intrinsic parameters + projected_pts = np.matmul(Ks, pts_homogeneous.swapaxes(1, 2)).swapaxes(1, 2) + + depths = projected_pts[..., 2:3] + # Normalize homogeneous coordinates + projected_pts = projected_pts[..., :2] / np.clip(depths, a_min=1e-6, a_max=None) + projected_pts = projected_pts.reshape(N, N, -1, 2).swapaxes(0, 1) + depths = depths.reshape(N, N, -1).swapaxes(0, 1) + return projected_pts, depths + + +def evaluate_2d_tracking(data_dict, result_dict): + train_w2cs = data_dict["train_w2cs"] + keypoints_2d = data_dict["keypoints_2d"] + visibilities = keypoints_2d[..., -1].astype(np.bool_) + time_ids = data_dict["time_ids"] + num_frames = len(time_ids) + num_pts = keypoints_2d.shape[1] + pred_train_depths = result_dict["pred_train_depths"] + pred_keypoint_Ks = result_dict["pred_keypoint_Ks"] + pred_keypoint_w2cs = result_dict["pred_keypoint_w2cs"] + pred_keypoints_3d = result_dict["pred_keypoints_3d"].reshape( + num_frames, -1, num_pts, 3 + ) + keypoint_w2cs = train_w2cs[time_ids] + s = solve_procrustes( + torch.from_numpy(np.linalg.inv(pred_keypoint_w2cs)[:, :3, -1]).to( + torch.float32 + ), + torch.from_numpy(np.linalg.inv(keypoint_w2cs)[:, :3, -1]).to(torch.float32), + )[0][-1].item() + + target_points = keypoints_2d[None].repeat(num_frames, axis=0)[..., :2] + target_visibilities = visibilities[None].repeat(num_frames, axis=0) + + pred_points, pred_depths = project( + pred_keypoint_Ks, pred_keypoint_w2cs, pred_keypoints_3d + ) + if result_dict["pred_visibilities"] is not None: + pred_visibilities = result_dict["pred_visibilities"].reshape( + num_frames, -1, num_pts + ) + else: + rendered_depths = [] + for i, points in zip( + data_dict["index_pairs"][:, -1], + pred_points.reshape(-1, pred_points.shape[2], 2), + ): + rendered_depths.append( + cv2.remap( + pred_train_depths[i].astype(np.float32), + points[None].astype(np.float32), # type: ignore + None, # type: ignore + cv2.INTER_LINEAR, + borderMode=cv2.BORDER_CONSTANT, + )[0] + ) + rendered_depths = np.array(rendered_depths).reshape(num_frames, -1, num_pts) + pred_visibilities = (np.abs(rendered_depths - pred_depths) * s) < 0.05 + + one_hot_eye = np.eye(target_points.shape[0])[..., None].repeat(num_pts, axis=-1) + evaluation_points = one_hot_eye == 0 + for i in range(num_frames): + evaluation_points[i, :, ~visibilities[i]] = False + occ_acc = np.sum( + np.equal(pred_visibilities, target_visibilities) & evaluation_points + ) / np.sum(evaluation_points) + all_frac_within = [] + all_jaccard = [] + + for thresh in [4, 8, 16, 32, 64]: + within_dist = np.sum( + np.square(pred_points - target_points), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, target_visibilities) + count_correct = np.sum(is_correct & evaluation_points) + count_visible_points = np.sum(target_visibilities & evaluation_points) + frac_correct = count_correct / count_visible_points + all_frac_within.append(frac_correct) + + true_positives = np.sum(is_correct & pred_visibilities & evaluation_points) + gt_positives = np.sum(target_visibilities & evaluation_points) + false_positives = (~target_visibilities) & pred_visibilities + false_positives = false_positives | ((~within_dist) & pred_visibilities) + false_positives = np.sum(false_positives & evaluation_points) + jaccard = true_positives / (gt_positives + false_positives) + all_jaccard.append(jaccard) + AJ = np.mean(all_jaccard) + APCK = np.mean(all_frac_within) + + print(f"2D tracking AJ: {AJ:.4f}") + print(f"2D tracking avg PCK: {APCK:.4f}") + print(f"2D tracking occlusion accuracy: {occ_acc:.4f}") + print("-----------------------------") + return AJ, APCK, occ_acc + + +def evaluate_nv(data_dict, result_dict): + device = "cuda" + psnr_metric = mPSNR().to(device) + ssim_metric = mSSIM().to(device) + lpips_metric = mLPIPS().to(device) + + val_imgs = torch.from_numpy(data_dict["val_imgs"])[..., :3].to(device) + val_covisibles = torch.from_numpy(data_dict["val_covisibles"]).to(device) + pred_val_imgs = torch.from_numpy(result_dict["pred_val_imgs"]).to(device) + + for i in range(len(val_imgs)): + val_img = val_imgs[i] / 255.0 + pred_val_img = pred_val_imgs[i] / 255.0 + val_covisible = val_covisibles[i] / 255.0 + psnr_metric.update(val_img, pred_val_img, val_covisible) + ssim_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + lpips_metric.update(val_img[None], pred_val_img[None], val_covisible[None]) + mpsnr = psnr_metric.compute().item() + mssim = ssim_metric.compute().item() + mlpips = lpips_metric.compute().item() + print(f"NV mPSNR: {mpsnr:.4f}") + print(f"NV mSSIM: {mssim:.4f}") + print(f"NV mLPIPS: {mlpips:.4f}") + return mpsnr, mssim, mlpips + + +if __name__ == "__main__": + seq_names = args.seq_names + + epe_all, pck_3d_10cm_all, pck_3d_5cm_all = [], [], [] + AJ_all, APCK_all, occ_acc_all = [], [], [] + mpsnr_all, mssim_all, mlpips_all = [], [], [] + + for seq_name in seq_names: + print("=========================================") + print(f"Evaluating {seq_name}") + print("=========================================") + data_dir = osp.join(args.data_dir, seq_name) + if not osp.exists(data_dir): + data_dir = args.data_dir + if not osp.exists(data_dir): + raise ValueError(f"Data directory {data_dir} not found.") + result_dir = osp.join(args.result_dir, seq_name, "results/") + if not osp.exists(result_dir): + result_dir = osp.join(args.result_dir, "results/") + if not osp.exists(result_dir): + raise ValueError(f"Result directory {result_dir} not found.") + + with open(osp.join(data_dir, "splits/train.json")) as f: + train_names = json.load(f)["frame_names"] + with open(osp.join(data_dir, "splits/val.json")) as f: + val_names = json.load(f)["frame_names"] + + data_dict = load_data_dict(data_dir, train_names, val_names) + result_dict = load_result_dict(result_dir, val_names) + if result_dict["pred_keypoints_3d"] is not None: + epe, pck_3d_10cm, pck_3d_5cm = evaluate_3d_tracking(data_dict, result_dict) + AJ, APCK, occ_acc = evaluate_2d_tracking(data_dict, result_dict) + epe_all.append(epe) + pck_3d_10cm_all.append(pck_3d_10cm) + pck_3d_5cm_all.append(pck_3d_5cm) + AJ_all.append(AJ) + APCK_all.append(APCK) + occ_acc_all.append(occ_acc) + if len(data_dict["val_imgs"]) > 0: + if result_dict["pred_val_imgs"] is None: + print("No NV results found.") + continue + mpsnr, mssim, mlpips = evaluate_nv(data_dict, result_dict) + mpsnr_all.append(mpsnr) + mssim_all.append(mssim) + mlpips_all.append(mlpips) + + print(f"mean 3D tracking EPE: {np.mean(epe_all):.4f}") + print(f"mean 3D tracking PCK (10cm): {np.mean(pck_3d_10cm_all):.4f}") + print(f"mean 3D tracking PCK (5cm): {np.mean(pck_3d_5cm_all):.4f}") + print(f"mean 2D tracking AJ: {np.mean(AJ_all):.4f}") + print(f"mean 2D tracking avg PCK: {np.mean(APCK_all):.4f}") + print(f"mean 2D tracking occlusion accuracy: {np.mean(occ_acc_all):.4f}") + print(f"mean NV mPSNR: {np.mean(mpsnr_all):.4f}") + print(f"mean NV mSSIM: {np.mean(mssim_all):.4f}") + print(f"mean NV mLPIPS: {np.mean(mlpips_all):.4f}") diff --git a/Davis_out/swing/events.out.tfevents.1729879346.cvsv00-140.1480844.0 b/Davis_out/swing/events.out.tfevents.1729879346.cvsv00-140.1480844.0 new file mode 100644 index 0000000000000000000000000000000000000000..54998e7b59292ec66e3dc358a9946f0af0adee33 --- /dev/null +++ b/Davis_out/swing/events.out.tfevents.1729879346.cvsv00-140.1480844.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb6297add773fcea4a8e75f32046e35a3537222c454c6e900614c74ef6b9f436 +size 3865878