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+

LightGlue ⚡️
Local Feature Matching at Light Speed

+

+ Philipp Lindenberger + · + Paul-Edouard Sarlin + · + Marc Pollefeys +

+

+

ICCV 2023

+ Paper | + Colab | + Poster | + Train your own! +

+ +

+

+ example +
+ LightGlue is a deep neural network that matches sparse local features across image pairs.
An adaptive mechanism makes it fast for easy pairs (top) and reduces the computational complexity for difficult ones (bottom). +

+ +## + +This repository hosts the inference code of LightGlue, a lightweight feature matcher with high accuracy and blazing fast inference. It takes as input a set of keypoints and descriptors for each image and returns the indices of corresponding points. The architecture is based on adaptive pruning techniques, in both network width and depth - [check out the paper for more details](https://arxiv.org/pdf/2306.13643.pdf). + +We release pretrained weights of LightGlue with [SuperPoint](https://arxiv.org/abs/1712.07629), [DISK](https://arxiv.org/abs/2006.13566), [ALIKED](https://arxiv.org/abs/2304.03608) and [SIFT](https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf) local features. +The training and evaluation code can be found in our library [glue-factory](https://github.com/cvg/glue-factory/). + +## Installation and demo [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cvg/LightGlue/blob/main/demo.ipynb) + +Install this repo using pip: + +```bash +git clone https://github.com/cvg/LightGlue.git && cd LightGlue +python -m pip install -e . +``` + +We provide a [demo notebook](demo.ipynb) which shows how to perform feature extraction and matching on an image pair. + +Here is a minimal script to match two images: + +```python +from lightglue import LightGlue, SuperPoint, DISK, SIFT, ALIKED, DoGHardNet +from lightglue.utils import load_image, rbd + +# SuperPoint+LightGlue +extractor = SuperPoint(max_num_keypoints=2048).eval().cuda() # load the extractor +matcher = LightGlue(features='superpoint').eval().cuda() # load the matcher + +# or DISK+LightGlue, ALIKED+LightGlue or SIFT+LightGlue +extractor = DISK(max_num_keypoints=2048).eval().cuda() # load the extractor +matcher = LightGlue(features='disk').eval().cuda() # load the matcher + +# load each image as a torch.Tensor on GPU with shape (3,H,W), normalized in [0,1] +image0 = load_image('path/to/image_0.jpg').cuda() +image1 = load_image('path/to/image_1.jpg').cuda() + +# extract local features +feats0 = extractor.extract(image0) # auto-resize the image, disable with resize=None +feats1 = extractor.extract(image1) + +# match the features +matches01 = matcher({'image0': feats0, 'image1': feats1}) +feats0, feats1, matches01 = [rbd(x) for x in [feats0, feats1, matches01]] # remove batch dimension +matches = matches01['matches'] # indices with shape (K,2) +points0 = feats0['keypoints'][matches[..., 0]] # coordinates in image #0, shape (K,2) +points1 = feats1['keypoints'][matches[..., 1]] # coordinates in image #1, shape (K,2) +``` + +We also provide a convenience method to match a pair of images: + +```python +from lightglue import match_pair +feats0, feats1, matches01 = match_pair(extractor, matcher, image0, image1) +``` + +## + +

+ Logo +
+ LightGlue can adjust its depth (number of layers) and width (number of keypoints) per image pair, with a marginal impact on accuracy. +

+ +## Advanced configuration + +
+[Detail of all parameters - click to expand] + +- ```n_layers```: Number of stacked self+cross attention layers. Reduce this value for faster inference at the cost of accuracy (continuous red line in the plot above). Default: 9 (all layers). +- ```flash```: Enable FlashAttention. Significantly increases the speed and reduces the memory consumption without any impact on accuracy. Default: True (LightGlue automatically detects if FlashAttention is available). +- ```mp```: Enable mixed precision inference. Default: False (off) +- ```depth_confidence```: Controls the early stopping. A lower values stops more often at earlier layers. Default: 0.95, disable with -1. +- ```width_confidence```: Controls the iterative point pruning. A lower value prunes more points earlier. Default: 0.99, disable with -1. +- ```filter_threshold```: Match confidence. Increase this value to obtain less, but stronger matches. Default: 0.1 + +
+ +The default values give a good trade-off between speed and accuracy. To maximize the accuracy, use all keypoints and disable the adaptive mechanisms: +```python +extractor = SuperPoint(max_num_keypoints=None) +matcher = LightGlue(features='superpoint', depth_confidence=-1, width_confidence=-1) +``` + +To increase the speed with a small drop of accuracy, decrease the number of keypoints and lower the adaptive thresholds: +```python +extractor = SuperPoint(max_num_keypoints=1024) +matcher = LightGlue(features='superpoint', depth_confidence=0.9, width_confidence=0.95) +``` + +The maximum speed is obtained with a combination of: +- [FlashAttention](https://arxiv.org/abs/2205.14135): automatically used when ```torch >= 2.0``` or if [installed from source](https://github.com/HazyResearch/flash-attention#installation-and-features). +- PyTorch compilation, available when ```torch >= 2.0```: +```python +matcher = matcher.eval().cuda() +matcher.compile(mode='reduce-overhead') +``` +For inputs with fewer than 1536 keypoints (determined experimentally), this compiles LightGlue but disables point pruning (large overhead). For larger input sizes, it automatically falls backs to eager mode with point pruning. Adaptive depths is supported for any input size. + +## Benchmark + + +

+ Logo +
+ Benchmark results on GPU (RTX 3080). With compilation and adaptivity, LightGlue runs at 150 FPS @ 1024 keypoints and 50 FPS @ 4096 keypoints per image. This is a 4-10x speedup over SuperGlue. +

+ +

+ Logo +
+ Benchmark results on CPU (Intel i7 10700K). LightGlue runs at 20 FPS @ 512 keypoints. +

+ +Obtain the same plots for your setup using our [benchmark script](benchmark.py): +``` +python benchmark.py [--device cuda] [--add_superglue] [--num_keypoints 512 1024 2048 4096] [--compile] +``` + +
+[Performance tip - click to expand] + +Note: **Point pruning** introduces an overhead that sometimes outweighs its benefits. +Point pruning is thus enabled only when the there are more than N keypoints in an image, where N is hardware-dependent. +We provide defaults optimized for current hardware (RTX 30xx GPUs). +We suggest running the benchmark script and adjusting the thresholds for your hardware by updating `LightGlue.pruning_keypoint_thresholds['cuda']`. + +
+ +## Training and evaluation + +With [Glue Factory](https://github.com/cvg/glue-factory), you can train LightGlue with your own local features, on your own dataset! +You can also evaluate it and other baselines on standard benchmarks like HPatches and MegaDepth. + +## Other links +- [hloc - the visual localization toolbox](https://github.com/cvg/Hierarchical-Localization/): run LightGlue for Structure-from-Motion and visual localization. +- [LightGlue-ONNX](https://github.com/fabio-sim/LightGlue-ONNX): export LightGlue to the Open Neural Network Exchange (ONNX) format with support for TensorRT and OpenVINO. +- [Image Matching WebUI](https://github.com/Vincentqyw/image-matching-webui): a web GUI to easily compare different matchers, including LightGlue. +- [kornia](https://kornia.readthedocs.io) now exposes LightGlue via the interfaces [`LightGlue`](https://kornia.readthedocs.io/en/latest/feature.html#kornia.feature.LightGlue) and [`LightGlueMatcher`](https://kornia.readthedocs.io/en/latest/feature.html#kornia.feature.LightGlueMatcher). + +## BibTeX citation +If you use any ideas from the paper or code from this repo, please consider citing: + +```txt +@inproceedings{lindenberger2023lightglue, + author = {Philipp Lindenberger and + Paul-Edouard Sarlin and + Marc Pollefeys}, + title = {{LightGlue: Local Feature Matching at Light Speed}}, + booktitle = {ICCV}, + year = {2023} +} +``` + + +## License +The pre-trained weights of LightGlue and the code provided in this repository are released under the [Apache-2.0 license](./LICENSE). [DISK](https://github.com/cvlab-epfl/disk) follows this license as well but SuperPoint follows [a different, restrictive license](https://github.com/magicleap/SuperPointPretrainedNetwork/blob/master/LICENSE) (this includes its pre-trained weights and its [inference file](./lightglue/superpoint.py)). [ALIKED](https://github.com/Shiaoming/ALIKED) was published under a BSD-3-Clause license. diff --git a/LightGlue/assets/DSC_0410.JPG b/LightGlue/assets/DSC_0410.JPG new file mode 100644 index 0000000000000000000000000000000000000000..117569e91296c1f9647978443fb77092e2fe64d9 Binary files /dev/null and b/LightGlue/assets/DSC_0410.JPG differ diff --git a/LightGlue/assets/DSC_0411.JPG b/LightGlue/assets/DSC_0411.JPG new file mode 100644 index 0000000000000000000000000000000000000000..dbfaad445c64c4d6ff8572543de354df50277603 Binary files /dev/null and b/LightGlue/assets/DSC_0411.JPG differ diff --git a/LightGlue/assets/architecture.svg b/LightGlue/assets/architecture.svg new file mode 100644 index 0000000000000000000000000000000000000000..df15d83690d20f28fef4a33d7a2442105cb786f6 --- /dev/null +++ b/LightGlue/assets/architecture.svg @@ -0,0 +1,769 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/LightGlue/benchmark.py b/LightGlue/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..b160f3a37bf64d2a42884ea29f165fb3f325b9cf --- /dev/null +++ b/LightGlue/benchmark.py @@ -0,0 +1,255 @@ +# Benchmark script for LightGlue on real images +import argparse +import time +from collections import defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch._dynamo + +from lightglue import LightGlue, SuperPoint +from lightglue.utils import load_image + +torch.set_grad_enabled(False) + + +def measure(matcher, data, device="cuda", r=100): + timings = np.zeros((r, 1)) + if device.type == "cuda": + starter = torch.cuda.Event(enable_timing=True) + ender = torch.cuda.Event(enable_timing=True) + # warmup + for _ in range(10): + _ = matcher(data) + # measurements + with torch.no_grad(): + for rep in range(r): + if device.type == "cuda": + starter.record() + _ = matcher(data) + ender.record() + # sync gpu + torch.cuda.synchronize() + curr_time = starter.elapsed_time(ender) + else: + start = time.perf_counter() + _ = matcher(data) + curr_time = (time.perf_counter() - start) * 1e3 + timings[rep] = curr_time + mean_syn = np.sum(timings) / r + std_syn = np.std(timings) + return {"mean": mean_syn, "std": std_syn} + + +def print_as_table(d, title, cnames): + print() + header = f"{title:30} " + " ".join([f"{x:>7}" for x in cnames]) + print(header) + print("-" * len(header)) + for k, l in d.items(): + print(f"{k:30}", " ".join([f"{x:>7.1f}" for x in l])) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Benchmark script for LightGlue") + parser.add_argument( + "--device", + choices=["auto", "cuda", "cpu", "mps"], + default="auto", + help="device to benchmark on", + ) + parser.add_argument("--compile", action="store_true", help="Compile LightGlue runs") + parser.add_argument( + "--no_flash", action="store_true", help="disable FlashAttention" + ) + parser.add_argument( + "--no_prune_thresholds", + action="store_true", + help="disable pruning thresholds (i.e. always do pruning)", + ) + parser.add_argument( + "--add_superglue", + action="store_true", + help="add SuperGlue to the benchmark (requires hloc)", + ) + parser.add_argument( + "--measure", default="time", choices=["time", "log-time", "throughput"] + ) + parser.add_argument( + "--repeat", "--r", type=int, default=100, help="repetitions of measurements" + ) + parser.add_argument( + "--num_keypoints", + nargs="+", + type=int, + default=[256, 512, 1024, 2048, 4096], + help="number of keypoints (list separated by spaces)", + ) + parser.add_argument( + "--matmul_precision", default="highest", choices=["highest", "high", "medium"] + ) + parser.add_argument( + "--save", default=None, type=str, help="path where figure should be saved" + ) + args = parser.parse_intermixed_args() + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if args.device != "auto": + device = torch.device(args.device) + + print("Running benchmark on device:", device) + + images = Path("assets") + inputs = { + "easy": ( + load_image(images / "DSC_0411.JPG"), + load_image(images / "DSC_0410.JPG"), + ), + "difficult": ( + load_image(images / "sacre_coeur1.jpg"), + load_image(images / "sacre_coeur2.jpg"), + ), + } + + configs = { + "LightGlue-full": { + "depth_confidence": -1, + "width_confidence": -1, + }, + # 'LG-prune': { + # 'width_confidence': -1, + # }, + # 'LG-depth': { + # 'depth_confidence': -1, + # }, + "LightGlue-adaptive": {}, + } + + if args.compile: + configs = {**configs, **{k + "-compile": v for k, v in configs.items()}} + + sg_configs = { + # 'SuperGlue': {}, + "SuperGlue-fast": {"sinkhorn_iterations": 5} + } + + torch.set_float32_matmul_precision(args.matmul_precision) + + results = {k: defaultdict(list) for k, v in inputs.items()} + + extractor = SuperPoint(max_num_keypoints=None, detection_threshold=-1) + extractor = extractor.eval().to(device) + figsize = (len(inputs) * 4.5, 4.5) + fig, axes = plt.subplots(1, len(inputs), sharey=True, figsize=figsize) + axes = axes if len(inputs) > 1 else [axes] + fig.canvas.manager.set_window_title(f"LightGlue benchmark ({device.type})") + + for title, ax in zip(inputs.keys(), axes): + ax.set_xscale("log", base=2) + bases = [2**x for x in range(7, 16)] + ax.set_xticks(bases, bases) + ax.grid(which="major") + if args.measure == "log-time": + ax.set_yscale("log") + yticks = [10**x for x in range(6)] + ax.set_yticks(yticks, yticks) + mpos = [10**x * i for x in range(6) for i in range(2, 10)] + mlabel = [ + 10**x * i if i in [2, 5] else None + for x in range(6) + for i in range(2, 10) + ] + ax.set_yticks(mpos, mlabel, minor=True) + ax.grid(which="minor", linewidth=0.2) + ax.set_title(title) + + ax.set_xlabel("# keypoints") + if args.measure == "throughput": + ax.set_ylabel("Throughput [pairs/s]") + else: + ax.set_ylabel("Latency [ms]") + + for name, conf in configs.items(): + print("Run benchmark for:", name) + torch.cuda.empty_cache() + matcher = LightGlue(features="superpoint", flash=not args.no_flash, **conf) + if args.no_prune_thresholds: + matcher.pruning_keypoint_thresholds = { + k: -1 for k in matcher.pruning_keypoint_thresholds + } + matcher = matcher.eval().to(device) + if name.endswith("compile"): + import torch._dynamo + + torch._dynamo.reset() # avoid buffer overflow + matcher.compile() + for pair_name, ax in zip(inputs.keys(), axes): + image0, image1 = [x.to(device) for x in inputs[pair_name]] + runtimes = [] + for num_kpts in args.num_keypoints: + extractor.conf.max_num_keypoints = num_kpts + feats0 = extractor.extract(image0) + feats1 = extractor.extract(image1) + runtime = measure( + matcher, + {"image0": feats0, "image1": feats1}, + device=device, + r=args.repeat, + )["mean"] + results[pair_name][name].append( + 1000 / runtime if args.measure == "throughput" else runtime + ) + ax.plot( + args.num_keypoints, results[pair_name][name], label=name, marker="o" + ) + del matcher, feats0, feats1 + + if args.add_superglue: + from hloc.matchers.superglue import SuperGlue + + for name, conf in sg_configs.items(): + print("Run benchmark for:", name) + matcher = SuperGlue(conf) + matcher = matcher.eval().to(device) + for pair_name, ax in zip(inputs.keys(), axes): + image0, image1 = [x.to(device) for x in inputs[pair_name]] + runtimes = [] + for num_kpts in args.num_keypoints: + extractor.conf.max_num_keypoints = num_kpts + feats0 = extractor.extract(image0) + feats1 = extractor.extract(image1) + data = { + "image0": image0[None], + "image1": image1[None], + **{k + "0": v for k, v in feats0.items()}, + **{k + "1": v for k, v in feats1.items()}, + } + data["scores0"] = data["keypoint_scores0"] + data["scores1"] = data["keypoint_scores1"] + data["descriptors0"] = ( + data["descriptors0"].transpose(-1, -2).contiguous() + ) + data["descriptors1"] = ( + data["descriptors1"].transpose(-1, -2).contiguous() + ) + runtime = measure(matcher, data, device=device, r=args.repeat)[ + "mean" + ] + results[pair_name][name].append( + 1000 / runtime if args.measure == "throughput" else runtime + ) + ax.plot( + args.num_keypoints, results[pair_name][name], label=name, marker="o" + ) + del matcher, data, image0, image1, feats0, feats1 + + for name, runtimes in results.items(): + print_as_table(runtimes, name, args.num_keypoints) + + axes[0].legend() + fig.tight_layout() + if args.save: + plt.savefig(args.save, dpi=fig.dpi) + plt.show() diff --git a/LightGlue/demo.ipynb b/LightGlue/demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1e8709167420bbbf059b40adbbdc188ed27781da --- /dev/null +++ b/LightGlue/demo.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LightGlue Demo\n", + "In this notebook we match two pairs of images using LightGlue with early stopping and point pruning." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we are on colab: this clones the repo and installs the dependencies\n", + "from pathlib import Path\n", + "\n", + "if Path.cwd().name != \"LightGlue\":\n", + " !git clone --quiet https://github.com/cvg/LightGlue/\n", + " %cd LightGlue\n", + " !pip install --progress-bar off --quiet -e .\n", + "\n", + "from lightglue import LightGlue, SuperPoint, DISK\n", + "from lightglue.utils import load_image, rbd\n", + "from lightglue import viz2d\n", + "import torch\n", + "\n", + "torch.set_grad_enabled(False)\n", + "images = Path(\"assets\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load extractor and matcher module\n", + "In this example we use SuperPoint features combined with LightGlue." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded SuperPoint model\n", + "Loaded LightGlue model\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # 'mps', 'cpu'\n", + "\n", + "extractor = SuperPoint(max_num_keypoints=2048).eval().to(device) # load the extractor\n", + "matcher = LightGlue(features=\"superpoint\").eval().to(device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Easy example\n", + "The top image shows the matches, while the bottom image shows the point pruning across layers. In this case, LightGlue prunes a few points with occlusions, but is able to stop the context aggregation after 4/9 layers." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image0 = load_image(images / \"DSC_0411.JPG\")\n", + "image1 = load_image(images / \"DSC_0410.JPG\")\n", + "\n", + "feats0 = extractor.extract(image0.to(device))\n", + "feats1 = extractor.extract(image1.to(device))\n", + "matches01 = matcher({\"image0\": feats0, \"image1\": feats1})\n", + "feats0, feats1, matches01 = [\n", + " rbd(x) for x in [feats0, feats1, matches01]\n", + "] # remove batch dimension\n", + "\n", + "kpts0, kpts1, matches = feats0[\"keypoints\"], feats1[\"keypoints\"], matches01[\"matches\"]\n", + "m_kpts0, m_kpts1 = kpts0[matches[..., 0]], kpts1[matches[..., 1]]\n", + "\n", + "axes = viz2d.plot_images([image0, image1])\n", + "viz2d.plot_matches(m_kpts0, m_kpts1, color=\"lime\", lw=0.2)\n", + "viz2d.add_text(0, f'Stop after {matches01[\"stop\"]} layers', fs=20)\n", + "\n", + "kpc0, kpc1 = viz2d.cm_prune(matches01[\"prune0\"]), viz2d.cm_prune(matches01[\"prune1\"])\n", + "viz2d.plot_images([image0, image1])\n", + "viz2d.plot_keypoints([kpts0, kpts1], colors=[kpc0, kpc1], ps=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Difficult example\n", + "For pairs with significant viewpoint- and illumination changes, LightGlue can exclude a lot of points early in the matching process (red points), which significantly reduces the inference time." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image0 = load_image(images / \"sacre_coeur1.jpg\")\n", + "image1 = load_image(images / \"sacre_coeur2.jpg\")\n", + "\n", + "feats0 = extractor.extract(image0.to(device))\n", + "feats1 = extractor.extract(image1.to(device))\n", + "matches01 = matcher({\"image0\": feats0, \"image1\": feats1})\n", + "feats0, feats1, matches01 = [\n", + " rbd(x) for x in [feats0, feats1, matches01]\n", + "] # remove batch dimension\n", + "\n", + "kpts0, kpts1, matches = feats0[\"keypoints\"], feats1[\"keypoints\"], matches01[\"matches\"]\n", + "m_kpts0, m_kpts1 = kpts0[matches[..., 0]], kpts1[matches[..., 1]]\n", + "\n", + "axes = viz2d.plot_images([image0, image1])\n", + "viz2d.plot_matches(m_kpts0, m_kpts1, color=\"lime\", lw=0.2)\n", + "viz2d.add_text(0, f'Stop after {matches01[\"stop\"]} layers')\n", + "\n", + "kpc0, kpc1 = viz2d.cm_prune(matches01[\"prune0\"]), viz2d.cm_prune(matches01[\"prune1\"])\n", + "viz2d.plot_images([image0, image1])\n", + "viz2d.plot_keypoints([kpts0, kpts1], colors=[kpc0, kpc1], ps=6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/LightGlue/lightglue/__init__.py b/LightGlue/lightglue/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b84d285cf2a29e3b17c8c2c052a45f856dcf89c0 --- /dev/null +++ b/LightGlue/lightglue/__init__.py @@ -0,0 +1,7 @@ +from .aliked import ALIKED # noqa +from .disk import DISK # noqa +from .dog_hardnet import DoGHardNet # noqa +from .lightglue import LightGlue # noqa +from .sift import SIFT # noqa +from .superpoint import SuperPoint # noqa +from .utils import match_pair # noqa diff --git a/LightGlue/lightglue/aliked.py b/LightGlue/lightglue/aliked.py new file mode 100644 index 0000000000000000000000000000000000000000..1161e1fc2d0cce32583031229e8ad4bb84f9a40c --- /dev/null +++ b/LightGlue/lightglue/aliked.py @@ -0,0 +1,758 @@ +# BSD 3-Clause License + +# Copyright (c) 2022, Zhao Xiaoming +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: + +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. + +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# Authors: +# Xiaoming Zhao, Xingming Wu, Weihai Chen, Peter C.Y. Chen, Qingsong Xu, and Zhengguo Li +# Code from https://github.com/Shiaoming/ALIKED + +from typing import Callable, Optional + +import torch +import torch.nn.functional as F +import torchvision +from kornia.color import grayscale_to_rgb +from torch import nn +from torch.nn.modules.utils import _pair +from torchvision.models import resnet + +from .utils import Extractor + + +def get_patches( + tensor: torch.Tensor, required_corners: torch.Tensor, ps: int +) -> torch.Tensor: + c, h, w = tensor.shape + corner = (required_corners - ps / 2 + 1).long() + corner[:, 0] = corner[:, 0].clamp(min=0, max=w - 1 - ps) + corner[:, 1] = corner[:, 1].clamp(min=0, max=h - 1 - ps) + offset = torch.arange(0, ps) + + kw = {"indexing": "ij"} if torch.__version__ >= "1.10" else {} + x, y = torch.meshgrid(offset, offset, **kw) + patches = torch.stack((x, y)).permute(2, 1, 0).unsqueeze(2) + patches = patches.to(corner) + corner[None, None] + pts = patches.reshape(-1, 2) + sampled = tensor.permute(1, 2, 0)[tuple(pts.T)[::-1]] + sampled = sampled.reshape(ps, ps, -1, c) + assert sampled.shape[:3] == patches.shape[:3] + return sampled.permute(2, 3, 0, 1) + + +def simple_nms(scores: torch.Tensor, nms_radius: int): + """Fast Non-maximum suppression to remove nearby points""" + + zeros = torch.zeros_like(scores) + max_mask = scores == torch.nn.functional.max_pool2d( + scores, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius + ) + + for _ in range(2): + supp_mask = ( + torch.nn.functional.max_pool2d( + max_mask.float(), + kernel_size=nms_radius * 2 + 1, + stride=1, + padding=nms_radius, + ) + > 0 + ) + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == torch.nn.functional.max_pool2d( + supp_scores, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius + ) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +class DKD(nn.Module): + def __init__( + self, + radius: int = 2, + top_k: int = 0, + scores_th: float = 0.2, + n_limit: int = 20000, + ): + """ + Args: + radius: soft detection radius, kernel size is (2 * radius + 1) + top_k: top_k > 0: return top k keypoints + scores_th: top_k <= 0 threshold mode: + scores_th > 0: return keypoints with scores>scores_th + else: return keypoints with scores > scores.mean() + n_limit: max number of keypoint in threshold mode + """ + super().__init__() + self.radius = radius + self.top_k = top_k + self.scores_th = scores_th + self.n_limit = n_limit + self.kernel_size = 2 * self.radius + 1 + self.temperature = 0.1 # tuned temperature + self.unfold = nn.Unfold(kernel_size=self.kernel_size, padding=self.radius) + # local xy grid + x = torch.linspace(-self.radius, self.radius, self.kernel_size) + # (kernel_size*kernel_size) x 2 : (w,h) + kw = {"indexing": "ij"} if torch.__version__ >= "1.10" else {} + self.hw_grid = ( + torch.stack(torch.meshgrid([x, x], **kw)).view(2, -1).t()[:, [1, 0]] + ) + + def forward( + self, + scores_map: torch.Tensor, + sub_pixel: bool = True, + image_size: Optional[torch.Tensor] = None, + ): + """ + :param scores_map: Bx1xHxW + :param descriptor_map: BxCxHxW + :param sub_pixel: whether to use sub-pixel keypoint detection + :return: kpts: list[Nx2,...]; kptscores: list[N,....] normalised position: -1~1 + """ + b, c, h, w = scores_map.shape + scores_nograd = scores_map.detach() + nms_scores = simple_nms(scores_nograd, self.radius) + + # remove border + nms_scores[:, :, : self.radius, :] = 0 + nms_scores[:, :, :, : self.radius] = 0 + if image_size is not None: + for i in range(scores_map.shape[0]): + w, h = image_size[i].long() + nms_scores[i, :, h.item() - self.radius :, :] = 0 + nms_scores[i, :, :, w.item() - self.radius :] = 0 + else: + nms_scores[:, :, -self.radius :, :] = 0 + nms_scores[:, :, :, -self.radius :] = 0 + + # detect keypoints without grad + if self.top_k > 0: + topk = torch.topk(nms_scores.view(b, -1), self.top_k) + indices_keypoints = [topk.indices[i] for i in range(b)] # B x top_k + else: + if self.scores_th > 0: + masks = nms_scores > self.scores_th + if masks.sum() == 0: + th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th + masks = nms_scores > th.reshape(b, 1, 1, 1) + else: + th = scores_nograd.reshape(b, -1).mean(dim=1) # th = self.scores_th + masks = nms_scores > th.reshape(b, 1, 1, 1) + masks = masks.reshape(b, -1) + + indices_keypoints = [] # list, B x (any size) + scores_view = scores_nograd.reshape(b, -1) + for mask, scores in zip(masks, scores_view): + indices = mask.nonzero()[:, 0] + if len(indices) > self.n_limit: + kpts_sc = scores[indices] + sort_idx = kpts_sc.sort(descending=True)[1] + sel_idx = sort_idx[: self.n_limit] + indices = indices[sel_idx] + indices_keypoints.append(indices) + + wh = torch.tensor([w - 1, h - 1], device=scores_nograd.device) + + keypoints = [] + scoredispersitys = [] + kptscores = [] + if sub_pixel: + # detect soft keypoints with grad backpropagation + patches = self.unfold(scores_map) # B x (kernel**2) x (H*W) + self.hw_grid = self.hw_grid.to(scores_map) # to device + for b_idx in range(b): + patch = patches[b_idx].t() # (H*W) x (kernel**2) + indices_kpt = indices_keypoints[ + b_idx + ] # one dimension vector, say its size is M + patch_scores = patch[indices_kpt] # M x (kernel**2) + keypoints_xy_nms = torch.stack( + [indices_kpt % w, torch.div(indices_kpt, w, rounding_mode="trunc")], + dim=1, + ) # Mx2 + + # max is detached to prevent undesired backprop loops in the graph + max_v = patch_scores.max(dim=1).values.detach()[:, None] + x_exp = ( + (patch_scores - max_v) / self.temperature + ).exp() # M * (kernel**2), in [0, 1] + + # \frac{ \sum{(i,j) \times \exp(x/T)} }{ \sum{\exp(x/T)} } + xy_residual = ( + x_exp @ self.hw_grid / x_exp.sum(dim=1)[:, None] + ) # Soft-argmax, Mx2 + + hw_grid_dist2 = ( + torch.norm( + (self.hw_grid[None, :, :] - xy_residual[:, None, :]) + / self.radius, + dim=-1, + ) + ** 2 + ) + scoredispersity = (x_exp * hw_grid_dist2).sum(dim=1) / x_exp.sum(dim=1) + + # compute result keypoints + keypoints_xy = keypoints_xy_nms + xy_residual + keypoints_xy = keypoints_xy / wh * 2 - 1 # (w,h) -> (-1~1,-1~1) + + kptscore = torch.nn.functional.grid_sample( + scores_map[b_idx].unsqueeze(0), + keypoints_xy.view(1, 1, -1, 2), + mode="bilinear", + align_corners=True, + )[ + 0, 0, 0, : + ] # CxN + + keypoints.append(keypoints_xy) + scoredispersitys.append(scoredispersity) + kptscores.append(kptscore) + else: + for b_idx in range(b): + indices_kpt = indices_keypoints[ + b_idx + ] # one dimension vector, say its size is M + # To avoid warning: UserWarning: __floordiv__ is deprecated + keypoints_xy_nms = torch.stack( + [indices_kpt % w, torch.div(indices_kpt, w, rounding_mode="trunc")], + dim=1, + ) # Mx2 + keypoints_xy = keypoints_xy_nms / wh * 2 - 1 # (w,h) -> (-1~1,-1~1) + kptscore = torch.nn.functional.grid_sample( + scores_map[b_idx].unsqueeze(0), + keypoints_xy.view(1, 1, -1, 2), + mode="bilinear", + align_corners=True, + )[ + 0, 0, 0, : + ] # CxN + keypoints.append(keypoints_xy) + scoredispersitys.append(kptscore) # for jit.script compatability + kptscores.append(kptscore) + + return keypoints, scoredispersitys, kptscores + + +class InputPadder(object): + """Pads images such that dimensions are divisible by 8""" + + def __init__(self, h: int, w: int, divis_by: int = 8): + self.ht = h + self.wd = w + pad_ht = (((self.ht // divis_by) + 1) * divis_by - self.ht) % divis_by + pad_wd = (((self.wd // divis_by) + 1) * divis_by - self.wd) % divis_by + self._pad = [ + pad_wd // 2, + pad_wd - pad_wd // 2, + pad_ht // 2, + pad_ht - pad_ht // 2, + ] + + def pad(self, x: torch.Tensor): + assert x.ndim == 4 + return F.pad(x, self._pad, mode="replicate") + + def unpad(self, x: torch.Tensor): + assert x.ndim == 4 + ht = x.shape[-2] + wd = x.shape[-1] + c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]] + return x[..., c[0] : c[1], c[2] : c[3]] + + +class DeformableConv2d(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False, + mask=False, + ): + super(DeformableConv2d, self).__init__() + + self.padding = padding + self.mask = mask + + self.channel_num = ( + 3 * kernel_size * kernel_size if mask else 2 * kernel_size * kernel_size + ) + self.offset_conv = nn.Conv2d( + in_channels, + self.channel_num, + kernel_size=kernel_size, + stride=stride, + padding=self.padding, + bias=True, + ) + + self.regular_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=self.padding, + bias=bias, + ) + + def forward(self, x): + h, w = x.shape[2:] + max_offset = max(h, w) / 4.0 + + out = self.offset_conv(x) + if self.mask: + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + else: + offset = out + mask = None + offset = offset.clamp(-max_offset, max_offset) + x = torchvision.ops.deform_conv2d( + input=x, + offset=offset, + weight=self.regular_conv.weight, + bias=self.regular_conv.bias, + padding=self.padding, + mask=mask, + ) + return x + + +def get_conv( + inplanes, + planes, + kernel_size=3, + stride=1, + padding=1, + bias=False, + conv_type="conv", + mask=False, +): + if conv_type == "conv": + conv = nn.Conv2d( + inplanes, + planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=bias, + ) + elif conv_type == "dcn": + conv = DeformableConv2d( + inplanes, + planes, + kernel_size=kernel_size, + stride=stride, + padding=_pair(padding), + bias=bias, + mask=mask, + ) + else: + raise TypeError + return conv + + +class ConvBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + gate: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + conv_type: str = "conv", + mask: bool = False, + ): + super().__init__() + if gate is None: + self.gate = nn.ReLU(inplace=True) + else: + self.gate = gate + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self.conv1 = get_conv( + in_channels, out_channels, kernel_size=3, conv_type=conv_type, mask=mask + ) + self.bn1 = norm_layer(out_channels) + self.conv2 = get_conv( + out_channels, out_channels, kernel_size=3, conv_type=conv_type, mask=mask + ) + self.bn2 = norm_layer(out_channels) + + def forward(self, x): + x = self.gate(self.bn1(self.conv1(x))) # B x in_channels x H x W + x = self.gate(self.bn2(self.conv2(x))) # B x out_channels x H x W + return x + + +# modified based on torchvision\models\resnet.py#27->BasicBlock +class ResBlock(nn.Module): + expansion: int = 1 + + def __init__( + self, + inplanes: int, + planes: int, + stride: int = 1, + downsample: Optional[nn.Module] = None, + groups: int = 1, + base_width: int = 64, + dilation: int = 1, + gate: Optional[Callable[..., nn.Module]] = None, + norm_layer: Optional[Callable[..., nn.Module]] = None, + conv_type: str = "conv", + mask: bool = False, + ) -> None: + super(ResBlock, self).__init__() + if gate is None: + self.gate = nn.ReLU(inplace=True) + else: + self.gate = gate + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("ResBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in ResBlock") + # Both self.conv1 and self.downsample layers + # downsample the input when stride != 1 + self.conv1 = get_conv( + inplanes, planes, kernel_size=3, conv_type=conv_type, mask=mask + ) + self.bn1 = norm_layer(planes) + self.conv2 = get_conv( + planes, planes, kernel_size=3, conv_type=conv_type, mask=mask + ) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x: torch.Tensor) -> torch.Tensor: + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.gate(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.gate(out) + + return out + + +class SDDH(nn.Module): + def __init__( + self, + dims: int, + kernel_size: int = 3, + n_pos: int = 8, + gate=nn.ReLU(), + conv2D=False, + mask=False, + ): + super(SDDH, self).__init__() + self.kernel_size = kernel_size + self.n_pos = n_pos + self.conv2D = conv2D + self.mask = mask + + self.get_patches_func = get_patches + + # estimate offsets + self.channel_num = 3 * n_pos if mask else 2 * n_pos + self.offset_conv = nn.Sequential( + nn.Conv2d( + dims, + self.channel_num, + kernel_size=kernel_size, + stride=1, + padding=0, + bias=True, + ), + gate, + nn.Conv2d( + self.channel_num, + self.channel_num, + kernel_size=1, + stride=1, + padding=0, + bias=True, + ), + ) + + # sampled feature conv + self.sf_conv = nn.Conv2d( + dims, dims, kernel_size=1, stride=1, padding=0, bias=False + ) + + # convM + if not conv2D: + # deformable desc weights + agg_weights = torch.nn.Parameter(torch.rand(n_pos, dims, dims)) + self.register_parameter("agg_weights", agg_weights) + else: + self.convM = nn.Conv2d( + dims * n_pos, dims, kernel_size=1, stride=1, padding=0, bias=False + ) + + def forward(self, x, keypoints): + # x: [B,C,H,W] + # keypoints: list, [[N_kpts,2], ...] (w,h) + b, c, h, w = x.shape + wh = torch.tensor([[w - 1, h - 1]], device=x.device) + max_offset = max(h, w) / 4.0 + + offsets = [] + descriptors = [] + # get offsets for each keypoint + for ib in range(b): + xi, kptsi = x[ib], keypoints[ib] + kptsi_wh = (kptsi / 2 + 0.5) * wh + N_kpts = len(kptsi) + + if self.kernel_size > 1: + patch = self.get_patches_func( + xi, kptsi_wh.long(), self.kernel_size + ) # [N_kpts, C, K, K] + else: + kptsi_wh_long = kptsi_wh.long() + patch = ( + xi[:, kptsi_wh_long[:, 1], kptsi_wh_long[:, 0]] + .permute(1, 0) + .reshape(N_kpts, c, 1, 1) + ) + + offset = self.offset_conv(patch).clamp( + -max_offset, max_offset + ) # [N_kpts, 2*n_pos, 1, 1] + if self.mask: + offset = ( + offset[:, :, 0, 0].view(N_kpts, 3, self.n_pos).permute(0, 2, 1) + ) # [N_kpts, n_pos, 3] + offset = offset[:, :, :-1] # [N_kpts, n_pos, 2] + mask_weight = torch.sigmoid(offset[:, :, -1]) # [N_kpts, n_pos] + else: + offset = ( + offset[:, :, 0, 0].view(N_kpts, 2, self.n_pos).permute(0, 2, 1) + ) # [N_kpts, n_pos, 2] + offsets.append(offset) # for visualization + + # get sample positions + pos = kptsi_wh.unsqueeze(1) + offset # [N_kpts, n_pos, 2] + pos = 2.0 * pos / wh[None] - 1 + pos = pos.reshape(1, N_kpts * self.n_pos, 1, 2) + + # sample features + features = F.grid_sample( + xi.unsqueeze(0), pos, mode="bilinear", align_corners=True + ) # [1,C,(N_kpts*n_pos),1] + features = features.reshape(c, N_kpts, self.n_pos, 1).permute( + 1, 0, 2, 3 + ) # [N_kpts, C, n_pos, 1] + if self.mask: + features = torch.einsum("ncpo,np->ncpo", features, mask_weight) + + features = torch.selu_(self.sf_conv(features)).squeeze( + -1 + ) # [N_kpts, C, n_pos] + # convM + if not self.conv2D: + descs = torch.einsum( + "ncp,pcd->nd", features, self.agg_weights + ) # [N_kpts, C] + else: + features = features.reshape(N_kpts, -1)[ + :, :, None, None + ] # [N_kpts, C*n_pos, 1, 1] + descs = self.convM(features).squeeze() # [N_kpts, C] + + # normalize + descs = F.normalize(descs, p=2.0, dim=1) + descriptors.append(descs) + + return descriptors, offsets + + +class ALIKED(Extractor): + default_conf = { + "model_name": "aliked-n16", + "max_num_keypoints": -1, + "detection_threshold": 0.2, + "nms_radius": 2, + } + + checkpoint_url = "https://github.com/Shiaoming/ALIKED/raw/main/models/{}.pth" + + n_limit_max = 20000 + + # c1, c2, c3, c4, dim, K, M + cfgs = { + "aliked-t16": [8, 16, 32, 64, 64, 3, 16], + "aliked-n16": [16, 32, 64, 128, 128, 3, 16], + "aliked-n16rot": [16, 32, 64, 128, 128, 3, 16], + "aliked-n32": [16, 32, 64, 128, 128, 3, 32], + } + preprocess_conf = { + "resize": 1024, + } + + required_data_keys = ["image"] + + def __init__(self, **conf): + super().__init__(**conf) # Update with default configuration. + conf = self.conf + c1, c2, c3, c4, dim, K, M = self.cfgs[conf.model_name] + conv_types = ["conv", "conv", "dcn", "dcn"] + conv2D = False + mask = False + + # build model + self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2) + self.pool4 = nn.AvgPool2d(kernel_size=4, stride=4) + self.norm = nn.BatchNorm2d + self.gate = nn.SELU(inplace=True) + self.block1 = ConvBlock(3, c1, self.gate, self.norm, conv_type=conv_types[0]) + self.block2 = self.get_resblock(c1, c2, conv_types[1], mask) + self.block3 = self.get_resblock(c2, c3, conv_types[2], mask) + self.block4 = self.get_resblock(c3, c4, conv_types[3], mask) + + self.conv1 = resnet.conv1x1(c1, dim // 4) + self.conv2 = resnet.conv1x1(c2, dim // 4) + self.conv3 = resnet.conv1x1(c3, dim // 4) + self.conv4 = resnet.conv1x1(dim, dim // 4) + self.upsample2 = nn.Upsample( + scale_factor=2, mode="bilinear", align_corners=True + ) + self.upsample4 = nn.Upsample( + scale_factor=4, mode="bilinear", align_corners=True + ) + self.upsample8 = nn.Upsample( + scale_factor=8, mode="bilinear", align_corners=True + ) + self.upsample32 = nn.Upsample( + scale_factor=32, mode="bilinear", align_corners=True + ) + self.score_head = nn.Sequential( + resnet.conv1x1(dim, 8), + self.gate, + resnet.conv3x3(8, 4), + self.gate, + resnet.conv3x3(4, 4), + self.gate, + resnet.conv3x3(4, 1), + ) + self.desc_head = SDDH(dim, K, M, gate=self.gate, conv2D=conv2D, mask=mask) + self.dkd = DKD( + radius=conf.nms_radius, + top_k=-1 if conf.detection_threshold > 0 else conf.max_num_keypoints, + scores_th=conf.detection_threshold, + n_limit=conf.max_num_keypoints + if conf.max_num_keypoints > 0 + else self.n_limit_max, + ) + + state_dict = torch.hub.load_state_dict_from_url( + self.checkpoint_url.format(conf.model_name), map_location="cpu" + ) + self.load_state_dict(state_dict, strict=True) + + def get_resblock(self, c_in, c_out, conv_type, mask): + return ResBlock( + c_in, + c_out, + 1, + nn.Conv2d(c_in, c_out, 1), + gate=self.gate, + norm_layer=self.norm, + conv_type=conv_type, + mask=mask, + ) + + def extract_dense_map(self, image): + # Pads images such that dimensions are divisible by + div_by = 2**5 + padder = InputPadder(image.shape[-2], image.shape[-1], div_by) + image = padder.pad(image) + + # ================================== feature encoder + x1 = self.block1(image) # B x c1 x H x W + x2 = self.pool2(x1) + x2 = self.block2(x2) # B x c2 x H/2 x W/2 + x3 = self.pool4(x2) + x3 = self.block3(x3) # B x c3 x H/8 x W/8 + x4 = self.pool4(x3) + x4 = self.block4(x4) # B x dim x H/32 x W/32 + # ================================== feature aggregation + x1 = self.gate(self.conv1(x1)) # B x dim//4 x H x W + x2 = self.gate(self.conv2(x2)) # B x dim//4 x H//2 x W//2 + x3 = self.gate(self.conv3(x3)) # B x dim//4 x H//8 x W//8 + x4 = self.gate(self.conv4(x4)) # B x dim//4 x H//32 x W//32 + x2_up = self.upsample2(x2) # B x dim//4 x H x W + x3_up = self.upsample8(x3) # B x dim//4 x H x W + x4_up = self.upsample32(x4) # B x dim//4 x H x W + x1234 = torch.cat([x1, x2_up, x3_up, x4_up], dim=1) + # ================================== score head + score_map = torch.sigmoid(self.score_head(x1234)) + feature_map = torch.nn.functional.normalize(x1234, p=2, dim=1) + + # Unpads images + feature_map = padder.unpad(feature_map) + score_map = padder.unpad(score_map) + + return feature_map, score_map + + def forward(self, data: dict) -> dict: + image = data["image"] + if image.shape[1] == 1: + image = grayscale_to_rgb(image) + feature_map, score_map = self.extract_dense_map(image) + keypoints, kptscores, scoredispersitys = self.dkd( + score_map, image_size=data.get("image_size") + ) + descriptors, offsets = self.desc_head(feature_map, keypoints) + + _, _, h, w = image.shape + wh = torch.tensor([w - 1, h - 1], device=image.device) + # no padding required + # we can set detection_threshold=-1 and conf.max_num_keypoints > 0 + return { + "keypoints": wh * (torch.stack(keypoints) + 1) / 2.0, # B x N x 2 + "descriptors": torch.stack(descriptors), # B x N x D + "keypoint_scores": torch.stack(kptscores), # B x N + } diff --git a/LightGlue/lightglue/disk.py b/LightGlue/lightglue/disk.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb2195fe2f95c32959b5be4b09ad91bb51a35d5 --- /dev/null +++ b/LightGlue/lightglue/disk.py @@ -0,0 +1,55 @@ +import kornia +import torch + +from .utils import Extractor + + +class DISK(Extractor): + default_conf = { + "weights": "depth", + "max_num_keypoints": None, + "desc_dim": 128, + "nms_window_size": 5, + "detection_threshold": 0.0, + "pad_if_not_divisible": True, + } + + preprocess_conf = { + "resize": 1024, + "grayscale": False, + } + + required_data_keys = ["image"] + + def __init__(self, **conf) -> None: + super().__init__(**conf) # Update with default configuration. + self.model = kornia.feature.DISK.from_pretrained(self.conf.weights) + + def forward(self, data: dict) -> dict: + """Compute keypoints, scores, descriptors for image""" + for key in self.required_data_keys: + assert key in data, f"Missing key {key} in data" + image = data["image"] + if image.shape[1] == 1: + image = kornia.color.grayscale_to_rgb(image) + features = self.model( + image, + n=self.conf.max_num_keypoints, + window_size=self.conf.nms_window_size, + score_threshold=self.conf.detection_threshold, + pad_if_not_divisible=self.conf.pad_if_not_divisible, + ) + keypoints = [f.keypoints for f in features] + scores = [f.detection_scores for f in features] + descriptors = [f.descriptors for f in features] + del features + + keypoints = torch.stack(keypoints, 0) + scores = torch.stack(scores, 0) + descriptors = torch.stack(descriptors, 0) + + return { + "keypoints": keypoints.to(image).contiguous(), + "keypoint_scores": scores.to(image).contiguous(), + "descriptors": descriptors.to(image).contiguous(), + } diff --git a/LightGlue/lightglue/dog_hardnet.py b/LightGlue/lightglue/dog_hardnet.py new file mode 100644 index 0000000000000000000000000000000000000000..cce307ae1f11e2066312fd44ecac8884d1de3358 --- /dev/null +++ b/LightGlue/lightglue/dog_hardnet.py @@ -0,0 +1,41 @@ +import torch +from kornia.color import rgb_to_grayscale +from kornia.feature import HardNet, LAFDescriptor, laf_from_center_scale_ori + +from .sift import SIFT + + +class DoGHardNet(SIFT): + required_data_keys = ["image"] + + def __init__(self, **conf): + super().__init__(**conf) + self.laf_desc = LAFDescriptor(HardNet(True)).eval() + + def forward(self, data: dict) -> dict: + image = data["image"] + if image.shape[1] == 3: + image = rgb_to_grayscale(image) + device = image.device + self.laf_desc = self.laf_desc.to(device) + self.laf_desc.descriptor = self.laf_desc.descriptor.eval() + pred = [] + if "image_size" in data.keys(): + im_size = data.get("image_size").long() + else: + im_size = None + for k in range(len(image)): + img = image[k] + if im_size is not None: + w, h = data["image_size"][k] + img = img[:, : h.to(torch.int32), : w.to(torch.int32)] + p = self.extract_single_image(img) + lafs = laf_from_center_scale_ori( + p["keypoints"].reshape(1, -1, 2), + 6.0 * p["scales"].reshape(1, -1, 1, 1), + torch.rad2deg(p["oris"]).reshape(1, -1, 1), + ).to(device) + p["descriptors"] = self.laf_desc(img[None], lafs).reshape(-1, 128) + pred.append(p) + pred = {k: torch.stack([p[k] for p in pred], 0).to(device) for k in pred[0]} + return pred diff --git a/LightGlue/lightglue/lightglue.py b/LightGlue/lightglue/lightglue.py new file mode 100644 index 0000000000000000000000000000000000000000..5b38b5bbc8632f476c1d59795b0f364c7313e77e --- /dev/null +++ b/LightGlue/lightglue/lightglue.py @@ -0,0 +1,655 @@ +import warnings +from pathlib import Path +from types import SimpleNamespace +from typing import Callable, List, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +try: + from flash_attn.modules.mha import FlashCrossAttention +except ModuleNotFoundError: + FlashCrossAttention = None + +if FlashCrossAttention or hasattr(F, "scaled_dot_product_attention"): + FLASH_AVAILABLE = True +else: + FLASH_AVAILABLE = False + +torch.backends.cudnn.deterministic = True + + +@torch.cuda.amp.custom_fwd(cast_inputs=torch.float32) +def normalize_keypoints( + kpts: torch.Tensor, size: Optional[torch.Tensor] = None +) -> torch.Tensor: + if size is None: + size = 1 + kpts.max(-2).values - kpts.min(-2).values + elif not isinstance(size, torch.Tensor): + size = torch.tensor(size, device=kpts.device, dtype=kpts.dtype) + size = size.to(kpts) + shift = size / 2 + scale = size.max(-1).values / 2 + kpts = (kpts - shift[..., None, :]) / scale[..., None, None] + return kpts + + +def pad_to_length(x: torch.Tensor, length: int) -> Tuple[torch.Tensor]: + if length <= x.shape[-2]: + return x, torch.ones_like(x[..., :1], dtype=torch.bool) + pad = torch.ones( + *x.shape[:-2], length - x.shape[-2], x.shape[-1], device=x.device, dtype=x.dtype + ) + y = torch.cat([x, pad], dim=-2) + mask = torch.zeros(*y.shape[:-1], 1, dtype=torch.bool, device=x.device) + mask[..., : x.shape[-2], :] = True + return y, mask + + +def rotate_half(x: torch.Tensor) -> torch.Tensor: + x = x.unflatten(-1, (-1, 2)) + x1, x2 = x.unbind(dim=-1) + return torch.stack((-x2, x1), dim=-1).flatten(start_dim=-2) + + +def apply_cached_rotary_emb(freqs: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + return (t * freqs[0]) + (rotate_half(t) * freqs[1]) + + +class LearnableFourierPositionalEncoding(nn.Module): + def __init__(self, M: int, dim: int, F_dim: int = None, gamma: float = 1.0) -> None: + super().__init__() + F_dim = F_dim if F_dim is not None else dim + self.gamma = gamma + self.Wr = nn.Linear(M, F_dim // 2, bias=False) + nn.init.normal_(self.Wr.weight.data, mean=0, std=self.gamma**-2) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """encode position vector""" + projected = self.Wr(x) + cosines, sines = torch.cos(projected), torch.sin(projected) + emb = torch.stack([cosines, sines], 0).unsqueeze(-3) + return emb.repeat_interleave(2, dim=-1) + + +class TokenConfidence(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.token = nn.Sequential(nn.Linear(dim, 1), nn.Sigmoid()) + + def forward(self, desc0: torch.Tensor, desc1: torch.Tensor): + """get confidence tokens""" + return ( + self.token(desc0.detach()).squeeze(-1), + self.token(desc1.detach()).squeeze(-1), + ) + + +class Attention(nn.Module): + def __init__(self, allow_flash: bool) -> None: + super().__init__() + if allow_flash and not FLASH_AVAILABLE: + warnings.warn( + "FlashAttention is not available. For optimal speed, " + "consider installing torch >= 2.0 or flash-attn.", + stacklevel=2, + ) + self.enable_flash = allow_flash and FLASH_AVAILABLE + self.has_sdp = hasattr(F, "scaled_dot_product_attention") + if allow_flash and FlashCrossAttention: + self.flash_ = FlashCrossAttention() + if self.has_sdp: + torch.backends.cuda.enable_flash_sdp(allow_flash) + + def forward(self, q, k, v, mask: Optional[torch.Tensor] = None) -> torch.Tensor: + if q.shape[-2] == 0 or k.shape[-2] == 0: + return q.new_zeros((*q.shape[:-1], v.shape[-1])) + if self.enable_flash and q.device.type == "cuda": + # use torch 2.0 scaled_dot_product_attention with flash + if self.has_sdp: + args = [x.half().contiguous() for x in [q, k, v]] + v = F.scaled_dot_product_attention(*args, attn_mask=mask).to(q.dtype) + return v if mask is None else v.nan_to_num() + else: + assert mask is None + q, k, v = [x.transpose(-2, -3).contiguous() for x in [q, k, v]] + m = self.flash_(q.half(), torch.stack([k, v], 2).half()) + return m.transpose(-2, -3).to(q.dtype).clone() + elif self.has_sdp: + args = [x.contiguous() for x in [q, k, v]] + v = F.scaled_dot_product_attention(*args, attn_mask=mask) + return v if mask is None else v.nan_to_num() + else: + s = q.shape[-1] ** -0.5 + sim = torch.einsum("...id,...jd->...ij", q, k) * s + if mask is not None: + sim.masked_fill(~mask, -float("inf")) + attn = F.softmax(sim, -1) + return torch.einsum("...ij,...jd->...id", attn, v) + + +class SelfBlock(nn.Module): + def __init__( + self, embed_dim: int, num_heads: int, flash: bool = False, bias: bool = True + ) -> None: + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + assert self.embed_dim % num_heads == 0 + self.head_dim = self.embed_dim // num_heads + self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias) + self.inner_attn = Attention(flash) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.ffn = nn.Sequential( + nn.Linear(2 * embed_dim, 2 * embed_dim), + nn.LayerNorm(2 * embed_dim, elementwise_affine=True), + nn.GELU(), + nn.Linear(2 * embed_dim, embed_dim), + ) + + def forward( + self, + x: torch.Tensor, + encoding: torch.Tensor, + mask: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + qkv = self.Wqkv(x) + qkv = qkv.unflatten(-1, (self.num_heads, -1, 3)).transpose(1, 2) + q, k, v = qkv[..., 0], qkv[..., 1], qkv[..., 2] + q = apply_cached_rotary_emb(encoding, q) + k = apply_cached_rotary_emb(encoding, k) + context = self.inner_attn(q, k, v, mask=mask) + message = self.out_proj(context.transpose(1, 2).flatten(start_dim=-2)) + return x + self.ffn(torch.cat([x, message], -1)) + + +class CrossBlock(nn.Module): + def __init__( + self, embed_dim: int, num_heads: int, flash: bool = False, bias: bool = True + ) -> None: + super().__init__() + self.heads = num_heads + dim_head = embed_dim // num_heads + self.scale = dim_head**-0.5 + inner_dim = dim_head * num_heads + self.to_qk = nn.Linear(embed_dim, inner_dim, bias=bias) + self.to_v = nn.Linear(embed_dim, inner_dim, bias=bias) + self.to_out = nn.Linear(inner_dim, embed_dim, bias=bias) + self.ffn = nn.Sequential( + nn.Linear(2 * embed_dim, 2 * embed_dim), + nn.LayerNorm(2 * embed_dim, elementwise_affine=True), + nn.GELU(), + nn.Linear(2 * embed_dim, embed_dim), + ) + if flash and FLASH_AVAILABLE: + self.flash = Attention(True) + else: + self.flash = None + + def map_(self, func: Callable, x0: torch.Tensor, x1: torch.Tensor): + return func(x0), func(x1) + + def forward( + self, x0: torch.Tensor, x1: torch.Tensor, mask: Optional[torch.Tensor] = None + ) -> List[torch.Tensor]: + qk0, qk1 = self.map_(self.to_qk, x0, x1) + v0, v1 = self.map_(self.to_v, x0, x1) + qk0, qk1, v0, v1 = map( + lambda t: t.unflatten(-1, (self.heads, -1)).transpose(1, 2), + (qk0, qk1, v0, v1), + ) + if self.flash is not None and qk0.device.type == "cuda": + m0 = self.flash(qk0, qk1, v1, mask) + m1 = self.flash( + qk1, qk0, v0, mask.transpose(-1, -2) if mask is not None else None + ) + else: + qk0, qk1 = qk0 * self.scale**0.5, qk1 * self.scale**0.5 + sim = torch.einsum("bhid, bhjd -> bhij", qk0, qk1) + if mask is not None: + sim = sim.masked_fill(~mask, -float("inf")) + attn01 = F.softmax(sim, dim=-1) + attn10 = F.softmax(sim.transpose(-2, -1).contiguous(), dim=-1) + m0 = torch.einsum("bhij, bhjd -> bhid", attn01, v1) + m1 = torch.einsum("bhji, bhjd -> bhid", attn10.transpose(-2, -1), v0) + if mask is not None: + m0, m1 = m0.nan_to_num(), m1.nan_to_num() + m0, m1 = self.map_(lambda t: t.transpose(1, 2).flatten(start_dim=-2), m0, m1) + m0, m1 = self.map_(self.to_out, m0, m1) + x0 = x0 + self.ffn(torch.cat([x0, m0], -1)) + x1 = x1 + self.ffn(torch.cat([x1, m1], -1)) + return x0, x1 + + +class TransformerLayer(nn.Module): + def __init__(self, *args, **kwargs): + super().__init__() + self.self_attn = SelfBlock(*args, **kwargs) + self.cross_attn = CrossBlock(*args, **kwargs) + + def forward( + self, + desc0, + desc1, + encoding0, + encoding1, + mask0: Optional[torch.Tensor] = None, + mask1: Optional[torch.Tensor] = None, + ): + if mask0 is not None and mask1 is not None: + return self.masked_forward(desc0, desc1, encoding0, encoding1, mask0, mask1) + else: + desc0 = self.self_attn(desc0, encoding0) + desc1 = self.self_attn(desc1, encoding1) + return self.cross_attn(desc0, desc1) + + # This part is compiled and allows padding inputs + def masked_forward(self, desc0, desc1, encoding0, encoding1, mask0, mask1): + mask = mask0 & mask1.transpose(-1, -2) + mask0 = mask0 & mask0.transpose(-1, -2) + mask1 = mask1 & mask1.transpose(-1, -2) + desc0 = self.self_attn(desc0, encoding0, mask0) + desc1 = self.self_attn(desc1, encoding1, mask1) + return self.cross_attn(desc0, desc1, mask) + + +def sigmoid_log_double_softmax( + sim: torch.Tensor, z0: torch.Tensor, z1: torch.Tensor +) -> torch.Tensor: + """create the log assignment matrix from logits and similarity""" + b, m, n = sim.shape + certainties = F.logsigmoid(z0) + F.logsigmoid(z1).transpose(1, 2) + scores0 = F.log_softmax(sim, 2) + scores1 = F.log_softmax(sim.transpose(-1, -2).contiguous(), 2).transpose(-1, -2) + scores = sim.new_full((b, m + 1, n + 1), 0) + scores[:, :m, :n] = scores0 + scores1 + certainties + scores[:, :-1, -1] = F.logsigmoid(-z0.squeeze(-1)) + scores[:, -1, :-1] = F.logsigmoid(-z1.squeeze(-1)) + return scores + + +class MatchAssignment(nn.Module): + def __init__(self, dim: int) -> None: + super().__init__() + self.dim = dim + self.matchability = nn.Linear(dim, 1, bias=True) + self.final_proj = nn.Linear(dim, dim, bias=True) + + def forward(self, desc0: torch.Tensor, desc1: torch.Tensor): + """build assignment matrix from descriptors""" + mdesc0, mdesc1 = self.final_proj(desc0), self.final_proj(desc1) + _, _, d = mdesc0.shape + mdesc0, mdesc1 = mdesc0 / d**0.25, mdesc1 / d**0.25 + sim = torch.einsum("bmd,bnd->bmn", mdesc0, mdesc1) + z0 = self.matchability(desc0) + z1 = self.matchability(desc1) + scores = sigmoid_log_double_softmax(sim, z0, z1) + return scores, sim + + def get_matchability(self, desc: torch.Tensor): + return torch.sigmoid(self.matchability(desc)).squeeze(-1) + + +def filter_matches(scores: torch.Tensor, th: float): + """obtain matches from a log assignment matrix [Bx M+1 x N+1]""" + max0, max1 = scores[:, :-1, :-1].max(2), scores[:, :-1, :-1].max(1) + m0, m1 = max0.indices, max1.indices + indices0 = torch.arange(m0.shape[1], device=m0.device)[None] + indices1 = torch.arange(m1.shape[1], device=m1.device)[None] + mutual0 = indices0 == m1.gather(1, m0) + mutual1 = indices1 == m0.gather(1, m1) + max0_exp = max0.values.exp() + zero = max0_exp.new_tensor(0) + mscores0 = torch.where(mutual0, max0_exp, zero) + mscores1 = torch.where(mutual1, mscores0.gather(1, m1), zero) + valid0 = mutual0 & (mscores0 > th) + valid1 = mutual1 & valid0.gather(1, m1) + m0 = torch.where(valid0, m0, -1) + m1 = torch.where(valid1, m1, -1) + return m0, m1, mscores0, mscores1 + + +class LightGlue(nn.Module): + default_conf = { + "name": "lightglue", # just for interfacing + "input_dim": 256, # input descriptor dimension (autoselected from weights) + "descriptor_dim": 256, + "add_scale_ori": False, + "n_layers": 9, + "num_heads": 4, + "flash": True, # enable FlashAttention if available. + "mp": False, # enable mixed precision + "depth_confidence": 0.95, # early stopping, disable with -1 + "width_confidence": 0.99, # point pruning, disable with -1 + "filter_threshold": 0.1, # match threshold + "weights": None, + } + + # Point pruning involves an overhead (gather). + # Therefore, we only activate it if there are enough keypoints. + pruning_keypoint_thresholds = { + "cpu": -1, + "mps": -1, + "cuda": 1024, + "flash": 1536, + } + + required_data_keys = ["image0", "image1"] + + version = "v0.1_arxiv" + url = "https://github.com/cvg/LightGlue/releases/download/{}/{}_lightglue.pth" + + features = { + "superpoint": { + "weights": "superpoint_lightglue", + "input_dim": 256, + }, + "disk": { + "weights": "disk_lightglue", + "input_dim": 128, + }, + "aliked": { + "weights": "aliked_lightglue", + "input_dim": 128, + }, + "sift": { + "weights": "sift_lightglue", + "input_dim": 128, + "add_scale_ori": True, + }, + "doghardnet": { + "weights": "doghardnet_lightglue", + "input_dim": 128, + "add_scale_ori": True, + }, + } + + def __init__(self, features="superpoint", **conf) -> None: + super().__init__() + self.conf = conf = SimpleNamespace(**{**self.default_conf, **conf}) + if features is not None: + if features not in self.features: + raise ValueError( + f"Unsupported features: {features} not in " + f"{{{','.join(self.features)}}}" + ) + for k, v in self.features[features].items(): + setattr(conf, k, v) + + if conf.input_dim != conf.descriptor_dim: + self.input_proj = nn.Linear(conf.input_dim, conf.descriptor_dim, bias=True) + else: + self.input_proj = nn.Identity() + + head_dim = conf.descriptor_dim // conf.num_heads + self.posenc = LearnableFourierPositionalEncoding( + 2 + 2 * self.conf.add_scale_ori, head_dim, head_dim + ) + + h, n, d = conf.num_heads, conf.n_layers, conf.descriptor_dim + + self.transformers = nn.ModuleList( + [TransformerLayer(d, h, conf.flash) for _ in range(n)] + ) + + self.log_assignment = nn.ModuleList([MatchAssignment(d) for _ in range(n)]) + self.token_confidence = nn.ModuleList( + [TokenConfidence(d) for _ in range(n - 1)] + ) + self.register_buffer( + "confidence_thresholds", + torch.Tensor( + [self.confidence_threshold(i) for i in range(self.conf.n_layers)] + ), + ) + + state_dict = None + if features is not None: + fname = f"{conf.weights}_{self.version.replace('.', '-')}.pth" + state_dict = torch.hub.load_state_dict_from_url( + self.url.format(self.version, features), model_dir='./LightGlue/ckpts',file_name="superpoint_lightglue.pth" + ) + self.load_state_dict(state_dict, strict=False) + elif conf.weights is not None: + path = Path(__file__).parent + path = path / "weights/{}.pth".format(self.conf.weights) + state_dict = torch.load(str(path), map_location="cpu") + + if state_dict: + # rename old state dict entries + for i in range(self.conf.n_layers): + pattern = f"self_attn.{i}", f"transformers.{i}.self_attn" + state_dict = {k.replace(*pattern): v for k, v in state_dict.items()} + pattern = f"cross_attn.{i}", f"transformers.{i}.cross_attn" + state_dict = {k.replace(*pattern): v for k, v in state_dict.items()} + self.load_state_dict(state_dict, strict=False) + + # static lengths LightGlue is compiled for (only used with torch.compile) + self.static_lengths = None + + def compile( + self, mode="reduce-overhead", static_lengths=[256, 512, 768, 1024, 1280, 1536] + ): + if self.conf.width_confidence != -1: + warnings.warn( + "Point pruning is partially disabled for compiled forward.", + stacklevel=2, + ) + + torch._inductor.cudagraph_mark_step_begin() + for i in range(self.conf.n_layers): + self.transformers[i].masked_forward = torch.compile( + self.transformers[i].masked_forward, mode=mode, fullgraph=True + ) + + self.static_lengths = static_lengths + + def forward(self, data: dict) -> dict: + """ + Match keypoints and descriptors between two images + + Input (dict): + image0: dict + keypoints: [B x M x 2] + descriptors: [B x M x D] + image: [B x C x H x W] or image_size: [B x 2] + image1: dict + keypoints: [B x N x 2] + descriptors: [B x N x D] + image: [B x C x H x W] or image_size: [B x 2] + Output (dict): + matches0: [B x M] + matching_scores0: [B x M] + matches1: [B x N] + matching_scores1: [B x N] + matches: List[[Si x 2]] + scores: List[[Si]] + stop: int + prune0: [B x M] + prune1: [B x N] + """ + with torch.autocast(enabled=self.conf.mp, device_type="cuda"): + return self._forward(data) + + def _forward(self, data: dict) -> dict: + for key in self.required_data_keys: + assert key in data, f"Missing key {key} in data" + data0, data1 = data["image0"], data["image1"] + kpts0, kpts1 = data0["keypoints"], data1["keypoints"] + b, m, _ = kpts0.shape + b, n, _ = kpts1.shape + device = kpts0.device + size0, size1 = data0.get("image_size"), data1.get("image_size") + kpts0 = normalize_keypoints(kpts0, size0).clone() + kpts1 = normalize_keypoints(kpts1, size1).clone() + + if self.conf.add_scale_ori: + kpts0 = torch.cat( + [kpts0] + [data0[k].unsqueeze(-1) for k in ("scales", "oris")], -1 + ) + kpts1 = torch.cat( + [kpts1] + [data1[k].unsqueeze(-1) for k in ("scales", "oris")], -1 + ) + desc0 = data0["descriptors"].detach().contiguous() + desc1 = data1["descriptors"].detach().contiguous() + + assert desc0.shape[-1] == self.conf.input_dim + assert desc1.shape[-1] == self.conf.input_dim + + if torch.is_autocast_enabled(): + desc0 = desc0.half() + desc1 = desc1.half() + + mask0, mask1 = None, None + c = max(m, n) + do_compile = self.static_lengths and c <= max(self.static_lengths) + if do_compile: + kn = min([k for k in self.static_lengths if k >= c]) + desc0, mask0 = pad_to_length(desc0, kn) + desc1, mask1 = pad_to_length(desc1, kn) + kpts0, _ = pad_to_length(kpts0, kn) + kpts1, _ = pad_to_length(kpts1, kn) + desc0 = self.input_proj(desc0) + desc1 = self.input_proj(desc1) + # cache positional embeddings + encoding0 = self.posenc(kpts0) + encoding1 = self.posenc(kpts1) + + # GNN + final_proj + assignment + do_early_stop = self.conf.depth_confidence > 0 + do_point_pruning = self.conf.width_confidence > 0 and not do_compile + pruning_th = self.pruning_min_kpts(device) + if do_point_pruning: + ind0 = torch.arange(0, m, device=device)[None] + ind1 = torch.arange(0, n, device=device)[None] + # We store the index of the layer at which pruning is detected. + prune0 = torch.ones_like(ind0) + prune1 = torch.ones_like(ind1) + token0, token1 = None, None + for i in range(self.conf.n_layers): + if desc0.shape[1] == 0 or desc1.shape[1] == 0: # no keypoints + break + desc0, desc1 = self.transformers[i]( + desc0, desc1, encoding0, encoding1, mask0=mask0, mask1=mask1 + ) + if i == self.conf.n_layers - 1: + continue # no early stopping or adaptive width at last layer + + if do_early_stop: + token0, token1 = self.token_confidence[i](desc0, desc1) + if self.check_if_stop(token0[..., :m], token1[..., :n], i, m + n): + break + if do_point_pruning and desc0.shape[-2] > pruning_th: + scores0 = self.log_assignment[i].get_matchability(desc0) + prunemask0 = self.get_pruning_mask(token0, scores0, i) + keep0 = torch.where(prunemask0)[1] + ind0 = ind0.index_select(1, keep0) + desc0 = desc0.index_select(1, keep0) + encoding0 = encoding0.index_select(-2, keep0) + prune0[:, ind0] += 1 + if do_point_pruning and desc1.shape[-2] > pruning_th: + scores1 = self.log_assignment[i].get_matchability(desc1) + prunemask1 = self.get_pruning_mask(token1, scores1, i) + keep1 = torch.where(prunemask1)[1] + ind1 = ind1.index_select(1, keep1) + desc1 = desc1.index_select(1, keep1) + encoding1 = encoding1.index_select(-2, keep1) + prune1[:, ind1] += 1 + + if desc0.shape[1] == 0 or desc1.shape[1] == 0: # no keypoints + m0 = desc0.new_full((b, m), -1, dtype=torch.long) + m1 = desc1.new_full((b, n), -1, dtype=torch.long) + mscores0 = desc0.new_zeros((b, m)) + mscores1 = desc1.new_zeros((b, n)) + matches = desc0.new_empty((b, 0, 2), dtype=torch.long) + mscores = desc0.new_empty((b, 0)) + if not do_point_pruning: + prune0 = torch.ones_like(mscores0) * self.conf.n_layers + prune1 = torch.ones_like(mscores1) * self.conf.n_layers + return { + "matches0": m0, + "matches1": m1, + "matching_scores0": mscores0, + "matching_scores1": mscores1, + "stop": i + 1, + "matches": matches, + "scores": mscores, + "prune0": prune0, + "prune1": prune1, + } + + desc0, desc1 = desc0[..., :m, :], desc1[..., :n, :] # remove padding + scores, _ = self.log_assignment[i](desc0, desc1) + m0, m1, mscores0, mscores1 = filter_matches(scores, self.conf.filter_threshold) + matches, mscores = [], [] + for k in range(b): + valid = m0[k] > -1 + m_indices_0 = torch.where(valid)[0] + m_indices_1 = m0[k][valid] + if do_point_pruning: + m_indices_0 = ind0[k, m_indices_0] + m_indices_1 = ind1[k, m_indices_1] + matches.append(torch.stack([m_indices_0, m_indices_1], -1)) + mscores.append(mscores0[k][valid]) + + # TODO: Remove when hloc switches to the compact format. + if do_point_pruning: + m0_ = torch.full((b, m), -1, device=m0.device, dtype=m0.dtype) + m1_ = torch.full((b, n), -1, device=m1.device, dtype=m1.dtype) + m0_[:, ind0] = torch.where(m0 == -1, -1, ind1.gather(1, m0.clamp(min=0))) + m1_[:, ind1] = torch.where(m1 == -1, -1, ind0.gather(1, m1.clamp(min=0))) + mscores0_ = torch.zeros((b, m), device=mscores0.device) + mscores1_ = torch.zeros((b, n), device=mscores1.device) + mscores0_[:, ind0] = mscores0 + mscores1_[:, ind1] = mscores1 + m0, m1, mscores0, mscores1 = m0_, m1_, mscores0_, mscores1_ + else: + prune0 = torch.ones_like(mscores0) * self.conf.n_layers + prune1 = torch.ones_like(mscores1) * self.conf.n_layers + + return { + "matches0": m0, + "matches1": m1, + "matching_scores0": mscores0, + "matching_scores1": mscores1, + "stop": i + 1, + "matches": matches, + "scores": mscores, + "prune0": prune0, + "prune1": prune1, + } + + def confidence_threshold(self, layer_index: int) -> float: + """scaled confidence threshold""" + threshold = 0.8 + 0.1 * np.exp(-4.0 * layer_index / self.conf.n_layers) + return np.clip(threshold, 0, 1) + + def get_pruning_mask( + self, confidences: torch.Tensor, scores: torch.Tensor, layer_index: int + ) -> torch.Tensor: + """mask points which should be removed""" + keep = scores > (1 - self.conf.width_confidence) + if confidences is not None: # Low-confidence points are never pruned. + keep |= confidences <= self.confidence_thresholds[layer_index] + return keep + + def check_if_stop( + self, + confidences0: torch.Tensor, + confidences1: torch.Tensor, + layer_index: int, + num_points: int, + ) -> torch.Tensor: + """evaluate stopping condition""" + confidences = torch.cat([confidences0, confidences1], -1) + threshold = self.confidence_thresholds[layer_index] + ratio_confident = 1.0 - (confidences < threshold).float().sum() / num_points + return ratio_confident > self.conf.depth_confidence + + def pruning_min_kpts(self, device: torch.device): + if self.conf.flash and FLASH_AVAILABLE and device.type == "cuda": + return self.pruning_keypoint_thresholds["flash"] + else: + return self.pruning_keypoint_thresholds[device.type] diff --git a/LightGlue/lightglue/sift.py b/LightGlue/lightglue/sift.py new file mode 100644 index 0000000000000000000000000000000000000000..802fc1c2eb9ee852691e0e4dd67455f822f8405f --- /dev/null +++ b/LightGlue/lightglue/sift.py @@ -0,0 +1,216 @@ +import warnings + +import cv2 +import numpy as np +import torch +from kornia.color import rgb_to_grayscale +from packaging import version + +try: + import pycolmap +except ImportError: + pycolmap = None + +from .utils import Extractor + + +def filter_dog_point(points, scales, angles, image_shape, nms_radius, scores=None): + h, w = image_shape + ij = np.round(points - 0.5).astype(int).T[::-1] + + # Remove duplicate points (identical coordinates). + # Pick highest scale or score + s = scales if scores is None else scores + buffer = np.zeros((h, w)) + np.maximum.at(buffer, tuple(ij), s) + keep = np.where(buffer[tuple(ij)] == s)[0] + + # Pick lowest angle (arbitrary). + ij = ij[:, keep] + buffer[:] = np.inf + o_abs = np.abs(angles[keep]) + np.minimum.at(buffer, tuple(ij), o_abs) + mask = buffer[tuple(ij)] == o_abs + ij = ij[:, mask] + keep = keep[mask] + + if nms_radius > 0: + # Apply NMS on the remaining points + buffer[:] = 0 + buffer[tuple(ij)] = s[keep] # scores or scale + + local_max = torch.nn.functional.max_pool2d( + torch.from_numpy(buffer).unsqueeze(0), + kernel_size=nms_radius * 2 + 1, + stride=1, + padding=nms_radius, + ).squeeze(0) + is_local_max = buffer == local_max.numpy() + keep = keep[is_local_max[tuple(ij)]] + return keep + + +def sift_to_rootsift(x: torch.Tensor, eps=1e-6) -> torch.Tensor: + x = torch.nn.functional.normalize(x, p=1, dim=-1, eps=eps) + x.clip_(min=eps).sqrt_() + return torch.nn.functional.normalize(x, p=2, dim=-1, eps=eps) + + +def run_opencv_sift(features: cv2.Feature2D, image: np.ndarray) -> np.ndarray: + """ + Detect keypoints using OpenCV Detector. + Optionally, perform description. + Args: + features: OpenCV based keypoints detector and descriptor + image: Grayscale image of uint8 data type + Returns: + keypoints: 1D array of detected cv2.KeyPoint + scores: 1D array of responses + descriptors: 1D array of descriptors + """ + detections, descriptors = features.detectAndCompute(image, None) + points = np.array([k.pt for k in detections], dtype=np.float32) + scores = np.array([k.response for k in detections], dtype=np.float32) + scales = np.array([k.size for k in detections], dtype=np.float32) + angles = np.deg2rad(np.array([k.angle for k in detections], dtype=np.float32)) + return points, scores, scales, angles, descriptors + + +class SIFT(Extractor): + default_conf = { + "rootsift": True, + "nms_radius": 0, # None to disable filtering entirely. + "max_num_keypoints": 4096, + "backend": "opencv", # in {opencv, pycolmap, pycolmap_cpu, pycolmap_cuda} + "detection_threshold": 0.0066667, # from COLMAP + "edge_threshold": 10, + "first_octave": -1, # only used by pycolmap, the default of COLMAP + "num_octaves": 4, + } + + preprocess_conf = { + "resize": 1024, + } + + required_data_keys = ["image"] + + def __init__(self, **conf): + super().__init__(**conf) # Update with default configuration. + backend = self.conf.backend + if backend.startswith("pycolmap"): + if pycolmap is None: + raise ImportError( + "Cannot find module pycolmap: install it with pip" + "or use backend=opencv." + ) + options = { + "peak_threshold": self.conf.detection_threshold, + "edge_threshold": self.conf.edge_threshold, + "first_octave": self.conf.first_octave, + "num_octaves": self.conf.num_octaves, + "normalization": pycolmap.Normalization.L2, # L1_ROOT is buggy. + } + device = ( + "auto" if backend == "pycolmap" else backend.replace("pycolmap_", "") + ) + if ( + backend == "pycolmap_cpu" or not pycolmap.has_cuda + ) and pycolmap.__version__ < "0.5.0": + warnings.warn( + "The pycolmap CPU SIFT is buggy in version < 0.5.0, " + "consider upgrading pycolmap or use the CUDA version.", + stacklevel=1, + ) + else: + options["max_num_features"] = self.conf.max_num_keypoints + self.sift = pycolmap.Sift(options=options, device=device) + elif backend == "opencv": + self.sift = cv2.SIFT_create( + contrastThreshold=self.conf.detection_threshold, + nfeatures=self.conf.max_num_keypoints, + edgeThreshold=self.conf.edge_threshold, + nOctaveLayers=self.conf.num_octaves, + ) + else: + backends = {"opencv", "pycolmap", "pycolmap_cpu", "pycolmap_cuda"} + raise ValueError( + f"Unknown backend: {backend} not in " f"{{{','.join(backends)}}}." + ) + + def extract_single_image(self, image: torch.Tensor): + image_np = image.cpu().numpy().squeeze(0) + + if self.conf.backend.startswith("pycolmap"): + if version.parse(pycolmap.__version__) >= version.parse("0.5.0"): + detections, descriptors = self.sift.extract(image_np) + scores = None # Scores are not exposed by COLMAP anymore. + else: + detections, scores, descriptors = self.sift.extract(image_np) + keypoints = detections[:, :2] # Keep only (x, y). + scales, angles = detections[:, -2:].T + if scores is not None and ( + self.conf.backend == "pycolmap_cpu" or not pycolmap.has_cuda + ): + # Set the scores as a combination of abs. response and scale. + scores = np.abs(scores) * scales + elif self.conf.backend == "opencv": + # TODO: Check if opencv keypoints are already in corner convention + keypoints, scores, scales, angles, descriptors = run_opencv_sift( + self.sift, (image_np * 255.0).astype(np.uint8) + ) + pred = { + "keypoints": keypoints, + "scales": scales, + "oris": angles, + "descriptors": descriptors, + } + if scores is not None: + pred["keypoint_scores"] = scores + + # sometimes pycolmap returns points outside the image. We remove them + if self.conf.backend.startswith("pycolmap"): + is_inside = ( + pred["keypoints"] + 0.5 < np.array([image_np.shape[-2:][::-1]]) + ).all(-1) + pred = {k: v[is_inside] for k, v in pred.items()} + + if self.conf.nms_radius is not None: + keep = filter_dog_point( + pred["keypoints"], + pred["scales"], + pred["oris"], + image_np.shape, + self.conf.nms_radius, + scores=pred.get("keypoint_scores"), + ) + pred = {k: v[keep] for k, v in pred.items()} + + pred = {k: torch.from_numpy(v) for k, v in pred.items()} + if scores is not None: + # Keep the k keypoints with highest score + num_points = self.conf.max_num_keypoints + if num_points is not None and len(pred["keypoints"]) > num_points: + indices = torch.topk(pred["keypoint_scores"], num_points).indices + pred = {k: v[indices] for k, v in pred.items()} + + return pred + + def forward(self, data: dict) -> dict: + image = data["image"] + if image.shape[1] == 3: + image = rgb_to_grayscale(image) + device = image.device + image = image.cpu() + pred = [] + for k in range(len(image)): + img = image[k] + if "image_size" in data.keys(): + # avoid extracting points in padded areas + w, h = data["image_size"][k] + img = img[:, :h, :w] + p = self.extract_single_image(img) + pred.append(p) + pred = {k: torch.stack([p[k] for p in pred], 0).to(device) for k in pred[0]} + if self.conf.rootsift: + pred["descriptors"] = sift_to_rootsift(pred["descriptors"]) + return pred diff --git a/LightGlue/lightglue/superpoint.py b/LightGlue/lightglue/superpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..67d91a1d4b91e5bc57ec19341a4a3db90e23db64 --- /dev/null +++ b/LightGlue/lightglue/superpoint.py @@ -0,0 +1,227 @@ +# %BANNER_BEGIN% +# --------------------------------------------------------------------- +# %COPYRIGHT_BEGIN% +# +# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL +# +# Unpublished Copyright (c) 2020 +# Magic Leap, Inc., All Rights Reserved. +# +# NOTICE: All information contained herein is, and remains the property +# of COMPANY. The intellectual and technical concepts contained herein +# are proprietary to COMPANY and may be covered by U.S. and Foreign +# Patents, patents in process, and are protected by trade secret or +# copyright law. Dissemination of this information or reproduction of +# this material is strictly forbidden unless prior written permission is +# obtained from COMPANY. Access to the source code contained herein is +# hereby forbidden to anyone except current COMPANY employees, managers +# or contractors who have executed Confidentiality and Non-disclosure +# agreements explicitly covering such access. +# +# The copyright notice above does not evidence any actual or intended +# publication or disclosure of this source code, which includes +# information that is confidential and/or proprietary, and is a trade +# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION, +# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS +# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS +# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND +# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE +# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS +# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE, +# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART. +# +# %COPYRIGHT_END% +# ---------------------------------------------------------------------- +# %AUTHORS_BEGIN% +# +# Originating Authors: Paul-Edouard Sarlin +# +# %AUTHORS_END% +# --------------------------------------------------------------------*/ +# %BANNER_END% + +# Adapted by Remi Pautrat, Philipp Lindenberger + +import torch +from kornia.color import rgb_to_grayscale +from torch import nn + +from .utils import Extractor + + +def simple_nms(scores, nms_radius: int): + """Fast Non-maximum suppression to remove nearby points""" + assert nms_radius >= 0 + + def max_pool(x): + return torch.nn.functional.max_pool2d( + x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius + ) + + zeros = torch.zeros_like(scores) + max_mask = scores == max_pool(scores) + for _ in range(2): + supp_mask = max_pool(max_mask.float()) > 0 + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == max_pool(supp_scores) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +def top_k_keypoints(keypoints, scores, k): + if k >= len(keypoints): + return keypoints, scores + scores, indices = torch.topk(scores, k, dim=0, sorted=True) + return keypoints[indices], scores + + +def sample_descriptors(keypoints, descriptors, s: int = 8): + """Interpolate descriptors at keypoint locations""" + b, c, h, w = descriptors.shape + keypoints = keypoints - s / 2 + 0.5 + keypoints /= torch.tensor( + [(w * s - s / 2 - 0.5), (h * s - s / 2 - 0.5)], + ).to( + keypoints + )[None] + keypoints = keypoints * 2 - 1 # normalize to (-1, 1) + args = {"align_corners": True} if torch.__version__ >= "1.3" else {} + descriptors = torch.nn.functional.grid_sample( + descriptors, keypoints.view(b, 1, -1, 2), mode="bilinear", **args + ) + descriptors = torch.nn.functional.normalize( + descriptors.reshape(b, c, -1), p=2, dim=1 + ) + return descriptors + + +class SuperPoint(Extractor): + """SuperPoint Convolutional Detector and Descriptor + + SuperPoint: Self-Supervised Interest Point Detection and + Description. Daniel DeTone, Tomasz Malisiewicz, and Andrew + Rabinovich. In CVPRW, 2019. https://arxiv.org/abs/1712.07629 + + """ + + default_conf = { + "descriptor_dim": 256, + "nms_radius": 4, + "max_num_keypoints": None, + "detection_threshold": 0.0005, + "remove_borders": 4, + } + + preprocess_conf = { + "resize": 1024, + } + + required_data_keys = ["image"] + + def __init__(self, **conf): + super().__init__(**conf) # Update with default configuration. + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + c1, c2, c3, c4, c5 = 64, 64, 128, 128, 256 + + self.conv1a = nn.Conv2d(1, c1, kernel_size=3, stride=1, padding=1) + self.conv1b = nn.Conv2d(c1, c1, kernel_size=3, stride=1, padding=1) + self.conv2a = nn.Conv2d(c1, c2, kernel_size=3, stride=1, padding=1) + self.conv2b = nn.Conv2d(c2, c2, kernel_size=3, stride=1, padding=1) + self.conv3a = nn.Conv2d(c2, c3, kernel_size=3, stride=1, padding=1) + self.conv3b = nn.Conv2d(c3, c3, kernel_size=3, stride=1, padding=1) + self.conv4a = nn.Conv2d(c3, c4, kernel_size=3, stride=1, padding=1) + self.conv4b = nn.Conv2d(c4, c4, kernel_size=3, stride=1, padding=1) + + self.convPa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convPb = nn.Conv2d(c5, 65, kernel_size=1, stride=1, padding=0) + + self.convDa = nn.Conv2d(c4, c5, kernel_size=3, stride=1, padding=1) + self.convDb = nn.Conv2d( + c5, self.conf.descriptor_dim, kernel_size=1, stride=1, padding=0 + ) + + url = "https://github.com/cvg/LightGlue/releases/download/v0.1_arxiv/superpoint_v1.pth" # noqa + self.load_state_dict(torch.hub.load_state_dict_from_url(url,model_dir='./LightGlue/ckpts/',file_name='superpoint_v1.pth')) + + if self.conf.max_num_keypoints is not None and self.conf.max_num_keypoints <= 0: + raise ValueError("max_num_keypoints must be positive or None") + + def forward(self, data: dict) -> dict: + """Compute keypoints, scores, descriptors for image""" + for key in self.required_data_keys: + assert key in data, f"Missing key {key} in data" + image = data["image"] + if image.shape[1] == 3: + image = rgb_to_grayscale(image) + + # Shared Encoder + x = self.relu(self.conv1a(image)) + x = self.relu(self.conv1b(x)) + x = self.pool(x) + x = self.relu(self.conv2a(x)) + x = self.relu(self.conv2b(x)) + x = self.pool(x) + x = self.relu(self.conv3a(x)) + x = self.relu(self.conv3b(x)) + x = self.pool(x) + x = self.relu(self.conv4a(x)) + x = self.relu(self.conv4b(x)) + + # Compute the dense keypoint scores + cPa = self.relu(self.convPa(x)) + scores = self.convPb(cPa) + scores = torch.nn.functional.softmax(scores, 1)[:, :-1] + b, _, h, w = scores.shape + scores = scores.permute(0, 2, 3, 1).reshape(b, h, w, 8, 8) + scores = scores.permute(0, 1, 3, 2, 4).reshape(b, h * 8, w * 8) + scores = simple_nms(scores, self.conf.nms_radius) + + # Discard keypoints near the image borders + if self.conf.remove_borders: + pad = self.conf.remove_borders + scores[:, :pad] = -1 + scores[:, :, :pad] = -1 + scores[:, -pad:] = -1 + scores[:, :, -pad:] = -1 + + # Extract keypoints + best_kp = torch.where(scores > self.conf.detection_threshold) + scores = scores[best_kp] + + # Separate into batches + keypoints = [ + torch.stack(best_kp[1:3], dim=-1)[best_kp[0] == i] for i in range(b) + ] + scores = [scores[best_kp[0] == i] for i in range(b)] + + # Keep the k keypoints with highest score + if self.conf.max_num_keypoints is not None: + keypoints, scores = list( + zip( + *[ + top_k_keypoints(k, s, self.conf.max_num_keypoints) + for k, s in zip(keypoints, scores) + ] + ) + ) + + # Convert (h, w) to (x, y) + keypoints = [torch.flip(k, [1]).float() for k in keypoints] + + # Compute the dense descriptors + cDa = self.relu(self.convDa(x)) + descriptors = self.convDb(cDa) + descriptors = torch.nn.functional.normalize(descriptors, p=2, dim=1) + + # Extract descriptors + descriptors = [ + sample_descriptors(k[None], d[None], 8)[0] + for k, d in zip(keypoints, descriptors) + ] + + return { + "keypoints": torch.stack(keypoints, 0), + "keypoint_scores": torch.stack(scores, 0), + "descriptors": torch.stack(descriptors, 0).transpose(-1, -2).contiguous(), + } diff --git a/LightGlue/lightglue/utils.py b/LightGlue/lightglue/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d1c1ab2e94716b1c54191a6ed5d01023036836c1 --- /dev/null +++ b/LightGlue/lightglue/utils.py @@ -0,0 +1,165 @@ +import collections.abc as collections +from pathlib import Path +from types import SimpleNamespace +from typing import Callable, List, Optional, Tuple, Union + +import cv2 +import kornia +import numpy as np +import torch + + +class ImagePreprocessor: + default_conf = { + "resize": None, # target edge length, None for no resizing + "side": "long", + "interpolation": "bilinear", + "align_corners": None, + "antialias": True, + } + + def __init__(self, **conf) -> None: + super().__init__() + self.conf = {**self.default_conf, **conf} + self.conf = SimpleNamespace(**self.conf) + + def __call__(self, img: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Resize and preprocess an image, return image and resize scale""" + h, w = img.shape[-2:] + if self.conf.resize is not None: + img = kornia.geometry.transform.resize( + img, + self.conf.resize, + side=self.conf.side, + antialias=self.conf.antialias, + align_corners=self.conf.align_corners, + ) + scale = torch.Tensor([img.shape[-1] / w, img.shape[-2] / h]).to(img) + return img, scale + + +def map_tensor(input_, func: Callable): + string_classes = (str, bytes) + if isinstance(input_, string_classes): + return input_ + elif isinstance(input_, collections.Mapping): + return {k: map_tensor(sample, func) for k, sample in input_.items()} + elif isinstance(input_, collections.Sequence): + return [map_tensor(sample, func) for sample in input_] + elif isinstance(input_, torch.Tensor): + return func(input_) + else: + return input_ + + +def batch_to_device(batch: dict, device: str = "cpu", non_blocking: bool = True): + """Move batch (dict) to device""" + + def _func(tensor): + return tensor.to(device=device, non_blocking=non_blocking).detach() + + return map_tensor(batch, _func) + + +def rbd(data: dict) -> dict: + """Remove batch dimension from elements in data""" + return { + k: v[0] if isinstance(v, (torch.Tensor, np.ndarray, list)) else v + for k, v in data.items() + } + + +def read_image(path: Path, grayscale: bool = False) -> np.ndarray: + """Read an image from path as RGB or grayscale""" + if not Path(path).exists(): + raise FileNotFoundError(f"No image at path {path}.") + mode = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR + image = cv2.imread(str(path), mode) + if image is None: + raise IOError(f"Could not read image at {path}.") + if not grayscale: + image = image[..., ::-1] + return image + + +def numpy_image_to_torch(image: np.ndarray) -> torch.Tensor: + """Normalize the image tensor and reorder the dimensions.""" + if image.ndim == 3: + image = image.transpose((2, 0, 1)) # HxWxC to CxHxW + elif image.ndim == 2: + image = image[None] # add channel axis + else: + raise ValueError(f"Not an image: {image.shape}") + return torch.tensor(image / 255.0, dtype=torch.float) + + +def resize_image( + image: np.ndarray, + size: Union[List[int], int], + fn: str = "max", + interp: Optional[str] = "area", +) -> np.ndarray: + """Resize an image to a fixed size, or according to max or min edge.""" + h, w = image.shape[:2] + + fn = {"max": max, "min": min}[fn] + if isinstance(size, int): + scale = size / fn(h, w) + h_new, w_new = int(round(h * scale)), int(round(w * scale)) + scale = (w_new / w, h_new / h) + elif isinstance(size, (tuple, list)): + h_new, w_new = size + scale = (w_new / w, h_new / h) + else: + raise ValueError(f"Incorrect new size: {size}") + mode = { + "linear": cv2.INTER_LINEAR, + "cubic": cv2.INTER_CUBIC, + "nearest": cv2.INTER_NEAREST, + "area": cv2.INTER_AREA, + }[interp] + return cv2.resize(image, (w_new, h_new), interpolation=mode), scale + + +def load_image(path: Path, resize: int = None, **kwargs) -> torch.Tensor: + image = read_image(path) + if resize is not None: + image, _ = resize_image(image, resize, **kwargs) + return numpy_image_to_torch(image) + + +class Extractor(torch.nn.Module): + def __init__(self, **conf): + super().__init__() + self.conf = SimpleNamespace(**{**self.default_conf, **conf}) + + @torch.no_grad() + def extract(self, img: torch.Tensor, **conf) -> dict: + """Perform extraction with online resizing""" + if img.dim() == 3: + img = img[None] # add batch dim + assert img.dim() == 4 and img.shape[0] == 1 + shape = img.shape[-2:][::-1] + img, scales = ImagePreprocessor(**{**self.preprocess_conf, **conf})(img) + feats = self.forward({"image": img}) + feats["image_size"] = torch.tensor(shape)[None].to(img).float() + feats["keypoints"] = (feats["keypoints"] + 0.5) / scales[None] - 0.5 + return feats + + +def match_pair( + extractor, + matcher, + image0: torch.Tensor, + image1: torch.Tensor, + device: str = "cpu", + **preprocess, +): + """Match a pair of images (image0, image1) with an extractor and matcher""" + feats0 = extractor.extract(image0, **preprocess) + feats1 = extractor.extract(image1, **preprocess) + matches01 = matcher({"image0": feats0, "image1": feats1}) + data = [feats0, feats1, matches01] + # remove batch dim and move to target device + feats0, feats1, matches01 = [batch_to_device(rbd(x), device) for x in data] + return feats0, feats1, matches01 diff --git a/LightGlue/lightglue/viz2d.py b/LightGlue/lightglue/viz2d.py new file mode 100644 index 0000000000000000000000000000000000000000..62af6b02a6f6813422e7e4513ea97a6c618ae23e --- /dev/null +++ b/LightGlue/lightglue/viz2d.py @@ -0,0 +1,185 @@ +""" +2D visualization primitives based on Matplotlib. +1) Plot images with `plot_images`. +2) Call `plot_keypoints` or `plot_matches` any number of times. +3) Optionally: save a .png or .pdf plot (nice in papers!) with `save_plot`. +""" + +import matplotlib +import matplotlib.patheffects as path_effects +import matplotlib.pyplot as plt +import numpy as np +import torch + + +def cm_RdGn(x): + """Custom colormap: red (0) -> yellow (0.5) -> green (1).""" + x = np.clip(x, 0, 1)[..., None] * 2 + c = x * np.array([[0, 1.0, 0]]) + (2 - x) * np.array([[1.0, 0, 0]]) + return np.clip(c, 0, 1) + + +def cm_BlRdGn(x_): + """Custom colormap: blue (-1) -> red (0.0) -> green (1).""" + x = np.clip(x_, 0, 1)[..., None] * 2 + c = x * np.array([[0, 1.0, 0, 1.0]]) + (2 - x) * np.array([[1.0, 0, 0, 1.0]]) + + xn = -np.clip(x_, -1, 0)[..., None] * 2 + cn = xn * np.array([[0, 0.1, 1, 1.0]]) + (2 - xn) * np.array([[1.0, 0, 0, 1.0]]) + out = np.clip(np.where(x_[..., None] < 0, cn, c), 0, 1) + return out + + +def cm_prune(x_): + """Custom colormap to visualize pruning""" + if isinstance(x_, torch.Tensor): + x_ = x_.cpu().numpy() + max_i = max(x_) + norm_x = np.where(x_ == max_i, -1, (x_ - 1) / 9) + return cm_BlRdGn(norm_x) + + +def plot_images(imgs, titles=None, cmaps="gray", dpi=100, pad=0.5, adaptive=True): + """Plot a set of images horizontally. + Args: + imgs: list of NumPy RGB (H, W, 3) or PyTorch RGB (3, H, W) or mono (H, W). + titles: a list of strings, as titles for each image. + cmaps: colormaps for monochrome images. + adaptive: whether the figure size should fit the image aspect ratios. + """ + # conversion to (H, W, 3) for torch.Tensor + imgs = [ + img.permute(1, 2, 0).cpu().numpy() + if (isinstance(img, torch.Tensor) and img.dim() == 3) + else img + for img in imgs + ] + + n = len(imgs) + if not isinstance(cmaps, (list, tuple)): + cmaps = [cmaps] * n + + if adaptive: + ratios = [i.shape[1] / i.shape[0] for i in imgs] # W / H + else: + ratios = [4 / 3] * n + figsize = [sum(ratios) * 4.5, 4.5] + fig, ax = plt.subplots( + 1, n, figsize=figsize, dpi=dpi, gridspec_kw={"width_ratios": ratios} + ) + if n == 1: + ax = [ax] + for i in range(n): + ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i])) + ax[i].get_yaxis().set_ticks([]) + ax[i].get_xaxis().set_ticks([]) + ax[i].set_axis_off() + for spine in ax[i].spines.values(): # remove frame + spine.set_visible(False) + if titles: + ax[i].set_title(titles[i]) + fig.tight_layout(pad=pad) + return fig, ax + + +def plot_keypoints(kpts, colors="lime", ps=4, axes=None, a=1.0): + """Plot keypoints for existing images. + Args: + kpts: list of ndarrays of size (N, 2). + colors: string, or list of list of tuples (one for each keypoints). + ps: size of the keypoints as float. + """ + if not isinstance(colors, list): + colors = [colors] * len(kpts) + if not isinstance(a, list): + a = [a] * len(kpts) + if axes is None: + axes = plt.gcf().axes + for ax, k, c, alpha in zip(axes, kpts, colors, a): + if isinstance(k, torch.Tensor): + k = k.cpu().numpy() + ax.scatter(k[:, 0], k[:, 1], c=c, s=ps, linewidths=0, alpha=alpha) + + +def plot_matches(kpts0, kpts1, color=None, lw=1.5, ps=4, a=1.0, labels=None, axes=None): + """Plot matches for a pair of existing images. + Args: + kpts0, kpts1: corresponding keypoints of size (N, 2). + color: color of each match, string or RGB tuple. Random if not given. + lw: width of the lines. + ps: size of the end points (no endpoint if ps=0) + indices: indices of the images to draw the matches on. + a: alpha opacity of the match lines. + """ + fig = plt.gcf() + if axes is None: + ax = fig.axes + ax0, ax1 = ax[0], ax[1] + else: + ax0, ax1 = axes + if isinstance(kpts0, torch.Tensor): + kpts0 = kpts0.cpu().numpy() + if isinstance(kpts1, torch.Tensor): + kpts1 = kpts1.cpu().numpy() + assert len(kpts0) == len(kpts1) + if color is None: + color = matplotlib.cm.hsv(np.random.rand(len(kpts0))).tolist() + elif len(color) > 0 and not isinstance(color[0], (tuple, list)): + color = [color] * len(kpts0) + + if lw > 0: + for i in range(len(kpts0)): + line = matplotlib.patches.ConnectionPatch( + xyA=(kpts0[i, 0], kpts0[i, 1]), + xyB=(kpts1[i, 0], kpts1[i, 1]), + coordsA=ax0.transData, + coordsB=ax1.transData, + axesA=ax0, + axesB=ax1, + zorder=1, + color=color[i], + linewidth=lw, + clip_on=True, + alpha=a, + label=None if labels is None else labels[i], + picker=5.0, + ) + line.set_annotation_clip(True) + fig.add_artist(line) + + # freeze the axes to prevent the transform to change + ax0.autoscale(enable=False) + ax1.autoscale(enable=False) + + if ps > 0: + ax0.scatter(kpts0[:, 0], kpts0[:, 1], c=color, s=ps) + ax1.scatter(kpts1[:, 0], kpts1[:, 1], c=color, s=ps) + + +def add_text( + idx, + text, + pos=(0.01, 0.99), + fs=15, + color="w", + lcolor="k", + lwidth=2, + ha="left", + va="top", +): + ax = plt.gcf().axes[idx] + t = ax.text( + *pos, text, fontsize=fs, ha=ha, va=va, color=color, transform=ax.transAxes + ) + if lcolor is not None: + t.set_path_effects( + [ + path_effects.Stroke(linewidth=lwidth, foreground=lcolor), + path_effects.Normal(), + ] + ) + + +def save_plot(path, **kw): + """Save the current figure without any white margin.""" + plt.savefig(path, bbox_inches="tight", pad_inches=0, **kw) diff --git a/LightGlue/pyproject.toml b/LightGlue/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..2744fbaaccc6361e210a4dbd32d62b51c3245c73 --- /dev/null +++ b/LightGlue/pyproject.toml @@ -0,0 +1,30 @@ +[project] +name = "lightglue" +description = "LightGlue: Local Feature Matching at Light Speed" +version = "0.0" +authors = [ + {name = "Philipp Lindenberger"}, + {name = "Paul-Edouard Sarlin"}, +] +readme = "README.md" +requires-python = ">=3.6" +license = {file = "LICENSE"} +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", +] +urls = {Repository = "https://github.com/cvg/LightGlue/"} +dynamic = ["dependencies"] + +[project.optional-dependencies] +dev = ["black==23.12.1", "flake8", "isort"] + +[tool.setuptools] +packages = ["lightglue"] + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} + +[tool.isort] +profile = "black" diff --git a/LightGlue/requirements.txt b/LightGlue/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e08ed4c109de395cbff65f76db43f866356a02d --- /dev/null +++ b/LightGlue/requirements.txt @@ -0,0 +1,6 @@ +# torch>=1.9.1 +# torchvision>=0.3 +# numpy +# opencv-python +# matplotlib +# kornia>=0.6.11 \ No newline at end of file diff --git a/ORIGINAL_README.md b/ORIGINAL_README.md new file mode 100644 index 0000000000000000000000000000000000000000..77bdaaa2604e8941c3b04c369810cb39cbe6877c --- /dev/null +++ b/ORIGINAL_README.md @@ -0,0 +1,115 @@ +# AniDoc: Animation Creation Made Easier + + + + + +https://github.com/user-attachments/assets/99e1e52a-f0e1-49f5-b81f-e787857901e4 + + + + +> **AniDoc: Animation Creation Made Easier** +> + +[Yihao Meng](https://yihao-meng.github.io/)1,2, [Hao Ouyang](https://ken-ouyang.github.io/)2, [Hanlin Wang](https://openreview.net/profile?id=~Hanlin_Wang2)3,2, [Qiuyu Wang](https://github.com/qiuyu96)2, [Wen Wang](https://github.com/encounter1997)4,2, [Ka Leong Cheng](https://felixcheng97.github.io/)1,2 , [Zhiheng Liu](https://johanan528.github.io/)5, [Yujun Shen](https://shenyujun.github.io/)2, [Huamin Qu](http://www.huamin.org/index.htm/)†,2
+1HKUST 2Ant Group 3NJU 4ZJU 5HKU †corresponding author + +> AniDoc colorizes a sequence of sketches based on a character design reference with high fidelity, even when the sketches significantly differ in pose and scale. +

+ +**Strongly recommend seeing our [demo page](https://yihao-meng.github.io/AniDoc_demo).** + + +## Showcases: +

+ GIF +

+

+ GIF +

+

+ GIF +

+

+ GIF +

+ +## Flexible Usage: +### Same Reference with Varying Sketches +
+GIF Animation +GIF Animation +GIF Animation +
+ Satoru Gojo from Jujutsu Kaisen +
+
+ +### Same Sketch with Different References. + +
+GIF Animation + +GIF Animation +GIF Animation +
+ Anya Forger from Spy x Family +
+
+ +## TODO List + +- [x] Release the paper and demo page. Visit [https://yihao-meng.github.io/AniDoc_demo/](https://yihao-meng.github.io/AniDoc_demo/) +- [x] Release the inference code. +- [ ] Build Gradio Demo +- [ ] Release the training code. +- [ ] Release the sparse sketch setting interpolation code. + + +## Requirements: +The training is conducted on 8 A100 GPUs (80GB VRAM), the inference is tested on RTX 5000 (32GB VRAM). In our test, the inference requires about 14GB VRAM. +## Setup +``` +git clone https://github.com/yihao-meng/AniDoc.git +cd AniDoc +``` + +## Environment +All the tests are conducted in Linux. We suggest running our code in Linux. To set up our environment in Linux, please run: +``` +conda create -n anidoc python=3.8 -y +conda activate anidoc + +bash install.sh +``` +## Checkpoints +1. please download the pre-trained stable video diffusion (SVD) checkpoints from [here](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/tree/main), and put the whole folder under `pretrained_weight`, it should look like `./pretrained_weights/stable-video-diffusion-img2vid-xt` +2. please download the checkpoint for our Unet and ControlNet from [here](https://huggingface.co/Yhmeng1106/anidoc/tree/main), and put the whole folder as `./pretrained_weights/anidoc`. +3. please download the co_tracker checkpoint from [here](https://huggingface.co/facebook/cotracker/blob/main/cotracker2.pth) and put it as `./pretrained_weights/cotracker2.pth`. + + + + +## Generate Your Animation! +To colorize the target lineart sequence with a specific character design, you can run the following command: +``` +bash scripts_infer/anidoc_inference.sh +``` + + +We provide some test cases in `data_test` folder. You can also try our model with your own data. You can change the lineart sequence and corresponding character design in the script `anidoc_inference.sh`, where `--control_image` refers to the lineart sequence and `--ref_image` refers to the character design. + + + +## Citation: +Don't forget to cite this source if it proves useful in your research! +```bibtex +@article{meng2024anidoc, + title={AniDoc: Animation Creation Made Easier}, + author={Yihao Meng and Hao Ouyang and Hanlin Wang and Qiuyu Wang and Wen Wang and Ka Leong Cheng and Zhiheng Liu and Yujun Shen and Huamin Qu}, + journal={arXiv preprint arXiv:2412.14173}, + year={2024} +} + +``` diff --git a/cotracker/__init__.py b/cotracker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/datasets/__init__.py b/cotracker/build/lib/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/datasets/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/datasets/dataclass_utils.py b/cotracker/build/lib/datasets/dataclass_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..11e103b6002b4ecf72b463a829fe16d31cc65cff --- /dev/null +++ b/cotracker/build/lib/datasets/dataclass_utils.py @@ -0,0 +1,166 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import json +import dataclasses +import numpy as np +from dataclasses import Field, MISSING +from typing import IO, TypeVar, Type, get_args, get_origin, Union, Any, Tuple + +_X = TypeVar("_X") + + +def load_dataclass(f: IO, cls: Type[_X], binary: bool = False) -> _X: + """ + Loads to a @dataclass or collection hierarchy including dataclasses + from a json recursively. + Call it like load_dataclass(f, typing.List[FrameAnnotationAnnotation]). + raises KeyError if json has keys not mapping to the dataclass fields. + + Args: + f: Either a path to a file, or a file opened for writing. + cls: The class of the loaded dataclass. + binary: Set to True if `f` is a file handle, else False. + """ + if binary: + asdict = json.loads(f.read().decode("utf8")) + else: + asdict = json.load(f) + + # in the list case, run a faster "vectorized" version + cls = get_args(cls)[0] + res = list(_dataclass_list_from_dict_list(asdict, cls)) + + return res + + +def _resolve_optional(type_: Any) -> Tuple[bool, Any]: + """Check whether `type_` is equivalent to `typing.Optional[T]` for some T.""" + if get_origin(type_) is Union: + args = get_args(type_) + if len(args) == 2 and args[1] == type(None): # noqa E721 + return True, args[0] + if type_ is Any: + return True, Any + + return False, type_ + + +def _unwrap_type(tp): + # strips Optional wrapper, if any + if get_origin(tp) is Union: + args = get_args(tp) + if len(args) == 2 and any(a is type(None) for a in args): # noqa: E721 + # this is typing.Optional + return args[0] if args[1] is type(None) else args[1] # noqa: E721 + return tp + + +def _get_dataclass_field_default(field: Field) -> Any: + if field.default_factory is not MISSING: + # pyre-fixme[29]: `Union[dataclasses._MISSING_TYPE, + # dataclasses._DefaultFactory[typing.Any]]` is not a function. + return field.default_factory() + elif field.default is not MISSING: + return field.default + else: + return None + + +def _dataclass_list_from_dict_list(dlist, typeannot): + """ + Vectorised version of `_dataclass_from_dict`. + The output should be equivalent to + `[_dataclass_from_dict(d, typeannot) for d in dlist]`. + + Args: + dlist: list of objects to convert. + typeannot: type of each of those objects. + Returns: + iterator or list over converted objects of the same length as `dlist`. + + Raises: + ValueError: it assumes the objects have None's in consistent places across + objects, otherwise it would ignore some values. This generally holds for + auto-generated annotations, but otherwise use `_dataclass_from_dict`. + """ + + cls = get_origin(typeannot) or typeannot + + if typeannot is Any: + return dlist + if all(obj is None for obj in dlist): # 1st recursion base: all None nodes + return dlist + if any(obj is None for obj in dlist): + # filter out Nones and recurse on the resulting list + idx_notnone = [(i, obj) for i, obj in enumerate(dlist) if obj is not None] + idx, notnone = zip(*idx_notnone) + converted = _dataclass_list_from_dict_list(notnone, typeannot) + res = [None] * len(dlist) + for i, obj in zip(idx, converted): + res[i] = obj + return res + + is_optional, contained_type = _resolve_optional(typeannot) + if is_optional: + return _dataclass_list_from_dict_list(dlist, contained_type) + + # otherwise, we dispatch by the type of the provided annotation to convert to + if issubclass(cls, tuple) and hasattr(cls, "_fields"): # namedtuple + # For namedtuple, call the function recursively on the lists of corresponding keys + types = cls.__annotations__.values() + dlist_T = zip(*dlist) + res_T = [ + _dataclass_list_from_dict_list(key_list, tp) for key_list, tp in zip(dlist_T, types) + ] + return [cls(*converted_as_tuple) for converted_as_tuple in zip(*res_T)] + elif issubclass(cls, (list, tuple)): + # For list/tuple, call the function recursively on the lists of corresponding positions + types = get_args(typeannot) + if len(types) == 1: # probably List; replicate for all items + types = types * len(dlist[0]) + dlist_T = zip(*dlist) + res_T = ( + _dataclass_list_from_dict_list(pos_list, tp) for pos_list, tp in zip(dlist_T, types) + ) + if issubclass(cls, tuple): + return list(zip(*res_T)) + else: + return [cls(converted_as_tuple) for converted_as_tuple in zip(*res_T)] + elif issubclass(cls, dict): + # For the dictionary, call the function recursively on concatenated keys and vertices + key_t, val_t = get_args(typeannot) + all_keys_res = _dataclass_list_from_dict_list( + [k for obj in dlist for k in obj.keys()], key_t + ) + all_vals_res = _dataclass_list_from_dict_list( + [k for obj in dlist for k in obj.values()], val_t + ) + indices = np.cumsum([len(obj) for obj in dlist]) + assert indices[-1] == len(all_keys_res) + + keys = np.split(list(all_keys_res), indices[:-1]) + all_vals_res_iter = iter(all_vals_res) + return [cls(zip(k, all_vals_res_iter)) for k in keys] + elif not dataclasses.is_dataclass(typeannot): + return dlist + + # dataclass node: 2nd recursion base; call the function recursively on the lists + # of the corresponding fields + assert dataclasses.is_dataclass(cls) + fieldtypes = { + f.name: (_unwrap_type(f.type), _get_dataclass_field_default(f)) + for f in dataclasses.fields(typeannot) + } + + # NOTE the default object is shared here + key_lists = ( + _dataclass_list_from_dict_list([obj.get(k, default) for obj in dlist], type_) + for k, (type_, default) in fieldtypes.items() + ) + transposed = zip(*key_lists) + return [cls(*vals_as_tuple) for vals_as_tuple in transposed] diff --git a/cotracker/build/lib/datasets/dr_dataset.py b/cotracker/build/lib/datasets/dr_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..70af653e8852ae4b70776beba3bf12a324723f5a --- /dev/null +++ b/cotracker/build/lib/datasets/dr_dataset.py @@ -0,0 +1,161 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import gzip +import torch +import numpy as np +import torch.utils.data as data +from collections import defaultdict +from dataclasses import dataclass +from typing import List, Optional, Any, Dict, Tuple + +from cotracker.datasets.utils import CoTrackerData +from cotracker.datasets.dataclass_utils import load_dataclass + + +@dataclass +class ImageAnnotation: + # path to jpg file, relative w.r.t. dataset_root + path: str + # H x W + size: Tuple[int, int] + + +@dataclass +class DynamicReplicaFrameAnnotation: + """A dataclass used to load annotations from json.""" + + # can be used to join with `SequenceAnnotation` + sequence_name: str + # 0-based, continuous frame number within sequence + frame_number: int + # timestamp in seconds from the video start + frame_timestamp: float + + image: ImageAnnotation + meta: Optional[Dict[str, Any]] = None + + camera_name: Optional[str] = None + trajectories: Optional[str] = None + + +class DynamicReplicaDataset(data.Dataset): + def __init__( + self, + root, + split="valid", + traj_per_sample=256, + crop_size=None, + sample_len=-1, + only_first_n_samples=-1, + rgbd_input=False, + ): + super(DynamicReplicaDataset, self).__init__() + self.root = root + self.sample_len = sample_len + self.split = split + self.traj_per_sample = traj_per_sample + self.rgbd_input = rgbd_input + self.crop_size = crop_size + frame_annotations_file = f"frame_annotations_{split}.jgz" + self.sample_list = [] + with gzip.open( + os.path.join(root, split, frame_annotations_file), "rt", encoding="utf8" + ) as zipfile: + frame_annots_list = load_dataclass(zipfile, List[DynamicReplicaFrameAnnotation]) + seq_annot = defaultdict(list) + for frame_annot in frame_annots_list: + if frame_annot.camera_name == "left": + seq_annot[frame_annot.sequence_name].append(frame_annot) + + for seq_name in seq_annot.keys(): + seq_len = len(seq_annot[seq_name]) + + step = self.sample_len if self.sample_len > 0 else seq_len + counter = 0 + + for ref_idx in range(0, seq_len, step): + sample = seq_annot[seq_name][ref_idx : ref_idx + step] + self.sample_list.append(sample) + counter += 1 + if only_first_n_samples > 0 and counter >= only_first_n_samples: + break + + def __len__(self): + return len(self.sample_list) + + def crop(self, rgbs, trajs): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + H_new = H + W_new = W + + # simple random crop + y0 = 0 if self.crop_size[0] >= H_new else (H_new - self.crop_size[0]) // 2 + x0 = 0 if self.crop_size[1] >= W_new else (W_new - self.crop_size[1]) // 2 + rgbs = [rgb[y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] for rgb in rgbs] + + trajs[:, :, 0] -= x0 + trajs[:, :, 1] -= y0 + + return rgbs, trajs + + def __getitem__(self, index): + sample = self.sample_list[index] + T = len(sample) + rgbs, visibilities, traj_2d = [], [], [] + + H, W = sample[0].image.size + image_size = (H, W) + + for i in range(T): + traj_path = os.path.join(self.root, self.split, sample[i].trajectories["path"]) + traj = torch.load(traj_path) + + visibilities.append(traj["verts_inds_vis"].numpy()) + + rgbs.append(traj["img"].numpy()) + traj_2d.append(traj["traj_2d"].numpy()[..., :2]) + + traj_2d = np.stack(traj_2d) + visibility = np.stack(visibilities) + T, N, D = traj_2d.shape + # subsample trajectories for augmentations + visible_inds_sampled = torch.randperm(N)[: self.traj_per_sample] + + traj_2d = traj_2d[:, visible_inds_sampled] + visibility = visibility[:, visible_inds_sampled] + + if self.crop_size is not None: + rgbs, traj_2d = self.crop(rgbs, traj_2d) + H, W, _ = rgbs[0].shape + image_size = self.crop_size + + visibility[traj_2d[:, :, 0] > image_size[1] - 1] = False + visibility[traj_2d[:, :, 0] < 0] = False + visibility[traj_2d[:, :, 1] > image_size[0] - 1] = False + visibility[traj_2d[:, :, 1] < 0] = False + + # filter out points that're visible for less than 10 frames + visible_inds_resampled = visibility.sum(0) > 10 + traj_2d = torch.from_numpy(traj_2d[:, visible_inds_resampled]) + visibility = torch.from_numpy(visibility[:, visible_inds_resampled]) + + rgbs = np.stack(rgbs, 0) + video = torch.from_numpy(rgbs).reshape(T, H, W, 3).permute(0, 3, 1, 2).float() + return CoTrackerData( + video=video, + trajectory=traj_2d, + visibility=visibility, + valid=torch.ones(T, N), + seq_name=sample[0].sequence_name, + ) diff --git a/cotracker/build/lib/datasets/kubric_movif_dataset.py b/cotracker/build/lib/datasets/kubric_movif_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..366d7383e2797359500508448806f39d8b298ac5 --- /dev/null +++ b/cotracker/build/lib/datasets/kubric_movif_dataset.py @@ -0,0 +1,441 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import cv2 + +import imageio +import numpy as np + +from cotracker.datasets.utils import CoTrackerData +from torchvision.transforms import ColorJitter, GaussianBlur +from PIL import Image + + +class CoTrackerDataset(torch.utils.data.Dataset): + def __init__( + self, + data_root, + crop_size=(384, 512), + seq_len=24, + traj_per_sample=768, + sample_vis_1st_frame=False, + use_augs=False, + ): + super(CoTrackerDataset, self).__init__() + np.random.seed(0) + torch.manual_seed(0) + self.data_root = data_root + self.seq_len = seq_len + self.traj_per_sample = traj_per_sample + self.sample_vis_1st_frame = sample_vis_1st_frame + self.use_augs = use_augs + self.crop_size = crop_size + + # photometric augmentation + self.photo_aug = ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.25 / 3.14) + self.blur_aug = GaussianBlur(11, sigma=(0.1, 2.0)) + + self.blur_aug_prob = 0.25 + self.color_aug_prob = 0.25 + + # occlusion augmentation + self.eraser_aug_prob = 0.5 + self.eraser_bounds = [2, 100] + self.eraser_max = 10 + + # occlusion augmentation + self.replace_aug_prob = 0.5 + self.replace_bounds = [2, 100] + self.replace_max = 10 + + # spatial augmentations + self.pad_bounds = [0, 100] + self.crop_size = crop_size + self.resize_lim = [0.25, 2.0] # sample resizes from here + self.resize_delta = 0.2 + self.max_crop_offset = 50 + + self.do_flip = True + self.h_flip_prob = 0.5 + self.v_flip_prob = 0.5 + + def getitem_helper(self, index): + return NotImplementedError + + def __getitem__(self, index): + gotit = False + + sample, gotit = self.getitem_helper(index) + if not gotit: + print("warning: sampling failed") + # fake sample, so we can still collate + sample = CoTrackerData( + video=torch.zeros((self.seq_len, 3, self.crop_size[0], self.crop_size[1])), + trajectory=torch.zeros((self.seq_len, self.traj_per_sample, 2)), + visibility=torch.zeros((self.seq_len, self.traj_per_sample)), + valid=torch.zeros((self.seq_len, self.traj_per_sample)), + ) + + return sample, gotit + + def add_photometric_augs(self, rgbs, trajs, visibles, eraser=True, replace=True): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + if eraser: + ############ eraser transform (per image after the first) ############ + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + for i in range(1, S): + if np.random.rand() < self.eraser_aug_prob: + for _ in range( + np.random.randint(1, self.eraser_max + 1) + ): # number of times to occlude + xc = np.random.randint(0, W) + yc = np.random.randint(0, H) + dx = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1]) + dy = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1]) + x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32) + x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32) + y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32) + y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32) + + mean_color = np.mean(rgbs[i][y0:y1, x0:x1, :].reshape(-1, 3), axis=0) + rgbs[i][y0:y1, x0:x1, :] = mean_color + + occ_inds = np.logical_and( + np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1), + np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1), + ) + visibles[i, occ_inds] = 0 + rgbs = [rgb.astype(np.uint8) for rgb in rgbs] + + if replace: + rgbs_alt = [ + np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs + ] + rgbs_alt = [ + np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs_alt + ] + + ############ replace transform (per image after the first) ############ + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + rgbs_alt = [rgb.astype(np.float32) for rgb in rgbs_alt] + for i in range(1, S): + if np.random.rand() < self.replace_aug_prob: + for _ in range( + np.random.randint(1, self.replace_max + 1) + ): # number of times to occlude + xc = np.random.randint(0, W) + yc = np.random.randint(0, H) + dx = np.random.randint(self.replace_bounds[0], self.replace_bounds[1]) + dy = np.random.randint(self.replace_bounds[0], self.replace_bounds[1]) + x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32) + x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32) + y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32) + y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32) + + wid = x1 - x0 + hei = y1 - y0 + y00 = np.random.randint(0, H - hei) + x00 = np.random.randint(0, W - wid) + fr = np.random.randint(0, S) + rep = rgbs_alt[fr][y00 : y00 + hei, x00 : x00 + wid, :] + rgbs[i][y0:y1, x0:x1, :] = rep + + occ_inds = np.logical_and( + np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1), + np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1), + ) + visibles[i, occ_inds] = 0 + rgbs = [rgb.astype(np.uint8) for rgb in rgbs] + + ############ photometric augmentation ############ + if np.random.rand() < self.color_aug_prob: + # random per-frame amount of aug + rgbs = [np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs] + + if np.random.rand() < self.blur_aug_prob: + # random per-frame amount of blur + rgbs = [np.array(self.blur_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs] + + return rgbs, trajs, visibles + + def add_spatial_augs(self, rgbs, trajs, visibles): + T, N, __ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + + ############ spatial transform ############ + + # padding + pad_x0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_x1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_y0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_y1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + + rgbs = [np.pad(rgb, ((pad_y0, pad_y1), (pad_x0, pad_x1), (0, 0))) for rgb in rgbs] + trajs[:, :, 0] += pad_x0 + trajs[:, :, 1] += pad_y0 + H, W = rgbs[0].shape[:2] + + # scaling + stretching + scale = np.random.uniform(self.resize_lim[0], self.resize_lim[1]) + scale_x = scale + scale_y = scale + H_new = H + W_new = W + + scale_delta_x = 0.0 + scale_delta_y = 0.0 + + rgbs_scaled = [] + for s in range(S): + if s == 1: + scale_delta_x = np.random.uniform(-self.resize_delta, self.resize_delta) + scale_delta_y = np.random.uniform(-self.resize_delta, self.resize_delta) + elif s > 1: + scale_delta_x = ( + scale_delta_x * 0.8 + + np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2 + ) + scale_delta_y = ( + scale_delta_y * 0.8 + + np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2 + ) + scale_x = scale_x + scale_delta_x + scale_y = scale_y + scale_delta_y + + # bring h/w closer + scale_xy = (scale_x + scale_y) * 0.5 + scale_x = scale_x * 0.5 + scale_xy * 0.5 + scale_y = scale_y * 0.5 + scale_xy * 0.5 + + # don't get too crazy + scale_x = np.clip(scale_x, 0.2, 2.0) + scale_y = np.clip(scale_y, 0.2, 2.0) + + H_new = int(H * scale_y) + W_new = int(W * scale_x) + + # make it at least slightly bigger than the crop area, + # so that the random cropping can add diversity + H_new = np.clip(H_new, self.crop_size[0] + 10, None) + W_new = np.clip(W_new, self.crop_size[1] + 10, None) + # recompute scale in case we clipped + scale_x = (W_new - 1) / float(W - 1) + scale_y = (H_new - 1) / float(H - 1) + rgbs_scaled.append(cv2.resize(rgbs[s], (W_new, H_new), interpolation=cv2.INTER_LINEAR)) + trajs[s, :, 0] *= scale_x + trajs[s, :, 1] *= scale_y + rgbs = rgbs_scaled + + ok_inds = visibles[0, :] > 0 + vis_trajs = trajs[:, ok_inds] # S,?,2 + + if vis_trajs.shape[1] > 0: + mid_x = np.mean(vis_trajs[0, :, 0]) + mid_y = np.mean(vis_trajs[0, :, 1]) + else: + mid_y = self.crop_size[0] + mid_x = self.crop_size[1] + + x0 = int(mid_x - self.crop_size[1] // 2) + y0 = int(mid_y - self.crop_size[0] // 2) + + offset_x = 0 + offset_y = 0 + + for s in range(S): + # on each frame, shift a bit more + if s == 1: + offset_x = np.random.randint(-self.max_crop_offset, self.max_crop_offset) + offset_y = np.random.randint(-self.max_crop_offset, self.max_crop_offset) + elif s > 1: + offset_x = int( + offset_x * 0.8 + + np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2 + ) + offset_y = int( + offset_y * 0.8 + + np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2 + ) + x0 = x0 + offset_x + y0 = y0 + offset_y + + H_new, W_new = rgbs[s].shape[:2] + if H_new == self.crop_size[0]: + y0 = 0 + else: + y0 = min(max(0, y0), H_new - self.crop_size[0] - 1) + + if W_new == self.crop_size[1]: + x0 = 0 + else: + x0 = min(max(0, x0), W_new - self.crop_size[1] - 1) + + rgbs[s] = rgbs[s][y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] + trajs[s, :, 0] -= x0 + trajs[s, :, 1] -= y0 + + H_new = self.crop_size[0] + W_new = self.crop_size[1] + + # flip + h_flipped = False + v_flipped = False + if self.do_flip: + # h flip + if np.random.rand() < self.h_flip_prob: + h_flipped = True + rgbs = [rgb[:, ::-1] for rgb in rgbs] + # v flip + if np.random.rand() < self.v_flip_prob: + v_flipped = True + rgbs = [rgb[::-1] for rgb in rgbs] + if h_flipped: + trajs[:, :, 0] = W_new - trajs[:, :, 0] + if v_flipped: + trajs[:, :, 1] = H_new - trajs[:, :, 1] + + return rgbs, trajs + + def crop(self, rgbs, trajs): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + ############ spatial transform ############ + + H_new = H + W_new = W + + # simple random crop + y0 = 0 if self.crop_size[0] >= H_new else np.random.randint(0, H_new - self.crop_size[0]) + x0 = 0 if self.crop_size[1] >= W_new else np.random.randint(0, W_new - self.crop_size[1]) + rgbs = [rgb[y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] for rgb in rgbs] + + trajs[:, :, 0] -= x0 + trajs[:, :, 1] -= y0 + + return rgbs, trajs + + +class KubricMovifDataset(CoTrackerDataset): + def __init__( + self, + data_root, + crop_size=(384, 512), + seq_len=24, + traj_per_sample=768, + sample_vis_1st_frame=False, + use_augs=False, + ): + super(KubricMovifDataset, self).__init__( + data_root=data_root, + crop_size=crop_size, + seq_len=seq_len, + traj_per_sample=traj_per_sample, + sample_vis_1st_frame=sample_vis_1st_frame, + use_augs=use_augs, + ) + + self.pad_bounds = [0, 25] + self.resize_lim = [0.75, 1.25] # sample resizes from here + self.resize_delta = 0.05 + self.max_crop_offset = 15 + self.seq_names = [ + fname + for fname in os.listdir(data_root) + if os.path.isdir(os.path.join(data_root, fname)) + ] + print("found %d unique videos in %s" % (len(self.seq_names), self.data_root)) + + def getitem_helper(self, index): + gotit = True + seq_name = self.seq_names[index] + + npy_path = os.path.join(self.data_root, seq_name, seq_name + ".npy") + rgb_path = os.path.join(self.data_root, seq_name, "frames") + + img_paths = sorted(os.listdir(rgb_path)) + rgbs = [] + for i, img_path in enumerate(img_paths): + rgbs.append(imageio.v2.imread(os.path.join(rgb_path, img_path))) + + rgbs = np.stack(rgbs) + annot_dict = np.load(npy_path, allow_pickle=True).item() + traj_2d = annot_dict["coords"] + visibility = annot_dict["visibility"] + + # random crop + assert self.seq_len <= len(rgbs) + if self.seq_len < len(rgbs): + start_ind = np.random.choice(len(rgbs) - self.seq_len, 1)[0] + + rgbs = rgbs[start_ind : start_ind + self.seq_len] + traj_2d = traj_2d[:, start_ind : start_ind + self.seq_len] + visibility = visibility[:, start_ind : start_ind + self.seq_len] + + traj_2d = np.transpose(traj_2d, (1, 0, 2)) + visibility = np.transpose(np.logical_not(visibility), (1, 0)) + if self.use_augs: + rgbs, traj_2d, visibility = self.add_photometric_augs(rgbs, traj_2d, visibility) + rgbs, traj_2d = self.add_spatial_augs(rgbs, traj_2d, visibility) + else: + rgbs, traj_2d = self.crop(rgbs, traj_2d) + + visibility[traj_2d[:, :, 0] > self.crop_size[1] - 1] = False + visibility[traj_2d[:, :, 0] < 0] = False + visibility[traj_2d[:, :, 1] > self.crop_size[0] - 1] = False + visibility[traj_2d[:, :, 1] < 0] = False + + visibility = torch.from_numpy(visibility) + traj_2d = torch.from_numpy(traj_2d) + + visibile_pts_first_frame_inds = (visibility[0]).nonzero(as_tuple=False)[:, 0] + + if self.sample_vis_1st_frame: + visibile_pts_inds = visibile_pts_first_frame_inds + else: + visibile_pts_mid_frame_inds = (visibility[self.seq_len // 2]).nonzero(as_tuple=False)[ + :, 0 + ] + visibile_pts_inds = torch.cat( + (visibile_pts_first_frame_inds, visibile_pts_mid_frame_inds), dim=0 + ) + point_inds = torch.randperm(len(visibile_pts_inds))[: self.traj_per_sample] + if len(point_inds) < self.traj_per_sample: + gotit = False + + visible_inds_sampled = visibile_pts_inds[point_inds] + + trajs = traj_2d[:, visible_inds_sampled].float() + visibles = visibility[:, visible_inds_sampled] + valids = torch.ones((self.seq_len, self.traj_per_sample)) + + rgbs = torch.from_numpy(np.stack(rgbs)).permute(0, 3, 1, 2).float() + sample = CoTrackerData( + video=rgbs, + trajectory=trajs, + visibility=visibles, + valid=valids, + seq_name=seq_name, + ) + return sample, gotit + + def __len__(self): + return len(self.seq_names) diff --git a/cotracker/build/lib/datasets/tap_vid_datasets.py b/cotracker/build/lib/datasets/tap_vid_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..72e000177c95fb54b1dba22d2dd96e9db9f0096e --- /dev/null +++ b/cotracker/build/lib/datasets/tap_vid_datasets.py @@ -0,0 +1,209 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import io +import glob +import torch +import pickle +import numpy as np +import mediapy as media + +from PIL import Image +from typing import Mapping, Tuple, Union + +from cotracker.datasets.utils import CoTrackerData + +DatasetElement = Mapping[str, Mapping[str, Union[np.ndarray, str]]] + + +def resize_video(video: np.ndarray, output_size: Tuple[int, int]) -> np.ndarray: + """Resize a video to output_size.""" + # If you have a GPU, consider replacing this with a GPU-enabled resize op, + # such as a jitted jax.image.resize. It will make things faster. + return media.resize_video(video, output_size) + + +def sample_queries_first( + target_occluded: np.ndarray, + target_points: np.ndarray, + frames: np.ndarray, +) -> Mapping[str, np.ndarray]: + """Package a set of frames and tracks for use in TAPNet evaluations. + Given a set of frames and tracks with no query points, use the first + visible point in each track as the query. + Args: + target_occluded: Boolean occlusion flag, of shape [n_tracks, n_frames], + where True indicates occluded. + target_points: Position, of shape [n_tracks, n_frames, 2], where each point + is [x,y] scaled between 0 and 1. + frames: Video tensor, of shape [n_frames, height, width, 3]. Scaled between + -1 and 1. + Returns: + A dict with the keys: + video: Video tensor of shape [1, n_frames, height, width, 3] + query_points: Query points of shape [1, n_queries, 3] where + each point is [t, y, x] scaled to the range [-1, 1] + target_points: Target points of shape [1, n_queries, n_frames, 2] where + each point is [x, y] scaled to the range [-1, 1] + """ + valid = np.sum(~target_occluded, axis=1) > 0 + target_points = target_points[valid, :] + target_occluded = target_occluded[valid, :] + + query_points = [] + for i in range(target_points.shape[0]): + index = np.where(target_occluded[i] == 0)[0][0] + x, y = target_points[i, index, 0], target_points[i, index, 1] + query_points.append(np.array([index, y, x])) # [t, y, x] + query_points = np.stack(query_points, axis=0) + + return { + "video": frames[np.newaxis, ...], + "query_points": query_points[np.newaxis, ...], + "target_points": target_points[np.newaxis, ...], + "occluded": target_occluded[np.newaxis, ...], + } + + +def sample_queries_strided( + target_occluded: np.ndarray, + target_points: np.ndarray, + frames: np.ndarray, + query_stride: int = 5, +) -> Mapping[str, np.ndarray]: + """Package a set of frames and tracks for use in TAPNet evaluations. + + Given a set of frames and tracks with no query points, sample queries + strided every query_stride frames, ignoring points that are not visible + at the selected frames. + + Args: + target_occluded: Boolean occlusion flag, of shape [n_tracks, n_frames], + where True indicates occluded. + target_points: Position, of shape [n_tracks, n_frames, 2], where each point + is [x,y] scaled between 0 and 1. + frames: Video tensor, of shape [n_frames, height, width, 3]. Scaled between + -1 and 1. + query_stride: When sampling query points, search for un-occluded points + every query_stride frames and convert each one into a query. + + Returns: + A dict with the keys: + video: Video tensor of shape [1, n_frames, height, width, 3]. The video + has floats scaled to the range [-1, 1]. + query_points: Query points of shape [1, n_queries, 3] where + each point is [t, y, x] scaled to the range [-1, 1]. + target_points: Target points of shape [1, n_queries, n_frames, 2] where + each point is [x, y] scaled to the range [-1, 1]. + trackgroup: Index of the original track that each query point was + sampled from. This is useful for visualization. + """ + tracks = [] + occs = [] + queries = [] + trackgroups = [] + total = 0 + trackgroup = np.arange(target_occluded.shape[0]) + for i in range(0, target_occluded.shape[1], query_stride): + mask = target_occluded[:, i] == 0 + query = np.stack( + [ + i * np.ones(target_occluded.shape[0:1]), + target_points[:, i, 1], + target_points[:, i, 0], + ], + axis=-1, + ) + queries.append(query[mask]) + tracks.append(target_points[mask]) + occs.append(target_occluded[mask]) + trackgroups.append(trackgroup[mask]) + total += np.array(np.sum(target_occluded[:, i] == 0)) + + return { + "video": frames[np.newaxis, ...], + "query_points": np.concatenate(queries, axis=0)[np.newaxis, ...], + "target_points": np.concatenate(tracks, axis=0)[np.newaxis, ...], + "occluded": np.concatenate(occs, axis=0)[np.newaxis, ...], + "trackgroup": np.concatenate(trackgroups, axis=0)[np.newaxis, ...], + } + + +class TapVidDataset(torch.utils.data.Dataset): + def __init__( + self, + data_root, + dataset_type="davis", + resize_to_256=True, + queried_first=True, + ): + self.dataset_type = dataset_type + self.resize_to_256 = resize_to_256 + self.queried_first = queried_first + if self.dataset_type == "kinetics": + all_paths = glob.glob(os.path.join(data_root, "*_of_0010.pkl")) + points_dataset = [] + for pickle_path in all_paths: + with open(pickle_path, "rb") as f: + data = pickle.load(f) + points_dataset = points_dataset + data + self.points_dataset = points_dataset + else: + with open(data_root, "rb") as f: + self.points_dataset = pickle.load(f) + if self.dataset_type == "davis": + self.video_names = list(self.points_dataset.keys()) + print("found %d unique videos in %s" % (len(self.points_dataset), data_root)) + + def __getitem__(self, index): + if self.dataset_type == "davis": + video_name = self.video_names[index] + else: + video_name = index + video = self.points_dataset[video_name] + frames = video["video"] + + if isinstance(frames[0], bytes): + # TAP-Vid is stored and JPEG bytes rather than `np.ndarray`s. + def decode(frame): + byteio = io.BytesIO(frame) + img = Image.open(byteio) + return np.array(img) + + frames = np.array([decode(frame) for frame in frames]) + + target_points = self.points_dataset[video_name]["points"] + if self.resize_to_256: + frames = resize_video(frames, [256, 256]) + target_points *= np.array([255, 255]) # 1 should be mapped to 256-1 + else: + target_points *= np.array([frames.shape[2] - 1, frames.shape[1] - 1]) + + target_occ = self.points_dataset[video_name]["occluded"] + if self.queried_first: + converted = sample_queries_first(target_occ, target_points, frames) + else: + converted = sample_queries_strided(target_occ, target_points, frames) + assert converted["target_points"].shape[1] == converted["query_points"].shape[1] + + trajs = torch.from_numpy(converted["target_points"])[0].permute(1, 0, 2).float() # T, N, D + + rgbs = torch.from_numpy(frames).permute(0, 3, 1, 2).float() + visibles = torch.logical_not(torch.from_numpy(converted["occluded"]))[0].permute( + 1, 0 + ) # T, N + query_points = torch.from_numpy(converted["query_points"])[0] # T, N + return CoTrackerData( + rgbs, + trajs, + visibles, + seq_name=str(video_name), + query_points=query_points, + ) + + def __len__(self): + return len(self.points_dataset) diff --git a/cotracker/build/lib/datasets/utils.py b/cotracker/build/lib/datasets/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..30149f1e8d6248684ae519dfba964992f7ea77b3 --- /dev/null +++ b/cotracker/build/lib/datasets/utils.py @@ -0,0 +1,106 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import dataclasses +import torch.nn.functional as F +from dataclasses import dataclass +from typing import Any, Optional + + +@dataclass(eq=False) +class CoTrackerData: + """ + Dataclass for storing video tracks data. + """ + + video: torch.Tensor # B, S, C, H, W + trajectory: torch.Tensor # B, S, N, 2 + visibility: torch.Tensor # B, S, N + # optional data + valid: Optional[torch.Tensor] = None # B, S, N + segmentation: Optional[torch.Tensor] = None # B, S, 1, H, W + seq_name: Optional[str] = None + query_points: Optional[torch.Tensor] = None # TapVID evaluation format + + +def collate_fn(batch): + """ + Collate function for video tracks data. + """ + video = torch.stack([b.video for b in batch], dim=0) + trajectory = torch.stack([b.trajectory for b in batch], dim=0) + visibility = torch.stack([b.visibility for b in batch], dim=0) + query_points = segmentation = None + if batch[0].query_points is not None: + query_points = torch.stack([b.query_points for b in batch], dim=0) + if batch[0].segmentation is not None: + segmentation = torch.stack([b.segmentation for b in batch], dim=0) + seq_name = [b.seq_name for b in batch] + + return CoTrackerData( + video=video, + trajectory=trajectory, + visibility=visibility, + segmentation=segmentation, + seq_name=seq_name, + query_points=query_points, + ) + + +def collate_fn_train(batch): + """ + Collate function for video tracks data during training. + """ + gotit = [gotit for _, gotit in batch] + video = torch.stack([b.video for b, _ in batch], dim=0) + trajectory = torch.stack([b.trajectory for b, _ in batch], dim=0) + visibility = torch.stack([b.visibility for b, _ in batch], dim=0) + valid = torch.stack([b.valid for b, _ in batch], dim=0) + seq_name = [b.seq_name for b, _ in batch] + return ( + CoTrackerData( + video=video, + trajectory=trajectory, + visibility=visibility, + valid=valid, + seq_name=seq_name, + ), + gotit, + ) + + +def try_to_cuda(t: Any) -> Any: + """ + Try to move the input variable `t` to a cuda device. + + Args: + t: Input. + + Returns: + t_cuda: `t` moved to a cuda device, if supported. + """ + try: + t = t.float().cuda() + except AttributeError: + pass + return t + + +def dataclass_to_cuda_(obj): + """ + Move all contents of a dataclass to cuda inplace if supported. + + Args: + batch: Input dataclass. + + Returns: + batch_cuda: `batch` moved to a cuda device, if supported. + """ + for f in dataclasses.fields(obj): + setattr(obj, f.name, try_to_cuda(getattr(obj, f.name))) + return obj diff --git a/cotracker/build/lib/evaluation/__init__.py b/cotracker/build/lib/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/evaluation/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/evaluation/core/__init__.py b/cotracker/build/lib/evaluation/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/evaluation/core/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/evaluation/core/eval_utils.py b/cotracker/build/lib/evaluation/core/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7002fa557eb4af487cf8536df87b297fd94ae236 --- /dev/null +++ b/cotracker/build/lib/evaluation/core/eval_utils.py @@ -0,0 +1,138 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np + +from typing import Iterable, Mapping, Tuple, Union + + +def compute_tapvid_metrics( + query_points: np.ndarray, + gt_occluded: np.ndarray, + gt_tracks: np.ndarray, + pred_occluded: np.ndarray, + pred_tracks: np.ndarray, + query_mode: str, +) -> Mapping[str, np.ndarray]: + """Computes TAP-Vid metrics (Jaccard, Pts. Within Thresh, Occ. Acc.) + See the TAP-Vid paper for details on the metric computation. All inputs are + given in raster coordinates. The first three arguments should be the direct + outputs of the reader: the 'query_points', 'occluded', and 'target_points'. + The paper metrics assume these are scaled relative to 256x256 images. + pred_occluded and pred_tracks are your algorithm's predictions. + This function takes a batch of inputs, and computes metrics separately for + each video. The metrics for the full benchmark are a simple mean of the + metrics across the full set of videos. These numbers are between 0 and 1, + but the paper multiplies them by 100 to ease reading. + Args: + query_points: The query points, an in the format [t, y, x]. Its size is + [b, n, 3], where b is the batch size and n is the number of queries + gt_occluded: A boolean array of shape [b, n, t], where t is the number + of frames. True indicates that the point is occluded. + gt_tracks: The target points, of shape [b, n, t, 2]. Each point is + in the format [x, y] + pred_occluded: A boolean array of predicted occlusions, in the same + format as gt_occluded. + pred_tracks: An array of track predictions from your algorithm, in the + same format as gt_tracks. + query_mode: Either 'first' or 'strided', depending on how queries are + sampled. If 'first', we assume the prior knowledge that all points + before the query point are occluded, and these are removed from the + evaluation. + Returns: + A dict with the following keys: + occlusion_accuracy: Accuracy at predicting occlusion. + pts_within_{x} for x in [1, 2, 4, 8, 16]: Fraction of points + predicted to be within the given pixel threshold, ignoring occlusion + prediction. + jaccard_{x} for x in [1, 2, 4, 8, 16]: Jaccard metric for the given + threshold + average_pts_within_thresh: average across pts_within_{x} + average_jaccard: average across jaccard_{x} + """ + + metrics = {} + # Fixed bug is described in: + # https://github.com/facebookresearch/co-tracker/issues/20 + eye = np.eye(gt_tracks.shape[2], dtype=np.int32) + + if query_mode == "first": + # evaluate frames after the query frame + query_frame_to_eval_frames = np.cumsum(eye, axis=1) - eye + elif query_mode == "strided": + # evaluate all frames except the query frame + query_frame_to_eval_frames = 1 - eye + else: + raise ValueError("Unknown query mode " + query_mode) + + query_frame = query_points[..., 0] + query_frame = np.round(query_frame).astype(np.int32) + evaluation_points = query_frame_to_eval_frames[query_frame] > 0 + + # Occlusion accuracy is simply how often the predicted occlusion equals the + # ground truth. + occ_acc = np.sum( + np.equal(pred_occluded, gt_occluded) & evaluation_points, + axis=(1, 2), + ) / np.sum(evaluation_points) + metrics["occlusion_accuracy"] = occ_acc + + # Next, convert the predictions and ground truth positions into pixel + # coordinates. + visible = np.logical_not(gt_occluded) + pred_visible = np.logical_not(pred_occluded) + all_frac_within = [] + all_jaccard = [] + for thresh in [1, 2, 4, 8, 16]: + # True positives are points that are within the threshold and where both + # the prediction and the ground truth are listed as visible. + within_dist = np.sum( + np.square(pred_tracks - gt_tracks), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, visible) + + # Compute the frac_within_threshold, which is the fraction of points + # within the threshold among points that are visible in the ground truth, + # ignoring whether they're predicted to be visible. + count_correct = np.sum( + is_correct & evaluation_points, + axis=(1, 2), + ) + count_visible_points = np.sum(visible & evaluation_points, axis=(1, 2)) + frac_correct = count_correct / count_visible_points + metrics["pts_within_" + str(thresh)] = frac_correct + all_frac_within.append(frac_correct) + + true_positives = np.sum( + is_correct & pred_visible & evaluation_points, axis=(1, 2) + ) + + # The denominator of the jaccard metric is the true positives plus + # false positives plus false negatives. However, note that true positives + # plus false negatives is simply the number of points in the ground truth + # which is easier to compute than trying to compute all three quantities. + # Thus we just add the number of points in the ground truth to the number + # of false positives. + # + # False positives are simply points that are predicted to be visible, + # but the ground truth is not visible or too far from the prediction. + gt_positives = np.sum(visible & evaluation_points, axis=(1, 2)) + false_positives = (~visible) & pred_visible + false_positives = false_positives | ((~within_dist) & pred_visible) + false_positives = np.sum(false_positives & evaluation_points, axis=(1, 2)) + jaccard = true_positives / (gt_positives + false_positives) + metrics["jaccard_" + str(thresh)] = jaccard + all_jaccard.append(jaccard) + metrics["average_jaccard"] = np.mean( + np.stack(all_jaccard, axis=1), + axis=1, + ) + metrics["average_pts_within_thresh"] = np.mean( + np.stack(all_frac_within, axis=1), + axis=1, + ) + return metrics diff --git a/cotracker/build/lib/evaluation/core/evaluator.py b/cotracker/build/lib/evaluation/core/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..ffc697ec5458b6bc071cb40abbe4234bd581395f --- /dev/null +++ b/cotracker/build/lib/evaluation/core/evaluator.py @@ -0,0 +1,253 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from collections import defaultdict +import os +from typing import Optional +import torch +from tqdm import tqdm +import numpy as np + +from torch.utils.tensorboard import SummaryWriter +from cotracker.datasets.utils import dataclass_to_cuda_ +from cotracker.utils.visualizer import Visualizer +from cotracker.models.core.model_utils import reduce_masked_mean +from cotracker.evaluation.core.eval_utils import compute_tapvid_metrics + +import logging + + +class Evaluator: + """ + A class defining the CoTracker evaluator. + """ + + def __init__(self, exp_dir) -> None: + # Visualization + self.exp_dir = exp_dir + os.makedirs(exp_dir, exist_ok=True) + self.visualization_filepaths = defaultdict(lambda: defaultdict(list)) + self.visualize_dir = os.path.join(exp_dir, "visualisations") + + def compute_metrics(self, metrics, sample, pred_trajectory, dataset_name): + if isinstance(pred_trajectory, tuple): + pred_trajectory, pred_visibility = pred_trajectory + else: + pred_visibility = None + if "tapvid" in dataset_name: + B, T, N, D = sample.trajectory.shape + traj = sample.trajectory.clone() + thr = 0.9 + + if pred_visibility is None: + logging.warning("visibility is NONE") + pred_visibility = torch.zeros_like(sample.visibility) + + if not pred_visibility.dtype == torch.bool: + pred_visibility = pred_visibility > thr + + query_points = sample.query_points.clone().cpu().numpy() + + pred_visibility = pred_visibility[:, :, :N] + pred_trajectory = pred_trajectory[:, :, :N] + + gt_tracks = traj.permute(0, 2, 1, 3).cpu().numpy() + gt_occluded = ( + torch.logical_not(sample.visibility.clone().permute(0, 2, 1)).cpu().numpy() + ) + + pred_occluded = ( + torch.logical_not(pred_visibility.clone().permute(0, 2, 1)).cpu().numpy() + ) + pred_tracks = pred_trajectory.permute(0, 2, 1, 3).cpu().numpy() + + out_metrics = compute_tapvid_metrics( + query_points, + gt_occluded, + gt_tracks, + pred_occluded, + pred_tracks, + query_mode="strided" if "strided" in dataset_name else "first", + ) + + metrics[sample.seq_name[0]] = out_metrics + for metric_name in out_metrics.keys(): + if "avg" not in metrics: + metrics["avg"] = {} + metrics["avg"][metric_name] = np.mean( + [v[metric_name] for k, v in metrics.items() if k != "avg"] + ) + + logging.info(f"Metrics: {out_metrics}") + logging.info(f"avg: {metrics['avg']}") + print("metrics", out_metrics) + print("avg", metrics["avg"]) + elif dataset_name == "dynamic_replica" or dataset_name == "pointodyssey": + *_, N, _ = sample.trajectory.shape + B, T, N = sample.visibility.shape + H, W = sample.video.shape[-2:] + device = sample.video.device + + out_metrics = {} + + d_vis_sum = d_occ_sum = d_sum_all = 0.0 + thrs = [1, 2, 4, 8, 16] + sx_ = (W - 1) / 255.0 + sy_ = (H - 1) / 255.0 + sc_py = np.array([sx_, sy_]).reshape([1, 1, 2]) + sc_pt = torch.from_numpy(sc_py).float().to(device) + __, first_visible_inds = torch.max(sample.visibility, dim=1) + + frame_ids_tensor = torch.arange(T, device=device)[None, :, None].repeat(B, 1, N) + start_tracking_mask = frame_ids_tensor > (first_visible_inds.unsqueeze(1)) + + for thr in thrs: + d_ = ( + torch.norm( + pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt, + dim=-1, + ) + < thr + ).float() # B,S-1,N + d_occ = ( + reduce_masked_mean(d_, (1 - sample.visibility) * start_tracking_mask).item() + * 100.0 + ) + d_occ_sum += d_occ + out_metrics[f"accuracy_occ_{thr}"] = d_occ + + d_vis = ( + reduce_masked_mean(d_, sample.visibility * start_tracking_mask).item() * 100.0 + ) + d_vis_sum += d_vis + out_metrics[f"accuracy_vis_{thr}"] = d_vis + + d_all = reduce_masked_mean(d_, start_tracking_mask).item() * 100.0 + d_sum_all += d_all + out_metrics[f"accuracy_{thr}"] = d_all + + d_occ_avg = d_occ_sum / len(thrs) + d_vis_avg = d_vis_sum / len(thrs) + d_all_avg = d_sum_all / len(thrs) + + sur_thr = 50 + dists = torch.norm( + pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt, + dim=-1, + ) # B,S,N + dist_ok = 1 - (dists > sur_thr).float() * sample.visibility # B,S,N + survival = torch.cumprod(dist_ok, dim=1) # B,S,N + out_metrics["survival"] = torch.mean(survival).item() * 100.0 + + out_metrics["accuracy_occ"] = d_occ_avg + out_metrics["accuracy_vis"] = d_vis_avg + out_metrics["accuracy"] = d_all_avg + + metrics[sample.seq_name[0]] = out_metrics + for metric_name in out_metrics.keys(): + if "avg" not in metrics: + metrics["avg"] = {} + metrics["avg"][metric_name] = float( + np.mean([v[metric_name] for k, v in metrics.items() if k != "avg"]) + ) + + logging.info(f"Metrics: {out_metrics}") + logging.info(f"avg: {metrics['avg']}") + print("metrics", out_metrics) + print("avg", metrics["avg"]) + + @torch.no_grad() + def evaluate_sequence( + self, + model, + test_dataloader: torch.utils.data.DataLoader, + dataset_name: str, + train_mode=False, + visualize_every: int = 1, + writer: Optional[SummaryWriter] = None, + step: Optional[int] = 0, + ): + metrics = {} + + vis = Visualizer( + save_dir=self.exp_dir, + fps=7, + ) + + for ind, sample in enumerate(tqdm(test_dataloader)): + if isinstance(sample, tuple): + sample, gotit = sample + if not all(gotit): + print("batch is None") + continue + if torch.cuda.is_available(): + dataclass_to_cuda_(sample) + device = torch.device("cuda") + else: + device = torch.device("cpu") + + if ( + not train_mode + and hasattr(model, "sequence_len") + and (sample.visibility[:, : model.sequence_len].sum() == 0) + ): + print(f"skipping batch {ind}") + continue + + if "tapvid" in dataset_name: + queries = sample.query_points.clone().float() + + queries = torch.stack( + [ + queries[:, :, 0], + queries[:, :, 2], + queries[:, :, 1], + ], + dim=2, + ).to(device) + else: + queries = torch.cat( + [ + torch.zeros_like(sample.trajectory[:, 0, :, :1]), + sample.trajectory[:, 0], + ], + dim=2, + ).to(device) + + pred_tracks = model(sample.video, queries) + if "strided" in dataset_name: + inv_video = sample.video.flip(1).clone() + inv_queries = queries.clone() + inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1 + + pred_trj, pred_vsb = pred_tracks + inv_pred_trj, inv_pred_vsb = model(inv_video, inv_queries) + + inv_pred_trj = inv_pred_trj.flip(1) + inv_pred_vsb = inv_pred_vsb.flip(1) + + mask = pred_trj == 0 + + pred_trj[mask] = inv_pred_trj[mask] + pred_vsb[mask[:, :, :, 0]] = inv_pred_vsb[mask[:, :, :, 0]] + + pred_tracks = pred_trj, pred_vsb + + if dataset_name == "badja" or dataset_name == "fastcapture": + seq_name = sample.seq_name[0] + else: + seq_name = str(ind) + if ind % visualize_every == 0: + vis.visualize( + sample.video, + pred_tracks[0] if isinstance(pred_tracks, tuple) else pred_tracks, + filename=dataset_name + "_" + seq_name, + writer=writer, + step=step, + ) + + self.compute_metrics(metrics, sample, pred_tracks, dataset_name) + return metrics diff --git a/cotracker/build/lib/evaluation/evaluate.py b/cotracker/build/lib/evaluation/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d679d2a14250e9daa10a643d357f573ad720cf8 --- /dev/null +++ b/cotracker/build/lib/evaluation/evaluate.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +from dataclasses import dataclass, field + +import hydra +import numpy as np + +import torch +from omegaconf import OmegaConf + +from cotracker.datasets.tap_vid_datasets import TapVidDataset +from cotracker.datasets.dr_dataset import DynamicReplicaDataset +from cotracker.datasets.utils import collate_fn + +from cotracker.models.evaluation_predictor import EvaluationPredictor + +from cotracker.evaluation.core.evaluator import Evaluator +from cotracker.models.build_cotracker import ( + build_cotracker, +) + + +@dataclass(eq=False) +class DefaultConfig: + # Directory where all outputs of the experiment will be saved. + exp_dir: str = "./outputs" + + # Name of the dataset to be used for the evaluation. + dataset_name: str = "tapvid_davis_first" + # The root directory of the dataset. + dataset_root: str = "./" + + # Path to the pre-trained model checkpoint to be used for the evaluation. + # The default value is the path to a specific CoTracker model checkpoint. + checkpoint: str = "./checkpoints/cotracker2.pth" + + # EvaluationPredictor parameters + # The size (N) of the support grid used in the predictor. + # The total number of points is (N*N). + grid_size: int = 5 + # The size (N) of the local support grid. + local_grid_size: int = 8 + # A flag indicating whether to evaluate one ground truth point at a time. + single_point: bool = True + # The number of iterative updates for each sliding window. + n_iters: int = 6 + + seed: int = 0 + gpu_idx: int = 0 + + # Override hydra's working directory to current working dir, + # also disable storing the .hydra logs: + hydra: dict = field( + default_factory=lambda: { + "run": {"dir": "."}, + "output_subdir": None, + } + ) + + +def run_eval(cfg: DefaultConfig): + """ + The function evaluates CoTracker on a specified benchmark dataset based on a provided configuration. + + Args: + cfg (DefaultConfig): An instance of DefaultConfig class which includes: + - exp_dir (str): The directory path for the experiment. + - dataset_name (str): The name of the dataset to be used. + - dataset_root (str): The root directory of the dataset. + - checkpoint (str): The path to the CoTracker model's checkpoint. + - single_point (bool): A flag indicating whether to evaluate one ground truth point at a time. + - n_iters (int): The number of iterative updates for each sliding window. + - seed (int): The seed for setting the random state for reproducibility. + - gpu_idx (int): The index of the GPU to be used. + """ + # Creating the experiment directory if it doesn't exist + os.makedirs(cfg.exp_dir, exist_ok=True) + + # Saving the experiment configuration to a .yaml file in the experiment directory + cfg_file = os.path.join(cfg.exp_dir, "expconfig.yaml") + with open(cfg_file, "w") as f: + OmegaConf.save(config=cfg, f=f) + + evaluator = Evaluator(cfg.exp_dir) + cotracker_model = build_cotracker(cfg.checkpoint) + + # Creating the EvaluationPredictor object + predictor = EvaluationPredictor( + cotracker_model, + grid_size=cfg.grid_size, + local_grid_size=cfg.local_grid_size, + single_point=cfg.single_point, + n_iters=cfg.n_iters, + ) + if torch.cuda.is_available(): + predictor.model = predictor.model.cuda() + + # Setting the random seeds + torch.manual_seed(cfg.seed) + np.random.seed(cfg.seed) + + # Constructing the specified dataset + curr_collate_fn = collate_fn + if "tapvid" in cfg.dataset_name: + dataset_type = cfg.dataset_name.split("_")[1] + if dataset_type == "davis": + data_root = os.path.join(cfg.dataset_root, "tapvid_davis", "tapvid_davis.pkl") + elif dataset_type == "kinetics": + data_root = os.path.join( + cfg.dataset_root, "/kinetics/kinetics-dataset/k700-2020/tapvid_kinetics" + ) + test_dataset = TapVidDataset( + dataset_type=dataset_type, + data_root=data_root, + queried_first=not "strided" in cfg.dataset_name, + ) + elif cfg.dataset_name == "dynamic_replica": + test_dataset = DynamicReplicaDataset(sample_len=300, only_first_n_samples=1) + + # Creating the DataLoader object + test_dataloader = torch.utils.data.DataLoader( + test_dataset, + batch_size=1, + shuffle=False, + num_workers=14, + collate_fn=curr_collate_fn, + ) + + # Timing and conducting the evaluation + import time + + start = time.time() + evaluate_result = evaluator.evaluate_sequence( + predictor, + test_dataloader, + dataset_name=cfg.dataset_name, + ) + end = time.time() + print(end - start) + + # Saving the evaluation results to a .json file + evaluate_result = evaluate_result["avg"] + print("evaluate_result", evaluate_result) + result_file = os.path.join(cfg.exp_dir, f"result_eval_.json") + evaluate_result["time"] = end - start + print(f"Dumping eval results to {result_file}.") + with open(result_file, "w") as f: + json.dump(evaluate_result, f) + + +cs = hydra.core.config_store.ConfigStore.instance() +cs.store(name="default_config_eval", node=DefaultConfig) + + +@hydra.main(config_path="./configs/", config_name="default_config_eval") +def evaluate(cfg: DefaultConfig) -> None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.gpu_idx) + run_eval(cfg) + + +if __name__ == "__main__": + evaluate() diff --git a/cotracker/build/lib/models/__init__.py b/cotracker/build/lib/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/models/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/models/build_cotracker.py b/cotracker/build/lib/models/build_cotracker.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae5f90413c9df16b7b6640d68a4502a719290c0 --- /dev/null +++ b/cotracker/build/lib/models/build_cotracker.py @@ -0,0 +1,33 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from cotracker.models.core.cotracker.cotracker import CoTracker2 + + +def build_cotracker( + checkpoint: str, +): + if checkpoint is None: + return build_cotracker() + model_name = checkpoint.split("/")[-1].split(".")[0] + if model_name == "cotracker": + return build_cotracker(checkpoint=checkpoint) + else: + raise ValueError(f"Unknown model name {model_name}") + + +def build_cotracker(checkpoint=None): + cotracker = CoTracker2(stride=4, window_len=8, add_space_attn=True) + + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f, map_location="cpu") + if "model" in state_dict: + state_dict = state_dict["model"] + cotracker.load_state_dict(state_dict) + return cotracker diff --git a/cotracker/build/lib/models/core/__init__.py b/cotracker/build/lib/models/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/models/core/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/models/core/cotracker/__init__.py b/cotracker/build/lib/models/core/cotracker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/models/core/cotracker/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/models/core/cotracker/blocks.py b/cotracker/build/lib/models/core/cotracker/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8d61b2581be967a31f1891fe93c326d5ce7451df --- /dev/null +++ b/cotracker/build/lib/models/core/cotracker/blocks.py @@ -0,0 +1,367 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial +from typing import Callable +import collections +from torch import Tensor +from itertools import repeat + +from cotracker.models.core.model_utils import bilinear_sampler + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + + return parse + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +to_2tuple = _ntuple(2) + + +class Mlp(nn.Module): + """MLP as used in Vision Transformer, MLP-Mixer and related networks""" + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class ResidualBlock(nn.Module): + def __init__(self, in_planes, planes, norm_fn="group", stride=1): + super(ResidualBlock, self).__init__() + + self.conv1 = nn.Conv2d( + in_planes, + planes, + kernel_size=3, + padding=1, + stride=stride, + padding_mode="zeros", + ) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, padding_mode="zeros") + self.relu = nn.ReLU(inplace=True) + + num_groups = planes // 8 + + if norm_fn == "group": + self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + if not stride == 1: + self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + + elif norm_fn == "batch": + self.norm1 = nn.BatchNorm2d(planes) + self.norm2 = nn.BatchNorm2d(planes) + if not stride == 1: + self.norm3 = nn.BatchNorm2d(planes) + + elif norm_fn == "instance": + self.norm1 = nn.InstanceNorm2d(planes) + self.norm2 = nn.InstanceNorm2d(planes) + if not stride == 1: + self.norm3 = nn.InstanceNorm2d(planes) + + elif norm_fn == "none": + self.norm1 = nn.Sequential() + self.norm2 = nn.Sequential() + if not stride == 1: + self.norm3 = nn.Sequential() + + if stride == 1: + self.downsample = None + + else: + self.downsample = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3 + ) + + def forward(self, x): + y = x + y = self.relu(self.norm1(self.conv1(y))) + y = self.relu(self.norm2(self.conv2(y))) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x + y) + + +class BasicEncoder(nn.Module): + def __init__(self, input_dim=3, output_dim=128, stride=4): + super(BasicEncoder, self).__init__() + self.stride = stride + self.norm_fn = "instance" + self.in_planes = output_dim // 2 + + self.norm1 = nn.InstanceNorm2d(self.in_planes) + self.norm2 = nn.InstanceNorm2d(output_dim * 2) + + self.conv1 = nn.Conv2d( + input_dim, + self.in_planes, + kernel_size=7, + stride=2, + padding=3, + padding_mode="zeros", + ) + self.relu1 = nn.ReLU(inplace=True) + self.layer1 = self._make_layer(output_dim // 2, stride=1) + self.layer2 = self._make_layer(output_dim // 4 * 3, stride=2) + self.layer3 = self._make_layer(output_dim, stride=2) + self.layer4 = self._make_layer(output_dim, stride=2) + + self.conv2 = nn.Conv2d( + output_dim * 3 + output_dim // 4, + output_dim * 2, + kernel_size=3, + padding=1, + padding_mode="zeros", + ) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(output_dim * 2, output_dim, kernel_size=1) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.InstanceNorm2d)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def _make_layer(self, dim, stride=1): + layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) + layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + _, _, H, W = x.shape + + x = self.conv1(x) + x = self.norm1(x) + x = self.relu1(x) + + a = self.layer1(x) + b = self.layer2(a) + c = self.layer3(b) + d = self.layer4(c) + + def _bilinear_intepolate(x): + return F.interpolate( + x, + (H // self.stride, W // self.stride), + mode="bilinear", + align_corners=True, + ) + + a = _bilinear_intepolate(a) + b = _bilinear_intepolate(b) + c = _bilinear_intepolate(c) + d = _bilinear_intepolate(d) + + x = self.conv2(torch.cat([a, b, c, d], dim=1)) + x = self.norm2(x) + x = self.relu2(x) + x = self.conv3(x) + return x + + +class CorrBlock: + def __init__( + self, + fmaps, + num_levels=4, + radius=4, + multiple_track_feats=False, + padding_mode="zeros", + ): + B, S, C, H, W = fmaps.shape + self.S, self.C, self.H, self.W = S, C, H, W + self.padding_mode = padding_mode + self.num_levels = num_levels + self.radius = radius + self.fmaps_pyramid = [] + self.multiple_track_feats = multiple_track_feats + + self.fmaps_pyramid.append(fmaps) + for i in range(self.num_levels - 1): + fmaps_ = fmaps.reshape(B * S, C, H, W) + fmaps_ = F.avg_pool2d(fmaps_, 2, stride=2) + _, _, H, W = fmaps_.shape + fmaps = fmaps_.reshape(B, S, C, H, W) + self.fmaps_pyramid.append(fmaps) + + def sample(self, coords): + r = self.radius + B, S, N, D = coords.shape + assert D == 2 + + H, W = self.H, self.W + out_pyramid = [] + for i in range(self.num_levels): + corrs = self.corrs_pyramid[i] # B, S, N, H, W + *_, H, W = corrs.shape + + dx = torch.linspace(-r, r, 2 * r + 1) + dy = torch.linspace(-r, r, 2 * r + 1) + delta = torch.stack(torch.meshgrid(dy, dx, indexing="ij"), axis=-1).to(coords.device) + + centroid_lvl = coords.reshape(B * S * N, 1, 1, 2) / 2**i + delta_lvl = delta.view(1, 2 * r + 1, 2 * r + 1, 2) + coords_lvl = centroid_lvl + delta_lvl + + corrs = bilinear_sampler( + corrs.reshape(B * S * N, 1, H, W), + coords_lvl, + padding_mode=self.padding_mode, + ) + corrs = corrs.view(B, S, N, -1) + out_pyramid.append(corrs) + + out = torch.cat(out_pyramid, dim=-1) # B, S, N, LRR*2 + out = out.permute(0, 2, 1, 3).contiguous().view(B * N, S, -1).float() + return out + + def corr(self, targets): + B, S, N, C = targets.shape + if self.multiple_track_feats: + targets_split = targets.split(C // self.num_levels, dim=-1) + B, S, N, C = targets_split[0].shape + + assert C == self.C + assert S == self.S + + fmap1 = targets + + self.corrs_pyramid = [] + for i, fmaps in enumerate(self.fmaps_pyramid): + *_, H, W = fmaps.shape + fmap2s = fmaps.view(B, S, C, H * W) # B S C H W -> B S C (H W) + if self.multiple_track_feats: + fmap1 = targets_split[i] + corrs = torch.matmul(fmap1, fmap2s) + corrs = corrs.view(B, S, N, H, W) # B S N (H W) -> B S N H W + corrs = corrs / torch.sqrt(torch.tensor(C).float()) + self.corrs_pyramid.append(corrs) + + +class Attention(nn.Module): + def __init__(self, query_dim, context_dim=None, num_heads=8, dim_head=48, qkv_bias=False): + super().__init__() + inner_dim = dim_head * num_heads + context_dim = default(context_dim, query_dim) + self.scale = dim_head**-0.5 + self.heads = num_heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=qkv_bias) + self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias=qkv_bias) + self.to_out = nn.Linear(inner_dim, query_dim) + + def forward(self, x, context=None, attn_bias=None): + B, N1, C = x.shape + h = self.heads + + q = self.to_q(x).reshape(B, N1, h, C // h).permute(0, 2, 1, 3) + context = default(context, x) + k, v = self.to_kv(context).chunk(2, dim=-1) + + N2 = context.shape[1] + k = k.reshape(B, N2, h, C // h).permute(0, 2, 1, 3) + v = v.reshape(B, N2, h, C // h).permute(0, 2, 1, 3) + + sim = (q @ k.transpose(-2, -1)) * self.scale + + if attn_bias is not None: + sim = sim + attn_bias + attn = sim.softmax(dim=-1) + + x = (attn @ v).transpose(1, 2).reshape(B, N1, C) + return self.to_out(x) + + +class AttnBlock(nn.Module): + def __init__( + self, + hidden_size, + num_heads, + attn_class: Callable[..., nn.Module] = Attention, + mlp_ratio=4.0, + **block_kwargs + ): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = attn_class(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=approx_gelu, + drop=0, + ) + + def forward(self, x, mask=None): + attn_bias = mask + if mask is not None: + mask = ( + (mask[:, None] * mask[:, :, None]) + .unsqueeze(1) + .expand(-1, self.attn.num_heads, -1, -1) + ) + max_neg_value = -torch.finfo(x.dtype).max + attn_bias = (~mask) * max_neg_value + x = x + self.attn(self.norm1(x), attn_bias=attn_bias) + x = x + self.mlp(self.norm2(x)) + return x diff --git a/cotracker/build/lib/models/core/cotracker/cotracker.py b/cotracker/build/lib/models/core/cotracker/cotracker.py new file mode 100644 index 0000000000000000000000000000000000000000..53178fbe067552da46224c5e09760d2c747d8e16 --- /dev/null +++ b/cotracker/build/lib/models/core/cotracker/cotracker.py @@ -0,0 +1,503 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from cotracker.models.core.model_utils import sample_features4d, sample_features5d +from cotracker.models.core.embeddings import ( + get_2d_embedding, + get_1d_sincos_pos_embed_from_grid, + get_2d_sincos_pos_embed, +) + +from cotracker.models.core.cotracker.blocks import ( + Mlp, + BasicEncoder, + AttnBlock, + CorrBlock, + Attention, +) + +torch.manual_seed(0) + + +class CoTracker2(nn.Module): + def __init__( + self, + window_len=8, + stride=4, + add_space_attn=True, + num_virtual_tracks=64, + model_resolution=(384, 512), + ): + super(CoTracker2, self).__init__() + self.window_len = window_len + self.stride = stride + self.hidden_dim = 256 + self.latent_dim = 128 + self.add_space_attn = add_space_attn + self.fnet = BasicEncoder(output_dim=self.latent_dim) + self.num_virtual_tracks = num_virtual_tracks + self.model_resolution = model_resolution + self.input_dim = 456 + self.updateformer = EfficientUpdateFormer( + space_depth=6, + time_depth=6, + input_dim=self.input_dim, + hidden_size=384, + output_dim=self.latent_dim + 2, + mlp_ratio=4.0, + add_space_attn=add_space_attn, + num_virtual_tracks=num_virtual_tracks, + ) + + time_grid = torch.linspace(0, window_len - 1, window_len).reshape(1, window_len, 1) + + self.register_buffer( + "time_emb", get_1d_sincos_pos_embed_from_grid(self.input_dim, time_grid[0]) + ) + + self.register_buffer( + "pos_emb", + get_2d_sincos_pos_embed( + embed_dim=self.input_dim, + grid_size=( + model_resolution[0] // stride, + model_resolution[1] // stride, + ), + ), + ) + self.norm = nn.GroupNorm(1, self.latent_dim) + self.track_feat_updater = nn.Sequential( + nn.Linear(self.latent_dim, self.latent_dim), + nn.GELU(), + ) + self.vis_predictor = nn.Sequential( + nn.Linear(self.latent_dim, 1), + ) + + def forward_window( + self, + fmaps, + coords, + track_feat=None, + vis=None, + track_mask=None, + attention_mask=None, + iters=4, + ): + # B = batch size + # S = number of frames in the window) + # N = number of tracks + # C = channels of a point feature vector + # E = positional embedding size + # LRR = local receptive field radius + # D = dimension of the transformer input tokens + + # track_feat = B S N C + # vis = B S N 1 + # track_mask = B S N 1 + # attention_mask = B S N + + B, S_init, N, __ = track_mask.shape + B, S, *_ = fmaps.shape + + track_mask = F.pad(track_mask, (0, 0, 0, 0, 0, S - S_init), "constant") + track_mask_vis = ( + torch.cat([track_mask, vis], dim=-1).permute(0, 2, 1, 3).reshape(B * N, S, 2) + ) + + corr_block = CorrBlock( + fmaps, + num_levels=4, + radius=3, + padding_mode="border", + ) + + sampled_pos_emb = ( + sample_features4d(self.pos_emb.repeat(B, 1, 1, 1), coords[:, 0]) + .reshape(B * N, self.input_dim) + .unsqueeze(1) + ) # B E N -> (B N) 1 E + + coord_preds = [] + for __ in range(iters): + coords = coords.detach() # B S N 2 + corr_block.corr(track_feat) + + # Sample correlation features around each point + fcorrs = corr_block.sample(coords) # (B N) S LRR + + # Get the flow embeddings + flows = (coords - coords[:, 0:1]).permute(0, 2, 1, 3).reshape(B * N, S, 2) + flow_emb = get_2d_embedding(flows, 64, cat_coords=True) # N S E + + track_feat_ = track_feat.permute(0, 2, 1, 3).reshape(B * N, S, self.latent_dim) + + transformer_input = torch.cat([flow_emb, fcorrs, track_feat_, track_mask_vis], dim=2) + x = transformer_input + sampled_pos_emb + self.time_emb + x = x.view(B, N, S, -1) # (B N) S D -> B N S D + + delta = self.updateformer( + x, + attention_mask.reshape(B * S, N), # B S N -> (B S) N + ) + + delta_coords = delta[..., :2].permute(0, 2, 1, 3) + coords = coords + delta_coords + coord_preds.append(coords * self.stride) + + delta_feats_ = delta[..., 2:].reshape(B * N * S, self.latent_dim) + track_feat_ = track_feat.permute(0, 2, 1, 3).reshape(B * N * S, self.latent_dim) + track_feat_ = self.track_feat_updater(self.norm(delta_feats_)) + track_feat_ + track_feat = track_feat_.reshape(B, N, S, self.latent_dim).permute( + 0, 2, 1, 3 + ) # (B N S) C -> B S N C + + vis_pred = self.vis_predictor(track_feat).reshape(B, S, N) + return coord_preds, vis_pred + + def get_track_feat(self, fmaps, queried_frames, queried_coords): + sample_frames = queried_frames[:, None, :, None] + sample_coords = torch.cat( + [ + sample_frames, + queried_coords[:, None], + ], + dim=-1, + ) + sample_track_feats = sample_features5d(fmaps, sample_coords) + return sample_track_feats + + def init_video_online_processing(self): + self.online_ind = 0 + self.online_track_feat = None + self.online_coords_predicted = None + self.online_vis_predicted = None + + def forward(self, video, queries, iters=4, is_train=False, is_online=False): + """Predict tracks + + Args: + video (FloatTensor[B, T, 3]): input videos. + queries (FloatTensor[B, N, 3]): point queries. + iters (int, optional): number of updates. Defaults to 4. + is_train (bool, optional): enables training mode. Defaults to False. + is_online (bool, optional): enables online mode. Defaults to False. Before enabling, call model.init_video_online_processing(). + + Returns: + - coords_predicted (FloatTensor[B, T, N, 2]): + - vis_predicted (FloatTensor[B, T, N]): + - train_data: `None` if `is_train` is false, otherwise: + - all_vis_predictions (List[FloatTensor[B, S, N, 1]]): + - all_coords_predictions (List[FloatTensor[B, S, N, 2]]): + - mask (BoolTensor[B, T, N]): + """ + B, T, C, H, W = video.shape + B, N, __ = queries.shape + S = self.window_len + device = queries.device + + # B = batch size + # S = number of frames in the window of the padded video + # S_trimmed = actual number of frames in the window + # N = number of tracks + # C = color channels (3 for RGB) + # E = positional embedding size + # LRR = local receptive field radius + # D = dimension of the transformer input tokens + + # video = B T C H W + # queries = B N 3 + # coords_init = B S N 2 + # vis_init = B S N 1 + + assert S >= 2 # A tracker needs at least two frames to track something + if is_online: + assert T <= S, "Online mode: video chunk must be <= window size." + assert self.online_ind is not None, "Call model.init_video_online_processing() first." + assert not is_train, "Training not supported in online mode." + step = S // 2 # How much the sliding window moves at every step + video = 2 * (video / 255.0) - 1.0 + + # The first channel is the frame number + # The rest are the coordinates of points we want to track + queried_frames = queries[:, :, 0].long() + + queried_coords = queries[..., 1:] + queried_coords = queried_coords / self.stride + + # We store our predictions here + coords_predicted = torch.zeros((B, T, N, 2), device=device) + vis_predicted = torch.zeros((B, T, N), device=device) + if is_online: + if self.online_coords_predicted is None: + # Init online predictions with zeros + self.online_coords_predicted = coords_predicted + self.online_vis_predicted = vis_predicted + else: + # Pad online predictions with zeros for the current window + pad = min(step, T - step) + coords_predicted = F.pad( + self.online_coords_predicted, (0, 0, 0, 0, 0, pad), "constant" + ) + vis_predicted = F.pad(self.online_vis_predicted, (0, 0, 0, pad), "constant") + all_coords_predictions, all_vis_predictions = [], [] + + # Pad the video so that an integer number of sliding windows fit into it + # TODO: we may drop this requirement because the transformer should not care + # TODO: pad the features instead of the video + pad = S - T if is_online else (S - T % S) % S # We don't want to pad if T % S == 0 + video = F.pad(video.reshape(B, 1, T, C * H * W), (0, 0, 0, pad), "replicate").reshape( + B, -1, C, H, W + ) + + # Compute convolutional features for the video or for the current chunk in case of online mode + fmaps = self.fnet(video.reshape(-1, C, H, W)).reshape( + B, -1, self.latent_dim, H // self.stride, W // self.stride + ) + + # We compute track features + track_feat = self.get_track_feat( + fmaps, + queried_frames - self.online_ind if is_online else queried_frames, + queried_coords, + ).repeat(1, S, 1, 1) + if is_online: + # We update track features for the current window + sample_frames = queried_frames[:, None, :, None] # B 1 N 1 + left = 0 if self.online_ind == 0 else self.online_ind + step + right = self.online_ind + S + sample_mask = (sample_frames >= left) & (sample_frames < right) + if self.online_track_feat is None: + self.online_track_feat = torch.zeros_like(track_feat, device=device) + self.online_track_feat += track_feat * sample_mask + track_feat = self.online_track_feat.clone() + # We process ((num_windows - 1) * step + S) frames in total, so there are + # (ceil((T - S) / step) + 1) windows + num_windows = (T - S + step - 1) // step + 1 + # We process only the current video chunk in the online mode + indices = [self.online_ind] if is_online else range(0, step * num_windows, step) + + coords_init = queried_coords.reshape(B, 1, N, 2).expand(B, S, N, 2).float() + vis_init = torch.ones((B, S, N, 1), device=device).float() * 10 + for ind in indices: + # We copy over coords and vis for tracks that are queried + # by the end of the previous window, which is ind + overlap + if ind > 0: + overlap = S - step + copy_over = (queried_frames < ind + overlap)[:, None, :, None] # B 1 N 1 + coords_prev = torch.nn.functional.pad( + coords_predicted[:, ind : ind + overlap] / self.stride, + (0, 0, 0, 0, 0, step), + "replicate", + ) # B S N 2 + vis_prev = torch.nn.functional.pad( + vis_predicted[:, ind : ind + overlap, :, None].clone(), + (0, 0, 0, 0, 0, step), + "replicate", + ) # B S N 1 + coords_init = torch.where( + copy_over.expand_as(coords_init), coords_prev, coords_init + ) + vis_init = torch.where(copy_over.expand_as(vis_init), vis_prev, vis_init) + + # The attention mask is 1 for the spatio-temporal points within + # a track which is updated in the current window + attention_mask = (queried_frames < ind + S).reshape(B, 1, N).repeat(1, S, 1) # B S N + + # The track mask is 1 for the spatio-temporal points that actually + # need updating: only after begin queried, and not if contained + # in a previous window + track_mask = ( + queried_frames[:, None, :, None] + <= torch.arange(ind, ind + S, device=device)[None, :, None, None] + ).contiguous() # B S N 1 + + if ind > 0: + track_mask[:, :overlap, :, :] = False + + # Predict the coordinates and visibility for the current window + coords, vis = self.forward_window( + fmaps=fmaps if is_online else fmaps[:, ind : ind + S], + coords=coords_init, + track_feat=attention_mask.unsqueeze(-1) * track_feat, + vis=vis_init, + track_mask=track_mask, + attention_mask=attention_mask, + iters=iters, + ) + + S_trimmed = T if is_online else min(T - ind, S) # accounts for last window duration + coords_predicted[:, ind : ind + S] = coords[-1][:, :S_trimmed] + vis_predicted[:, ind : ind + S] = vis[:, :S_trimmed] + if is_train: + all_coords_predictions.append([coord[:, :S_trimmed] for coord in coords]) + all_vis_predictions.append(torch.sigmoid(vis[:, :S_trimmed])) + + if is_online: + self.online_ind += step + self.online_coords_predicted = coords_predicted + self.online_vis_predicted = vis_predicted + vis_predicted = torch.sigmoid(vis_predicted) + + if is_train: + mask = queried_frames[:, None] <= torch.arange(0, T, device=device)[None, :, None] + train_data = (all_coords_predictions, all_vis_predictions, mask) + else: + train_data = None + + return coords_predicted, vis_predicted, train_data + + +class EfficientUpdateFormer(nn.Module): + """ + Transformer model that updates track estimates. + """ + + def __init__( + self, + space_depth=6, + time_depth=6, + input_dim=320, + hidden_size=384, + num_heads=8, + output_dim=130, + mlp_ratio=4.0, + add_space_attn=True, + num_virtual_tracks=64, + ): + super().__init__() + self.out_channels = 2 + self.num_heads = num_heads + self.hidden_size = hidden_size + self.add_space_attn = add_space_attn + self.input_transform = torch.nn.Linear(input_dim, hidden_size, bias=True) + self.flow_head = torch.nn.Linear(hidden_size, output_dim, bias=True) + self.num_virtual_tracks = num_virtual_tracks + self.virual_tracks = nn.Parameter(torch.randn(1, num_virtual_tracks, 1, hidden_size)) + self.time_blocks = nn.ModuleList( + [ + AttnBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + attn_class=Attention, + ) + for _ in range(time_depth) + ] + ) + + if add_space_attn: + self.space_virtual_blocks = nn.ModuleList( + [ + AttnBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + attn_class=Attention, + ) + for _ in range(space_depth) + ] + ) + self.space_point2virtual_blocks = nn.ModuleList( + [ + CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) + for _ in range(space_depth) + ] + ) + self.space_virtual2point_blocks = nn.ModuleList( + [ + CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) + for _ in range(space_depth) + ] + ) + assert len(self.time_blocks) >= len(self.space_virtual2point_blocks) + self.initialize_weights() + + def initialize_weights(self): + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + def forward(self, input_tensor, mask=None): + tokens = self.input_transform(input_tensor) + B, _, T, _ = tokens.shape + virtual_tokens = self.virual_tracks.repeat(B, 1, T, 1) + tokens = torch.cat([tokens, virtual_tokens], dim=1) + _, N, _, _ = tokens.shape + + j = 0 + for i in range(len(self.time_blocks)): + time_tokens = tokens.contiguous().view(B * N, T, -1) # B N T C -> (B N) T C + time_tokens = self.time_blocks[i](time_tokens) + + tokens = time_tokens.view(B, N, T, -1) # (B N) T C -> B N T C + if self.add_space_attn and ( + i % (len(self.time_blocks) // len(self.space_virtual_blocks)) == 0 + ): + space_tokens = ( + tokens.permute(0, 2, 1, 3).contiguous().view(B * T, N, -1) + ) # B N T C -> (B T) N C + point_tokens = space_tokens[:, : N - self.num_virtual_tracks] + virtual_tokens = space_tokens[:, N - self.num_virtual_tracks :] + + virtual_tokens = self.space_virtual2point_blocks[j]( + virtual_tokens, point_tokens, mask=mask + ) + virtual_tokens = self.space_virtual_blocks[j](virtual_tokens) + point_tokens = self.space_point2virtual_blocks[j]( + point_tokens, virtual_tokens, mask=mask + ) + space_tokens = torch.cat([point_tokens, virtual_tokens], dim=1) + tokens = space_tokens.view(B, T, N, -1).permute(0, 2, 1, 3) # (B T) N C -> B N T C + j += 1 + tokens = tokens[:, : N - self.num_virtual_tracks] + flow = self.flow_head(tokens) + return flow + + +class CrossAttnBlock(nn.Module): + def __init__(self, hidden_size, context_dim, num_heads=1, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.norm_context = nn.LayerNorm(hidden_size) + self.cross_attn = Attention( + hidden_size, context_dim=context_dim, num_heads=num_heads, qkv_bias=True, **block_kwargs + ) + + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=approx_gelu, + drop=0, + ) + + def forward(self, x, context, mask=None): + if mask is not None: + if mask.shape[1] == x.shape[1]: + mask = mask[:, None, :, None].expand( + -1, self.cross_attn.heads, -1, context.shape[1] + ) + else: + mask = mask[:, None, None].expand(-1, self.cross_attn.heads, x.shape[1], -1) + + max_neg_value = -torch.finfo(x.dtype).max + attn_bias = (~mask) * max_neg_value + x = x + self.cross_attn( + self.norm1(x), context=self.norm_context(context), attn_bias=attn_bias + ) + x = x + self.mlp(self.norm2(x)) + return x diff --git a/cotracker/build/lib/models/core/cotracker/losses.py b/cotracker/build/lib/models/core/cotracker/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..2bdcc2ead92b31e4aebce77449a108793d6e5425 --- /dev/null +++ b/cotracker/build/lib/models/core/cotracker/losses.py @@ -0,0 +1,61 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from cotracker.models.core.model_utils import reduce_masked_mean + +EPS = 1e-6 + + +def balanced_ce_loss(pred, gt, valid=None): + total_balanced_loss = 0.0 + for j in range(len(gt)): + B, S, N = gt[j].shape + # pred and gt are the same shape + for (a, b) in zip(pred[j].size(), gt[j].size()): + assert a == b # some shape mismatch! + # if valid is not None: + for (a, b) in zip(pred[j].size(), valid[j].size()): + assert a == b # some shape mismatch! + + pos = (gt[j] > 0.95).float() + neg = (gt[j] < 0.05).float() + + label = pos * 2.0 - 1.0 + a = -label * pred[j] + b = F.relu(a) + loss = b + torch.log(torch.exp(-b) + torch.exp(a - b)) + + pos_loss = reduce_masked_mean(loss, pos * valid[j]) + neg_loss = reduce_masked_mean(loss, neg * valid[j]) + + balanced_loss = pos_loss + neg_loss + total_balanced_loss += balanced_loss / float(N) + return total_balanced_loss + + +def sequence_loss(flow_preds, flow_gt, vis, valids, gamma=0.8): + """Loss function defined over sequence of flow predictions""" + total_flow_loss = 0.0 + for j in range(len(flow_gt)): + B, S, N, D = flow_gt[j].shape + assert D == 2 + B, S1, N = vis[j].shape + B, S2, N = valids[j].shape + assert S == S1 + assert S == S2 + n_predictions = len(flow_preds[j]) + flow_loss = 0.0 + for i in range(n_predictions): + i_weight = gamma ** (n_predictions - i - 1) + flow_pred = flow_preds[j][i] + i_loss = (flow_pred - flow_gt[j]).abs() # B, S, N, 2 + i_loss = torch.mean(i_loss, dim=3) # B, S, N + flow_loss += i_weight * reduce_masked_mean(i_loss, valids[j]) + flow_loss = flow_loss / n_predictions + total_flow_loss += flow_loss / float(N) + return total_flow_loss diff --git a/cotracker/build/lib/models/core/embeddings.py b/cotracker/build/lib/models/core/embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..897cd5d9f41121a9692281a719a2d24914293318 --- /dev/null +++ b/cotracker/build/lib/models/core/embeddings.py @@ -0,0 +1,120 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple, Union +import torch + + +def get_2d_sincos_pos_embed( + embed_dim: int, grid_size: Union[int, Tuple[int, int]] +) -> torch.Tensor: + """ + This function initializes a grid and generates a 2D positional embedding using sine and cosine functions. + It is a wrapper of get_2d_sincos_pos_embed_from_grid. + Args: + - embed_dim: The embedding dimension. + - grid_size: The grid size. + Returns: + - pos_embed: The generated 2D positional embedding. + """ + if isinstance(grid_size, tuple): + grid_size_h, grid_size_w = grid_size + else: + grid_size_h = grid_size_w = grid_size + grid_h = torch.arange(grid_size_h, dtype=torch.float) + grid_w = torch.arange(grid_size_w, dtype=torch.float) + grid = torch.meshgrid(grid_w, grid_h, indexing="xy") + grid = torch.stack(grid, dim=0) + grid = grid.reshape([2, 1, grid_size_h, grid_size_w]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + return pos_embed.reshape(1, grid_size_h, grid_size_w, -1).permute(0, 3, 1, 2) + + +def get_2d_sincos_pos_embed_from_grid( + embed_dim: int, grid: torch.Tensor +) -> torch.Tensor: + """ + This function generates a 2D positional embedding from a given grid using sine and cosine functions. + + Args: + - embed_dim: The embedding dimension. + - grid: The grid to generate the embedding from. + + Returns: + - emb: The generated 2D positional embedding. + """ + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = torch.cat([emb_h, emb_w], dim=2) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid( + embed_dim: int, pos: torch.Tensor +) -> torch.Tensor: + """ + This function generates a 1D positional embedding from a given grid using sine and cosine functions. + + Args: + - embed_dim: The embedding dimension. + - pos: The position to generate the embedding from. + + Returns: + - emb: The generated 1D positional embedding. + """ + assert embed_dim % 2 == 0 + omega = torch.arange(embed_dim // 2, dtype=torch.double) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + + emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) + return emb[None].float() + + +def get_2d_embedding(xy: torch.Tensor, C: int, cat_coords: bool = True) -> torch.Tensor: + """ + This function generates a 2D positional embedding from given coordinates using sine and cosine functions. + + Args: + - xy: The coordinates to generate the embedding from. + - C: The size of the embedding. + - cat_coords: A flag to indicate whether to concatenate the original coordinates to the embedding. + + Returns: + - pe: The generated 2D positional embedding. + """ + B, N, D = xy.shape + assert D == 2 + + x = xy[:, :, 0:1] + y = xy[:, :, 1:2] + div_term = ( + torch.arange(0, C, 2, device=xy.device, dtype=torch.float32) * (1000.0 / C) + ).reshape(1, 1, int(C / 2)) + + pe_x = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32) + pe_y = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32) + + pe_x[:, :, 0::2] = torch.sin(x * div_term) + pe_x[:, :, 1::2] = torch.cos(x * div_term) + + pe_y[:, :, 0::2] = torch.sin(y * div_term) + pe_y[:, :, 1::2] = torch.cos(y * div_term) + + pe = torch.cat([pe_x, pe_y], dim=2) # (B, N, C*3) + if cat_coords: + pe = torch.cat([xy, pe], dim=2) # (B, N, C*3+3) + return pe diff --git a/cotracker/build/lib/models/core/model_utils.py b/cotracker/build/lib/models/core/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e688e85ac3ebf59cab6aa1a5a5ac5119048386 --- /dev/null +++ b/cotracker/build/lib/models/core/model_utils.py @@ -0,0 +1,271 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from typing import Optional, Tuple + +EPS = 1e-6 + + +def smart_cat(tensor1, tensor2, dim): + if tensor1 is None: + return tensor2 + return torch.cat([tensor1, tensor2], dim=dim) + + +def get_points_on_a_grid( + size: int, + extent: Tuple[float, ...], + center: Optional[Tuple[float, ...]] = None, + device: Optional[torch.device] = torch.device("cpu"), + shift_grid: bool = False, +): + r"""Get a grid of points covering a rectangular region + + `get_points_on_a_grid(size, extent)` generates a :attr:`size` by + :attr:`size` grid fo points distributed to cover a rectangular area + specified by `extent`. + + The `extent` is a pair of integer :math:`(H,W)` specifying the height + and width of the rectangle. + + Optionally, the :attr:`center` can be specified as a pair :math:`(c_y,c_x)` + specifying the vertical and horizontal center coordinates. The center + defaults to the middle of the extent. + + Points are distributed uniformly within the rectangle leaving a margin + :math:`m=W/64` from the border. + + It returns a :math:`(1, \text{size} \times \text{size}, 2)` tensor of + points :math:`P_{ij}=(x_i, y_i)` where + + .. math:: + P_{ij} = \left( + c_x + m -\frac{W}{2} + \frac{W - 2m}{\text{size} - 1}\, j,~ + c_y + m -\frac{H}{2} + \frac{H - 2m}{\text{size} - 1}\, i + \right) + + Points are returned in row-major order. + + Args: + size (int): grid size. + extent (tuple): height and with of the grid extent. + center (tuple, optional): grid center. + device (str, optional): Defaults to `"cpu"`. + + Returns: + Tensor: grid. + """ + if size == 1: + return torch.tensor([extent[1] / 2, extent[0] / 2], device=device)[None, None] + + if center is None: + center = [extent[0] / 2, extent[1] / 2] + + margin = extent[1] / 64 + range_y = (margin - extent[0] / 2 + center[0], extent[0] / 2 + center[0] - margin) + range_x = (margin - extent[1] / 2 + center[1], extent[1] / 2 + center[1] - margin) + grid_y, grid_x = torch.meshgrid( + torch.linspace(*range_y, size, device=device), + torch.linspace(*range_x, size, device=device), + indexing="ij", + ) + + if shift_grid: + # shift the grid randomly + # grid_x: (10, 10) + # grid_y: (10, 10) + shift_x = (range_x[1] - range_x[0]) / (size - 1) + shift_y = (range_y[1] - range_y[0]) / (size - 1) + grid_x = grid_x + torch.randn_like(grid_x) / 3 * shift_x / 2 + grid_y = grid_y + torch.randn_like(grid_y) / 3 * shift_y / 2 + + # stay within the bounds + grid_x = torch.clamp(grid_x, range_x[0], range_x[1]) + grid_y = torch.clamp(grid_y, range_y[0], range_y[1]) + + return torch.stack([grid_x, grid_y], dim=-1).reshape(1, -1, 2) + + +def reduce_masked_mean(input, mask, dim=None, keepdim=False): + r"""Masked mean + + `reduce_masked_mean(x, mask)` computes the mean of a tensor :attr:`input` + over a mask :attr:`mask`, returning + + .. math:: + \text{output} = + \frac + {\sum_{i=1}^N \text{input}_i \cdot \text{mask}_i} + {\epsilon + \sum_{i=1}^N \text{mask}_i} + + where :math:`N` is the number of elements in :attr:`input` and + :attr:`mask`, and :math:`\epsilon` is a small constant to avoid + division by zero. + + `reduced_masked_mean(x, mask, dim)` computes the mean of a tensor + :attr:`input` over a mask :attr:`mask` along a dimension :attr:`dim`. + Optionally, the dimension can be kept in the output by setting + :attr:`keepdim` to `True`. Tensor :attr:`mask` must be broadcastable to + the same dimension as :attr:`input`. + + The interface is similar to `torch.mean()`. + + Args: + inout (Tensor): input tensor. + mask (Tensor): mask. + dim (int, optional): Dimension to sum over. Defaults to None. + keepdim (bool, optional): Keep the summed dimension. Defaults to False. + + Returns: + Tensor: mean tensor. + """ + + mask = mask.expand_as(input) + + prod = input * mask + + if dim is None: + numer = torch.sum(prod) + denom = torch.sum(mask) + else: + numer = torch.sum(prod, dim=dim, keepdim=keepdim) + denom = torch.sum(mask, dim=dim, keepdim=keepdim) + + mean = numer / (EPS + denom) + return mean + + +def bilinear_sampler(input, coords, align_corners=True, padding_mode="border"): + r"""Sample a tensor using bilinear interpolation + + `bilinear_sampler(input, coords)` samples a tensor :attr:`input` at + coordinates :attr:`coords` using bilinear interpolation. It is the same + as `torch.nn.functional.grid_sample()` but with a different coordinate + convention. + + The input tensor is assumed to be of shape :math:`(B, C, H, W)`, where + :math:`B` is the batch size, :math:`C` is the number of channels, + :math:`H` is the height of the image, and :math:`W` is the width of the + image. The tensor :attr:`coords` of shape :math:`(B, H_o, W_o, 2)` is + interpreted as an array of 2D point coordinates :math:`(x_i,y_i)`. + + Alternatively, the input tensor can be of size :math:`(B, C, T, H, W)`, + in which case sample points are triplets :math:`(t_i,x_i,y_i)`. Note + that in this case the order of the components is slightly different + from `grid_sample()`, which would expect :math:`(x_i,y_i,t_i)`. + + If `align_corners` is `True`, the coordinate :math:`x` is assumed to be + in the range :math:`[0,W-1]`, with 0 corresponding to the center of the + left-most image pixel :math:`W-1` to the center of the right-most + pixel. + + If `align_corners` is `False`, the coordinate :math:`x` is assumed to + be in the range :math:`[0,W]`, with 0 corresponding to the left edge of + the left-most pixel :math:`W` to the right edge of the right-most + pixel. + + Similar conventions apply to the :math:`y` for the range + :math:`[0,H-1]` and :math:`[0,H]` and to :math:`t` for the range + :math:`[0,T-1]` and :math:`[0,T]`. + + Args: + input (Tensor): batch of input images. + coords (Tensor): batch of coordinates. + align_corners (bool, optional): Coordinate convention. Defaults to `True`. + padding_mode (str, optional): Padding mode. Defaults to `"border"`. + + Returns: + Tensor: sampled points. + """ + + sizes = input.shape[2:] + + assert len(sizes) in [2, 3] + + if len(sizes) == 3: + # t x y -> x y t to match dimensions T H W in grid_sample + coords = coords[..., [1, 2, 0]] + + if align_corners: + coords = coords * torch.tensor( + [2 / max(size - 1, 1) for size in reversed(sizes)], device=coords.device + ) + else: + coords = coords * torch.tensor([2 / size for size in reversed(sizes)], device=coords.device) + + coords -= 1 + + return F.grid_sample(input, coords, align_corners=align_corners, padding_mode=padding_mode) + + +def sample_features4d(input, coords): + r"""Sample spatial features + + `sample_features4d(input, coords)` samples the spatial features + :attr:`input` represented by a 4D tensor :math:`(B, C, H, W)`. + + The field is sampled at coordinates :attr:`coords` using bilinear + interpolation. :attr:`coords` is assumed to be of shape :math:`(B, R, + 3)`, where each sample has the format :math:`(x_i, y_i)`. This uses the + same convention as :func:`bilinear_sampler` with `align_corners=True`. + + The output tensor has one feature per point, and has shape :math:`(B, + R, C)`. + + Args: + input (Tensor): spatial features. + coords (Tensor): points. + + Returns: + Tensor: sampled features. + """ + + B, _, _, _ = input.shape + + # B R 2 -> B R 1 2 + coords = coords.unsqueeze(2) + + # B C R 1 + feats = bilinear_sampler(input, coords) + + return feats.permute(0, 2, 1, 3).view( + B, -1, feats.shape[1] * feats.shape[3] + ) # B C R 1 -> B R C + + +def sample_features5d(input, coords): + r"""Sample spatio-temporal features + + `sample_features5d(input, coords)` works in the same way as + :func:`sample_features4d` but for spatio-temporal features and points: + :attr:`input` is a 5D tensor :math:`(B, T, C, H, W)`, :attr:`coords` is + a :math:`(B, R1, R2, 3)` tensor of spatio-temporal point :math:`(t_i, + x_i, y_i)`. The output tensor has shape :math:`(B, R1, R2, C)`. + + Args: + input (Tensor): spatio-temporal features. + coords (Tensor): spatio-temporal points. + + Returns: + Tensor: sampled features. + """ + + B, T, _, _, _ = input.shape + + # B T C H W -> B C T H W + input = input.permute(0, 2, 1, 3, 4) + + # B R1 R2 3 -> B R1 R2 1 3 + coords = coords.unsqueeze(3) + + # B C R1 R2 1 + feats = bilinear_sampler(input, coords) + + return feats.permute(0, 2, 3, 1, 4).view( + B, feats.shape[2], feats.shape[3], feats.shape[1] + ) # B C R1 R2 1 -> B R1 R2 C diff --git a/cotracker/build/lib/models/evaluation_predictor.py b/cotracker/build/lib/models/evaluation_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..87f8e18611e88fce4b69346d2210cf3c32d206fe --- /dev/null +++ b/cotracker/build/lib/models/evaluation_predictor.py @@ -0,0 +1,104 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from typing import Tuple + +from cotracker.models.core.cotracker.cotracker import CoTracker2 +from cotracker.models.core.model_utils import get_points_on_a_grid + + +class EvaluationPredictor(torch.nn.Module): + def __init__( + self, + cotracker_model: CoTracker2, + interp_shape: Tuple[int, int] = (384, 512), + grid_size: int = 5, + local_grid_size: int = 8, + single_point: bool = True, + n_iters: int = 6, + ) -> None: + super(EvaluationPredictor, self).__init__() + self.grid_size = grid_size + self.local_grid_size = local_grid_size + self.single_point = single_point + self.interp_shape = interp_shape + self.n_iters = n_iters + + self.model = cotracker_model + self.model.eval() + + def forward(self, video, queries): + queries = queries.clone() + B, T, C, H, W = video.shape + B, N, D = queries.shape + + assert D == 3 + + video = video.reshape(B * T, C, H, W) + video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True) + video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1]) + + device = video.device + + queries[:, :, 1] *= (self.interp_shape[1] - 1) / (W - 1) + queries[:, :, 2] *= (self.interp_shape[0] - 1) / (H - 1) + + if self.single_point: + traj_e = torch.zeros((B, T, N, 2), device=device) + vis_e = torch.zeros((B, T, N), device=device) + for pind in range((N)): + query = queries[:, pind : pind + 1] + + t = query[0, 0, 0].long() + + traj_e_pind, vis_e_pind = self._process_one_point(video, query) + traj_e[:, t:, pind : pind + 1] = traj_e_pind[:, :, :1] + vis_e[:, t:, pind : pind + 1] = vis_e_pind[:, :, :1] + else: + if self.grid_size > 0: + xy = get_points_on_a_grid(self.grid_size, video.shape[3:]) + xy = torch.cat([torch.zeros_like(xy[:, :, :1]), xy], dim=2).to(device) # + queries = torch.cat([queries, xy], dim=1) # + + traj_e, vis_e, __ = self.model( + video=video, + queries=queries, + iters=self.n_iters, + ) + + traj_e[:, :, :, 0] *= (W - 1) / float(self.interp_shape[1] - 1) + traj_e[:, :, :, 1] *= (H - 1) / float(self.interp_shape[0] - 1) + return traj_e, vis_e + + def _process_one_point(self, video, query): + t = query[0, 0, 0].long() + + device = query.device + if self.local_grid_size > 0: + xy_target = get_points_on_a_grid( + self.local_grid_size, + (50, 50), + [query[0, 0, 2].item(), query[0, 0, 1].item()], + ) + + xy_target = torch.cat([torch.zeros_like(xy_target[:, :, :1]), xy_target], dim=2).to( + device + ) # + query = torch.cat([query, xy_target], dim=1) # + + if self.grid_size > 0: + xy = get_points_on_a_grid(self.grid_size, video.shape[3:]) + xy = torch.cat([torch.zeros_like(xy[:, :, :1]), xy], dim=2).to(device) # + query = torch.cat([query, xy], dim=1) # + # crop the video to start from the queried frame + query[0, 0, 0] = 0 + traj_e_pind, vis_e_pind, __ = self.model( + video=video[:, t:], queries=query, iters=self.n_iters + ) + + return traj_e_pind, vis_e_pind diff --git a/cotracker/build/lib/utils/__init__.py b/cotracker/build/lib/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/build/lib/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/build/lib/utils/visualizer.py b/cotracker/build/lib/utils/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..c5e78615242c20d192faab616c702f629dcef5b8 --- /dev/null +++ b/cotracker/build/lib/utils/visualizer.py @@ -0,0 +1,375 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import os +import numpy as np +import imageio +import torch + +from matplotlib import cm +import torch.nn.functional as F +import torchvision.transforms as transforms +import matplotlib.pyplot as plt +from PIL import Image, ImageDraw +# import av +# import decord +import torchvision +from einops import rearrange + + +def read_video_from_path(path): + # try: + # reader = imageio.get_reader(path) + # except Exception as e: + # print("Error opening video file: ", e) + # return None + # frames = [] + # for i, im in enumerate(reader): + # frames.append(np.array(im)) + # return np.stack(frames) + + # # read videe using decord + # video = decord.VideoReader(path) + # frames = video.get_batch(range(len(video))) + # frames = [frame.asnumpy() for frame in frames] + # return np.stack(frames) + + # read video using torchvision + vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='THWC') + vframes = vframes.numpy() + return vframes + + + +def draw_circle(rgb, coord, radius, color=(255, 0, 0), visible=True): + # Create a draw object + draw = ImageDraw.Draw(rgb) + # Calculate the bounding box of the circle + left_up_point = (coord[0] - radius, coord[1] - radius) + right_down_point = (coord[0] + radius, coord[1] + radius) + # Draw the circle + draw.ellipse( + [left_up_point, right_down_point], + fill=tuple(color) if visible else None, + outline=tuple(color), + ) + return rgb + + +def draw_line(rgb, coord_y, coord_x, color, linewidth): + draw = ImageDraw.Draw(rgb) + draw.line( + (coord_y[0], coord_y[1], coord_x[0], coord_x[1]), + fill=tuple(color), + width=linewidth, + ) + return rgb + + +def add_weighted(rgb, alpha, original, beta, gamma): + return (rgb * alpha + original * beta + gamma).astype("uint8") + + +class Visualizer: + def __init__( + self, + save_dir: str = "./results", + grayscale: bool = False, + pad_value: int = 0, + fps: int = 10, + mode: str = "rainbow", # 'cool', 'optical_flow' + linewidth: int = 2, + show_first_frame: int = 10, + tracks_leave_trace: int = 0, # -1 for infinite + ): + self.mode = mode + self.save_dir = save_dir + if mode == "rainbow": + self.color_map = cm.get_cmap("gist_rainbow") + elif mode == "cool": + self.color_map = cm.get_cmap(mode) + self.show_first_frame = show_first_frame + self.grayscale = grayscale + self.tracks_leave_trace = tracks_leave_trace + self.pad_value = pad_value + self.linewidth = linewidth + self.fps = fps + + def visualize( + self, + video: torch.Tensor, # (B,T,C,H,W) + tracks: torch.Tensor, # (B,T,N,2) + visibility: torch.Tensor = None, # (B, T, N, 1) bool + gt_tracks: torch.Tensor = None, # (B,T,N,2) + segm_mask: torch.Tensor = None, # (B,1,H,W) + filename: str = "video", + writer=None, # tensorboard Summary Writer, used for visualization during training + step: int = 0, + query_frame: int = 0, + save_video: bool = True, + compensate_for_camera_motion: bool = False, + ): + if compensate_for_camera_motion: + assert segm_mask is not None + if segm_mask is not None: + coords = tracks[0, query_frame].round().long() + segm_mask = segm_mask[0, query_frame][coords[:, 1], coords[:, 0]].long() + + video = F.pad( + video, + (self.pad_value, self.pad_value, self.pad_value, self.pad_value), + "constant", + 255, + ) + tracks = tracks + self.pad_value + + if self.grayscale: + transform = transforms.Grayscale() + video = transform(video) + video = video.repeat(1, 1, 3, 1, 1) + + res_video = self.draw_tracks_on_video( + video=video, + tracks=tracks, + visibility=visibility, + segm_mask=segm_mask, + gt_tracks=gt_tracks, + query_frame=query_frame, + compensate_for_camera_motion=compensate_for_camera_motion, + ) + if save_video: + self.save_video(res_video, filename=filename, writer=writer, step=step) + return res_video + + def save_video(self, video, filename, writer=None, step=0): + if writer is not None: + writer.add_video( + filename, + video.to(torch.uint8), + global_step=step, + fps=self.fps, + ) + else: + os.makedirs(self.save_dir, exist_ok=True) + + # Prepare the video file path + save_path = os.path.join(self.save_dir, f"{filename}.mp4") + # save video using torchvision + assert video.shape[0] == 1 + video = rearrange(video[0], 'T C H W -> T H W C') + torchvision.io.write_video(save_path, video, fps=self.fps) + + # wide_list = list(video.unbind(1)) + # wide_list = [wide[0].permute(1, 2, 0).cpu().numpy() for wide in wide_list] + + # # Create a writer object + # video_writer = imageio.get_writer(save_path, fps=self.fps) + + # # Write frames to the video file + # for frame in wide_list[2:-1]: + # video_writer.append_data(frame) + + # video_writer.close() + + # # pyav + # container = av.open(save_path, mode="w") + # stream = container.add_stream("h264", rate=self.fps) + # for frame in wide_list[2:-1]: + # frame = Image.fromarray(frame) + # frame = np.array(frame) + # frame = av.VideoFrame.from_ndarray(frame, format="rgb24") + # for packet in stream.encode(frame): + # container.mux(packet) + + print(f"Video saved to {save_path}") + + def draw_tracks_on_video( + self, + video: torch.Tensor, + tracks: torch.Tensor, + visibility: torch.Tensor = None, + segm_mask: torch.Tensor = None, + gt_tracks=None, + query_frame: int = 0, + compensate_for_camera_motion=False, + ): + B, T, C, H, W = video.shape + _, _, N, D = tracks.shape + + assert D == 2 + assert C == 3 + video = video[0].permute(0, 2, 3, 1).byte().detach().cpu().numpy() # S, H, W, C + tracks = tracks[0].long().detach().cpu().numpy() # S, N, 2 + if gt_tracks is not None: + gt_tracks = gt_tracks[0].detach().cpu().numpy() + + res_video = [] + + # process input video + for rgb in video: + res_video.append(rgb.copy()) + vector_colors = np.zeros((T, N, 3)) + + # define vector colors + if self.mode == "optical_flow": + import flow_vis + + vector_colors = flow_vis.flow_to_color(tracks - tracks[query_frame][None]) + elif segm_mask is None: + if self.mode == "rainbow": + y_min, y_max = ( + tracks[query_frame, :, 1].min(), + tracks[query_frame, :, 1].max(), + ) + norm = plt.Normalize(y_min, y_max) + for n in range(N): + color = self.color_map(norm(tracks[query_frame, n, 1])) + color = np.array(color[:3])[None] * 255 + vector_colors[:, n] = np.repeat(color, T, axis=0) + else: + # color changes with time + for t in range(T): + color = np.array(self.color_map(t / T)[:3])[None] * 255 + vector_colors[t] = np.repeat(color, N, axis=0) + else: + if self.mode == "rainbow": + vector_colors[:, segm_mask <= 0, :] = 255 + + y_min, y_max = ( + tracks[0, segm_mask > 0, 1].min(), + tracks[0, segm_mask > 0, 1].max(), + ) + norm = plt.Normalize(y_min, y_max) + for n in range(N): + if segm_mask[n] > 0: + color = self.color_map(norm(tracks[0, n, 1])) + color = np.array(color[:3])[None] * 255 + vector_colors[:, n] = np.repeat(color, T, axis=0) + + else: + # color changes with segm class + segm_mask = segm_mask.cpu() + color = np.zeros((segm_mask.shape[0], 3), dtype=np.float32) + color[segm_mask > 0] = np.array(self.color_map(1.0)[:3]) * 255.0 + color[segm_mask <= 0] = np.array(self.color_map(0.0)[:3]) * 255.0 + vector_colors = np.repeat(color[None], T, axis=0) + + # draw tracks + if self.tracks_leave_trace != 0: + for t in range(query_frame + 1, T): + first_ind = ( + max(0, t - self.tracks_leave_trace) if self.tracks_leave_trace >= 0 else 0 + ) + curr_tracks = tracks[first_ind : t + 1] + curr_colors = vector_colors[first_ind : t + 1] + if compensate_for_camera_motion: + diff = ( + tracks[first_ind : t + 1, segm_mask <= 0] + - tracks[t : t + 1, segm_mask <= 0] + ).mean(1)[:, None] + + curr_tracks = curr_tracks - diff + curr_tracks = curr_tracks[:, segm_mask > 0] + curr_colors = curr_colors[:, segm_mask > 0] + + res_video[t] = self._draw_pred_tracks( + res_video[t], + curr_tracks, + curr_colors, + ) + if gt_tracks is not None: + res_video[t] = self._draw_gt_tracks(res_video[t], gt_tracks[first_ind : t + 1]) + + # draw points + for t in range(query_frame, T): + img = Image.fromarray(np.uint8(res_video[t])) + for i in range(N): + coord = (tracks[t, i, 0], tracks[t, i, 1]) + visibile = True + if visibility is not None: + visibile = visibility[0, t, i] + if coord[0] != 0 and coord[1] != 0: + if not compensate_for_camera_motion or ( + compensate_for_camera_motion and segm_mask[i] > 0 + ): + img = draw_circle( + img, + coord=coord, + radius=int(self.linewidth * 2), + color=vector_colors[t, i].astype(int), + visible=visibile, + ) + res_video[t] = np.array(img) + + # construct the final rgb sequence + if self.show_first_frame > 0: + res_video = [res_video[0]] * self.show_first_frame + res_video[1:] + return torch.from_numpy(np.stack(res_video)).permute(0, 3, 1, 2)[None].byte() + + def _draw_pred_tracks( + self, + rgb: np.ndarray, # H x W x 3 + tracks: np.ndarray, # T x 2 + vector_colors: np.ndarray, + alpha: float = 0.5, + ): + T, N, _ = tracks.shape + rgb = Image.fromarray(np.uint8(rgb)) + for s in range(T - 1): + vector_color = vector_colors[s] + original = rgb.copy() + alpha = (s / T) ** 2 + for i in range(N): + coord_y = (int(tracks[s, i, 0]), int(tracks[s, i, 1])) + coord_x = (int(tracks[s + 1, i, 0]), int(tracks[s + 1, i, 1])) + if coord_y[0] != 0 and coord_y[1] != 0: + rgb = draw_line( + rgb, + coord_y, + coord_x, + vector_color[i].astype(int), + self.linewidth, + ) + if self.tracks_leave_trace > 0: + rgb = Image.fromarray( + np.uint8(add_weighted(np.array(rgb), alpha, np.array(original), 1 - alpha, 0)) + ) + rgb = np.array(rgb) + return rgb + + def _draw_gt_tracks( + self, + rgb: np.ndarray, # H x W x 3, + gt_tracks: np.ndarray, # T x 2 + ): + T, N, _ = gt_tracks.shape + color = np.array((211, 0, 0)) + rgb = Image.fromarray(np.uint8(rgb)) + for t in range(T): + for i in range(N): + gt_tracks = gt_tracks[t][i] + # draw a red cross + if gt_tracks[0] > 0 and gt_tracks[1] > 0: + length = self.linewidth * 3 + coord_y = (int(gt_tracks[0]) + length, int(gt_tracks[1]) + length) + coord_x = (int(gt_tracks[0]) - length, int(gt_tracks[1]) - length) + rgb = draw_line( + rgb, + coord_y, + coord_x, + color, + self.linewidth, + ) + coord_y = (int(gt_tracks[0]) - length, int(gt_tracks[1]) + length) + coord_x = (int(gt_tracks[0]) + length, int(gt_tracks[1]) - length) + rgb = draw_line( + rgb, + coord_y, + coord_x, + color, + self.linewidth, + ) + rgb = np.array(rgb) + return rgb diff --git a/cotracker/datasets/__init__.py b/cotracker/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/datasets/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/datasets/dataclass_utils.py b/cotracker/datasets/dataclass_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..11e103b6002b4ecf72b463a829fe16d31cc65cff --- /dev/null +++ b/cotracker/datasets/dataclass_utils.py @@ -0,0 +1,166 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import json +import dataclasses +import numpy as np +from dataclasses import Field, MISSING +from typing import IO, TypeVar, Type, get_args, get_origin, Union, Any, Tuple + +_X = TypeVar("_X") + + +def load_dataclass(f: IO, cls: Type[_X], binary: bool = False) -> _X: + """ + Loads to a @dataclass or collection hierarchy including dataclasses + from a json recursively. + Call it like load_dataclass(f, typing.List[FrameAnnotationAnnotation]). + raises KeyError if json has keys not mapping to the dataclass fields. + + Args: + f: Either a path to a file, or a file opened for writing. + cls: The class of the loaded dataclass. + binary: Set to True if `f` is a file handle, else False. + """ + if binary: + asdict = json.loads(f.read().decode("utf8")) + else: + asdict = json.load(f) + + # in the list case, run a faster "vectorized" version + cls = get_args(cls)[0] + res = list(_dataclass_list_from_dict_list(asdict, cls)) + + return res + + +def _resolve_optional(type_: Any) -> Tuple[bool, Any]: + """Check whether `type_` is equivalent to `typing.Optional[T]` for some T.""" + if get_origin(type_) is Union: + args = get_args(type_) + if len(args) == 2 and args[1] == type(None): # noqa E721 + return True, args[0] + if type_ is Any: + return True, Any + + return False, type_ + + +def _unwrap_type(tp): + # strips Optional wrapper, if any + if get_origin(tp) is Union: + args = get_args(tp) + if len(args) == 2 and any(a is type(None) for a in args): # noqa: E721 + # this is typing.Optional + return args[0] if args[1] is type(None) else args[1] # noqa: E721 + return tp + + +def _get_dataclass_field_default(field: Field) -> Any: + if field.default_factory is not MISSING: + # pyre-fixme[29]: `Union[dataclasses._MISSING_TYPE, + # dataclasses._DefaultFactory[typing.Any]]` is not a function. + return field.default_factory() + elif field.default is not MISSING: + return field.default + else: + return None + + +def _dataclass_list_from_dict_list(dlist, typeannot): + """ + Vectorised version of `_dataclass_from_dict`. + The output should be equivalent to + `[_dataclass_from_dict(d, typeannot) for d in dlist]`. + + Args: + dlist: list of objects to convert. + typeannot: type of each of those objects. + Returns: + iterator or list over converted objects of the same length as `dlist`. + + Raises: + ValueError: it assumes the objects have None's in consistent places across + objects, otherwise it would ignore some values. This generally holds for + auto-generated annotations, but otherwise use `_dataclass_from_dict`. + """ + + cls = get_origin(typeannot) or typeannot + + if typeannot is Any: + return dlist + if all(obj is None for obj in dlist): # 1st recursion base: all None nodes + return dlist + if any(obj is None for obj in dlist): + # filter out Nones and recurse on the resulting list + idx_notnone = [(i, obj) for i, obj in enumerate(dlist) if obj is not None] + idx, notnone = zip(*idx_notnone) + converted = _dataclass_list_from_dict_list(notnone, typeannot) + res = [None] * len(dlist) + for i, obj in zip(idx, converted): + res[i] = obj + return res + + is_optional, contained_type = _resolve_optional(typeannot) + if is_optional: + return _dataclass_list_from_dict_list(dlist, contained_type) + + # otherwise, we dispatch by the type of the provided annotation to convert to + if issubclass(cls, tuple) and hasattr(cls, "_fields"): # namedtuple + # For namedtuple, call the function recursively on the lists of corresponding keys + types = cls.__annotations__.values() + dlist_T = zip(*dlist) + res_T = [ + _dataclass_list_from_dict_list(key_list, tp) for key_list, tp in zip(dlist_T, types) + ] + return [cls(*converted_as_tuple) for converted_as_tuple in zip(*res_T)] + elif issubclass(cls, (list, tuple)): + # For list/tuple, call the function recursively on the lists of corresponding positions + types = get_args(typeannot) + if len(types) == 1: # probably List; replicate for all items + types = types * len(dlist[0]) + dlist_T = zip(*dlist) + res_T = ( + _dataclass_list_from_dict_list(pos_list, tp) for pos_list, tp in zip(dlist_T, types) + ) + if issubclass(cls, tuple): + return list(zip(*res_T)) + else: + return [cls(converted_as_tuple) for converted_as_tuple in zip(*res_T)] + elif issubclass(cls, dict): + # For the dictionary, call the function recursively on concatenated keys and vertices + key_t, val_t = get_args(typeannot) + all_keys_res = _dataclass_list_from_dict_list( + [k for obj in dlist for k in obj.keys()], key_t + ) + all_vals_res = _dataclass_list_from_dict_list( + [k for obj in dlist for k in obj.values()], val_t + ) + indices = np.cumsum([len(obj) for obj in dlist]) + assert indices[-1] == len(all_keys_res) + + keys = np.split(list(all_keys_res), indices[:-1]) + all_vals_res_iter = iter(all_vals_res) + return [cls(zip(k, all_vals_res_iter)) for k in keys] + elif not dataclasses.is_dataclass(typeannot): + return dlist + + # dataclass node: 2nd recursion base; call the function recursively on the lists + # of the corresponding fields + assert dataclasses.is_dataclass(cls) + fieldtypes = { + f.name: (_unwrap_type(f.type), _get_dataclass_field_default(f)) + for f in dataclasses.fields(typeannot) + } + + # NOTE the default object is shared here + key_lists = ( + _dataclass_list_from_dict_list([obj.get(k, default) for obj in dlist], type_) + for k, (type_, default) in fieldtypes.items() + ) + transposed = zip(*key_lists) + return [cls(*vals_as_tuple) for vals_as_tuple in transposed] diff --git a/cotracker/datasets/dr_dataset.py b/cotracker/datasets/dr_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..70af653e8852ae4b70776beba3bf12a324723f5a --- /dev/null +++ b/cotracker/datasets/dr_dataset.py @@ -0,0 +1,161 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import gzip +import torch +import numpy as np +import torch.utils.data as data +from collections import defaultdict +from dataclasses import dataclass +from typing import List, Optional, Any, Dict, Tuple + +from cotracker.datasets.utils import CoTrackerData +from cotracker.datasets.dataclass_utils import load_dataclass + + +@dataclass +class ImageAnnotation: + # path to jpg file, relative w.r.t. dataset_root + path: str + # H x W + size: Tuple[int, int] + + +@dataclass +class DynamicReplicaFrameAnnotation: + """A dataclass used to load annotations from json.""" + + # can be used to join with `SequenceAnnotation` + sequence_name: str + # 0-based, continuous frame number within sequence + frame_number: int + # timestamp in seconds from the video start + frame_timestamp: float + + image: ImageAnnotation + meta: Optional[Dict[str, Any]] = None + + camera_name: Optional[str] = None + trajectories: Optional[str] = None + + +class DynamicReplicaDataset(data.Dataset): + def __init__( + self, + root, + split="valid", + traj_per_sample=256, + crop_size=None, + sample_len=-1, + only_first_n_samples=-1, + rgbd_input=False, + ): + super(DynamicReplicaDataset, self).__init__() + self.root = root + self.sample_len = sample_len + self.split = split + self.traj_per_sample = traj_per_sample + self.rgbd_input = rgbd_input + self.crop_size = crop_size + frame_annotations_file = f"frame_annotations_{split}.jgz" + self.sample_list = [] + with gzip.open( + os.path.join(root, split, frame_annotations_file), "rt", encoding="utf8" + ) as zipfile: + frame_annots_list = load_dataclass(zipfile, List[DynamicReplicaFrameAnnotation]) + seq_annot = defaultdict(list) + for frame_annot in frame_annots_list: + if frame_annot.camera_name == "left": + seq_annot[frame_annot.sequence_name].append(frame_annot) + + for seq_name in seq_annot.keys(): + seq_len = len(seq_annot[seq_name]) + + step = self.sample_len if self.sample_len > 0 else seq_len + counter = 0 + + for ref_idx in range(0, seq_len, step): + sample = seq_annot[seq_name][ref_idx : ref_idx + step] + self.sample_list.append(sample) + counter += 1 + if only_first_n_samples > 0 and counter >= only_first_n_samples: + break + + def __len__(self): + return len(self.sample_list) + + def crop(self, rgbs, trajs): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + H_new = H + W_new = W + + # simple random crop + y0 = 0 if self.crop_size[0] >= H_new else (H_new - self.crop_size[0]) // 2 + x0 = 0 if self.crop_size[1] >= W_new else (W_new - self.crop_size[1]) // 2 + rgbs = [rgb[y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] for rgb in rgbs] + + trajs[:, :, 0] -= x0 + trajs[:, :, 1] -= y0 + + return rgbs, trajs + + def __getitem__(self, index): + sample = self.sample_list[index] + T = len(sample) + rgbs, visibilities, traj_2d = [], [], [] + + H, W = sample[0].image.size + image_size = (H, W) + + for i in range(T): + traj_path = os.path.join(self.root, self.split, sample[i].trajectories["path"]) + traj = torch.load(traj_path) + + visibilities.append(traj["verts_inds_vis"].numpy()) + + rgbs.append(traj["img"].numpy()) + traj_2d.append(traj["traj_2d"].numpy()[..., :2]) + + traj_2d = np.stack(traj_2d) + visibility = np.stack(visibilities) + T, N, D = traj_2d.shape + # subsample trajectories for augmentations + visible_inds_sampled = torch.randperm(N)[: self.traj_per_sample] + + traj_2d = traj_2d[:, visible_inds_sampled] + visibility = visibility[:, visible_inds_sampled] + + if self.crop_size is not None: + rgbs, traj_2d = self.crop(rgbs, traj_2d) + H, W, _ = rgbs[0].shape + image_size = self.crop_size + + visibility[traj_2d[:, :, 0] > image_size[1] - 1] = False + visibility[traj_2d[:, :, 0] < 0] = False + visibility[traj_2d[:, :, 1] > image_size[0] - 1] = False + visibility[traj_2d[:, :, 1] < 0] = False + + # filter out points that're visible for less than 10 frames + visible_inds_resampled = visibility.sum(0) > 10 + traj_2d = torch.from_numpy(traj_2d[:, visible_inds_resampled]) + visibility = torch.from_numpy(visibility[:, visible_inds_resampled]) + + rgbs = np.stack(rgbs, 0) + video = torch.from_numpy(rgbs).reshape(T, H, W, 3).permute(0, 3, 1, 2).float() + return CoTrackerData( + video=video, + trajectory=traj_2d, + visibility=visibility, + valid=torch.ones(T, N), + seq_name=sample[0].sequence_name, + ) diff --git a/cotracker/datasets/kubric_movif_dataset.py b/cotracker/datasets/kubric_movif_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..366d7383e2797359500508448806f39d8b298ac5 --- /dev/null +++ b/cotracker/datasets/kubric_movif_dataset.py @@ -0,0 +1,441 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import cv2 + +import imageio +import numpy as np + +from cotracker.datasets.utils import CoTrackerData +from torchvision.transforms import ColorJitter, GaussianBlur +from PIL import Image + + +class CoTrackerDataset(torch.utils.data.Dataset): + def __init__( + self, + data_root, + crop_size=(384, 512), + seq_len=24, + traj_per_sample=768, + sample_vis_1st_frame=False, + use_augs=False, + ): + super(CoTrackerDataset, self).__init__() + np.random.seed(0) + torch.manual_seed(0) + self.data_root = data_root + self.seq_len = seq_len + self.traj_per_sample = traj_per_sample + self.sample_vis_1st_frame = sample_vis_1st_frame + self.use_augs = use_augs + self.crop_size = crop_size + + # photometric augmentation + self.photo_aug = ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.25 / 3.14) + self.blur_aug = GaussianBlur(11, sigma=(0.1, 2.0)) + + self.blur_aug_prob = 0.25 + self.color_aug_prob = 0.25 + + # occlusion augmentation + self.eraser_aug_prob = 0.5 + self.eraser_bounds = [2, 100] + self.eraser_max = 10 + + # occlusion augmentation + self.replace_aug_prob = 0.5 + self.replace_bounds = [2, 100] + self.replace_max = 10 + + # spatial augmentations + self.pad_bounds = [0, 100] + self.crop_size = crop_size + self.resize_lim = [0.25, 2.0] # sample resizes from here + self.resize_delta = 0.2 + self.max_crop_offset = 50 + + self.do_flip = True + self.h_flip_prob = 0.5 + self.v_flip_prob = 0.5 + + def getitem_helper(self, index): + return NotImplementedError + + def __getitem__(self, index): + gotit = False + + sample, gotit = self.getitem_helper(index) + if not gotit: + print("warning: sampling failed") + # fake sample, so we can still collate + sample = CoTrackerData( + video=torch.zeros((self.seq_len, 3, self.crop_size[0], self.crop_size[1])), + trajectory=torch.zeros((self.seq_len, self.traj_per_sample, 2)), + visibility=torch.zeros((self.seq_len, self.traj_per_sample)), + valid=torch.zeros((self.seq_len, self.traj_per_sample)), + ) + + return sample, gotit + + def add_photometric_augs(self, rgbs, trajs, visibles, eraser=True, replace=True): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + if eraser: + ############ eraser transform (per image after the first) ############ + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + for i in range(1, S): + if np.random.rand() < self.eraser_aug_prob: + for _ in range( + np.random.randint(1, self.eraser_max + 1) + ): # number of times to occlude + xc = np.random.randint(0, W) + yc = np.random.randint(0, H) + dx = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1]) + dy = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1]) + x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32) + x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32) + y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32) + y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32) + + mean_color = np.mean(rgbs[i][y0:y1, x0:x1, :].reshape(-1, 3), axis=0) + rgbs[i][y0:y1, x0:x1, :] = mean_color + + occ_inds = np.logical_and( + np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1), + np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1), + ) + visibles[i, occ_inds] = 0 + rgbs = [rgb.astype(np.uint8) for rgb in rgbs] + + if replace: + rgbs_alt = [ + np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs + ] + rgbs_alt = [ + np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs_alt + ] + + ############ replace transform (per image after the first) ############ + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + rgbs_alt = [rgb.astype(np.float32) for rgb in rgbs_alt] + for i in range(1, S): + if np.random.rand() < self.replace_aug_prob: + for _ in range( + np.random.randint(1, self.replace_max + 1) + ): # number of times to occlude + xc = np.random.randint(0, W) + yc = np.random.randint(0, H) + dx = np.random.randint(self.replace_bounds[0], self.replace_bounds[1]) + dy = np.random.randint(self.replace_bounds[0], self.replace_bounds[1]) + x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32) + x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32) + y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32) + y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32) + + wid = x1 - x0 + hei = y1 - y0 + y00 = np.random.randint(0, H - hei) + x00 = np.random.randint(0, W - wid) + fr = np.random.randint(0, S) + rep = rgbs_alt[fr][y00 : y00 + hei, x00 : x00 + wid, :] + rgbs[i][y0:y1, x0:x1, :] = rep + + occ_inds = np.logical_and( + np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1), + np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1), + ) + visibles[i, occ_inds] = 0 + rgbs = [rgb.astype(np.uint8) for rgb in rgbs] + + ############ photometric augmentation ############ + if np.random.rand() < self.color_aug_prob: + # random per-frame amount of aug + rgbs = [np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs] + + if np.random.rand() < self.blur_aug_prob: + # random per-frame amount of blur + rgbs = [np.array(self.blur_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs] + + return rgbs, trajs, visibles + + def add_spatial_augs(self, rgbs, trajs, visibles): + T, N, __ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + rgbs = [rgb.astype(np.float32) for rgb in rgbs] + + ############ spatial transform ############ + + # padding + pad_x0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_x1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_y0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + pad_y1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1]) + + rgbs = [np.pad(rgb, ((pad_y0, pad_y1), (pad_x0, pad_x1), (0, 0))) for rgb in rgbs] + trajs[:, :, 0] += pad_x0 + trajs[:, :, 1] += pad_y0 + H, W = rgbs[0].shape[:2] + + # scaling + stretching + scale = np.random.uniform(self.resize_lim[0], self.resize_lim[1]) + scale_x = scale + scale_y = scale + H_new = H + W_new = W + + scale_delta_x = 0.0 + scale_delta_y = 0.0 + + rgbs_scaled = [] + for s in range(S): + if s == 1: + scale_delta_x = np.random.uniform(-self.resize_delta, self.resize_delta) + scale_delta_y = np.random.uniform(-self.resize_delta, self.resize_delta) + elif s > 1: + scale_delta_x = ( + scale_delta_x * 0.8 + + np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2 + ) + scale_delta_y = ( + scale_delta_y * 0.8 + + np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2 + ) + scale_x = scale_x + scale_delta_x + scale_y = scale_y + scale_delta_y + + # bring h/w closer + scale_xy = (scale_x + scale_y) * 0.5 + scale_x = scale_x * 0.5 + scale_xy * 0.5 + scale_y = scale_y * 0.5 + scale_xy * 0.5 + + # don't get too crazy + scale_x = np.clip(scale_x, 0.2, 2.0) + scale_y = np.clip(scale_y, 0.2, 2.0) + + H_new = int(H * scale_y) + W_new = int(W * scale_x) + + # make it at least slightly bigger than the crop area, + # so that the random cropping can add diversity + H_new = np.clip(H_new, self.crop_size[0] + 10, None) + W_new = np.clip(W_new, self.crop_size[1] + 10, None) + # recompute scale in case we clipped + scale_x = (W_new - 1) / float(W - 1) + scale_y = (H_new - 1) / float(H - 1) + rgbs_scaled.append(cv2.resize(rgbs[s], (W_new, H_new), interpolation=cv2.INTER_LINEAR)) + trajs[s, :, 0] *= scale_x + trajs[s, :, 1] *= scale_y + rgbs = rgbs_scaled + + ok_inds = visibles[0, :] > 0 + vis_trajs = trajs[:, ok_inds] # S,?,2 + + if vis_trajs.shape[1] > 0: + mid_x = np.mean(vis_trajs[0, :, 0]) + mid_y = np.mean(vis_trajs[0, :, 1]) + else: + mid_y = self.crop_size[0] + mid_x = self.crop_size[1] + + x0 = int(mid_x - self.crop_size[1] // 2) + y0 = int(mid_y - self.crop_size[0] // 2) + + offset_x = 0 + offset_y = 0 + + for s in range(S): + # on each frame, shift a bit more + if s == 1: + offset_x = np.random.randint(-self.max_crop_offset, self.max_crop_offset) + offset_y = np.random.randint(-self.max_crop_offset, self.max_crop_offset) + elif s > 1: + offset_x = int( + offset_x * 0.8 + + np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2 + ) + offset_y = int( + offset_y * 0.8 + + np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2 + ) + x0 = x0 + offset_x + y0 = y0 + offset_y + + H_new, W_new = rgbs[s].shape[:2] + if H_new == self.crop_size[0]: + y0 = 0 + else: + y0 = min(max(0, y0), H_new - self.crop_size[0] - 1) + + if W_new == self.crop_size[1]: + x0 = 0 + else: + x0 = min(max(0, x0), W_new - self.crop_size[1] - 1) + + rgbs[s] = rgbs[s][y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] + trajs[s, :, 0] -= x0 + trajs[s, :, 1] -= y0 + + H_new = self.crop_size[0] + W_new = self.crop_size[1] + + # flip + h_flipped = False + v_flipped = False + if self.do_flip: + # h flip + if np.random.rand() < self.h_flip_prob: + h_flipped = True + rgbs = [rgb[:, ::-1] for rgb in rgbs] + # v flip + if np.random.rand() < self.v_flip_prob: + v_flipped = True + rgbs = [rgb[::-1] for rgb in rgbs] + if h_flipped: + trajs[:, :, 0] = W_new - trajs[:, :, 0] + if v_flipped: + trajs[:, :, 1] = H_new - trajs[:, :, 1] + + return rgbs, trajs + + def crop(self, rgbs, trajs): + T, N, _ = trajs.shape + + S = len(rgbs) + H, W = rgbs[0].shape[:2] + assert S == T + + ############ spatial transform ############ + + H_new = H + W_new = W + + # simple random crop + y0 = 0 if self.crop_size[0] >= H_new else np.random.randint(0, H_new - self.crop_size[0]) + x0 = 0 if self.crop_size[1] >= W_new else np.random.randint(0, W_new - self.crop_size[1]) + rgbs = [rgb[y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] for rgb in rgbs] + + trajs[:, :, 0] -= x0 + trajs[:, :, 1] -= y0 + + return rgbs, trajs + + +class KubricMovifDataset(CoTrackerDataset): + def __init__( + self, + data_root, + crop_size=(384, 512), + seq_len=24, + traj_per_sample=768, + sample_vis_1st_frame=False, + use_augs=False, + ): + super(KubricMovifDataset, self).__init__( + data_root=data_root, + crop_size=crop_size, + seq_len=seq_len, + traj_per_sample=traj_per_sample, + sample_vis_1st_frame=sample_vis_1st_frame, + use_augs=use_augs, + ) + + self.pad_bounds = [0, 25] + self.resize_lim = [0.75, 1.25] # sample resizes from here + self.resize_delta = 0.05 + self.max_crop_offset = 15 + self.seq_names = [ + fname + for fname in os.listdir(data_root) + if os.path.isdir(os.path.join(data_root, fname)) + ] + print("found %d unique videos in %s" % (len(self.seq_names), self.data_root)) + + def getitem_helper(self, index): + gotit = True + seq_name = self.seq_names[index] + + npy_path = os.path.join(self.data_root, seq_name, seq_name + ".npy") + rgb_path = os.path.join(self.data_root, seq_name, "frames") + + img_paths = sorted(os.listdir(rgb_path)) + rgbs = [] + for i, img_path in enumerate(img_paths): + rgbs.append(imageio.v2.imread(os.path.join(rgb_path, img_path))) + + rgbs = np.stack(rgbs) + annot_dict = np.load(npy_path, allow_pickle=True).item() + traj_2d = annot_dict["coords"] + visibility = annot_dict["visibility"] + + # random crop + assert self.seq_len <= len(rgbs) + if self.seq_len < len(rgbs): + start_ind = np.random.choice(len(rgbs) - self.seq_len, 1)[0] + + rgbs = rgbs[start_ind : start_ind + self.seq_len] + traj_2d = traj_2d[:, start_ind : start_ind + self.seq_len] + visibility = visibility[:, start_ind : start_ind + self.seq_len] + + traj_2d = np.transpose(traj_2d, (1, 0, 2)) + visibility = np.transpose(np.logical_not(visibility), (1, 0)) + if self.use_augs: + rgbs, traj_2d, visibility = self.add_photometric_augs(rgbs, traj_2d, visibility) + rgbs, traj_2d = self.add_spatial_augs(rgbs, traj_2d, visibility) + else: + rgbs, traj_2d = self.crop(rgbs, traj_2d) + + visibility[traj_2d[:, :, 0] > self.crop_size[1] - 1] = False + visibility[traj_2d[:, :, 0] < 0] = False + visibility[traj_2d[:, :, 1] > self.crop_size[0] - 1] = False + visibility[traj_2d[:, :, 1] < 0] = False + + visibility = torch.from_numpy(visibility) + traj_2d = torch.from_numpy(traj_2d) + + visibile_pts_first_frame_inds = (visibility[0]).nonzero(as_tuple=False)[:, 0] + + if self.sample_vis_1st_frame: + visibile_pts_inds = visibile_pts_first_frame_inds + else: + visibile_pts_mid_frame_inds = (visibility[self.seq_len // 2]).nonzero(as_tuple=False)[ + :, 0 + ] + visibile_pts_inds = torch.cat( + (visibile_pts_first_frame_inds, visibile_pts_mid_frame_inds), dim=0 + ) + point_inds = torch.randperm(len(visibile_pts_inds))[: self.traj_per_sample] + if len(point_inds) < self.traj_per_sample: + gotit = False + + visible_inds_sampled = visibile_pts_inds[point_inds] + + trajs = traj_2d[:, visible_inds_sampled].float() + visibles = visibility[:, visible_inds_sampled] + valids = torch.ones((self.seq_len, self.traj_per_sample)) + + rgbs = torch.from_numpy(np.stack(rgbs)).permute(0, 3, 1, 2).float() + sample = CoTrackerData( + video=rgbs, + trajectory=trajs, + visibility=visibles, + valid=valids, + seq_name=seq_name, + ) + return sample, gotit + + def __len__(self): + return len(self.seq_names) diff --git a/cotracker/datasets/tap_vid_datasets.py b/cotracker/datasets/tap_vid_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..72e000177c95fb54b1dba22d2dd96e9db9f0096e --- /dev/null +++ b/cotracker/datasets/tap_vid_datasets.py @@ -0,0 +1,209 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import io +import glob +import torch +import pickle +import numpy as np +import mediapy as media + +from PIL import Image +from typing import Mapping, Tuple, Union + +from cotracker.datasets.utils import CoTrackerData + +DatasetElement = Mapping[str, Mapping[str, Union[np.ndarray, str]]] + + +def resize_video(video: np.ndarray, output_size: Tuple[int, int]) -> np.ndarray: + """Resize a video to output_size.""" + # If you have a GPU, consider replacing this with a GPU-enabled resize op, + # such as a jitted jax.image.resize. It will make things faster. + return media.resize_video(video, output_size) + + +def sample_queries_first( + target_occluded: np.ndarray, + target_points: np.ndarray, + frames: np.ndarray, +) -> Mapping[str, np.ndarray]: + """Package a set of frames and tracks for use in TAPNet evaluations. + Given a set of frames and tracks with no query points, use the first + visible point in each track as the query. + Args: + target_occluded: Boolean occlusion flag, of shape [n_tracks, n_frames], + where True indicates occluded. + target_points: Position, of shape [n_tracks, n_frames, 2], where each point + is [x,y] scaled between 0 and 1. + frames: Video tensor, of shape [n_frames, height, width, 3]. Scaled between + -1 and 1. + Returns: + A dict with the keys: + video: Video tensor of shape [1, n_frames, height, width, 3] + query_points: Query points of shape [1, n_queries, 3] where + each point is [t, y, x] scaled to the range [-1, 1] + target_points: Target points of shape [1, n_queries, n_frames, 2] where + each point is [x, y] scaled to the range [-1, 1] + """ + valid = np.sum(~target_occluded, axis=1) > 0 + target_points = target_points[valid, :] + target_occluded = target_occluded[valid, :] + + query_points = [] + for i in range(target_points.shape[0]): + index = np.where(target_occluded[i] == 0)[0][0] + x, y = target_points[i, index, 0], target_points[i, index, 1] + query_points.append(np.array([index, y, x])) # [t, y, x] + query_points = np.stack(query_points, axis=0) + + return { + "video": frames[np.newaxis, ...], + "query_points": query_points[np.newaxis, ...], + "target_points": target_points[np.newaxis, ...], + "occluded": target_occluded[np.newaxis, ...], + } + + +def sample_queries_strided( + target_occluded: np.ndarray, + target_points: np.ndarray, + frames: np.ndarray, + query_stride: int = 5, +) -> Mapping[str, np.ndarray]: + """Package a set of frames and tracks for use in TAPNet evaluations. + + Given a set of frames and tracks with no query points, sample queries + strided every query_stride frames, ignoring points that are not visible + at the selected frames. + + Args: + target_occluded: Boolean occlusion flag, of shape [n_tracks, n_frames], + where True indicates occluded. + target_points: Position, of shape [n_tracks, n_frames, 2], where each point + is [x,y] scaled between 0 and 1. + frames: Video tensor, of shape [n_frames, height, width, 3]. Scaled between + -1 and 1. + query_stride: When sampling query points, search for un-occluded points + every query_stride frames and convert each one into a query. + + Returns: + A dict with the keys: + video: Video tensor of shape [1, n_frames, height, width, 3]. The video + has floats scaled to the range [-1, 1]. + query_points: Query points of shape [1, n_queries, 3] where + each point is [t, y, x] scaled to the range [-1, 1]. + target_points: Target points of shape [1, n_queries, n_frames, 2] where + each point is [x, y] scaled to the range [-1, 1]. + trackgroup: Index of the original track that each query point was + sampled from. This is useful for visualization. + """ + tracks = [] + occs = [] + queries = [] + trackgroups = [] + total = 0 + trackgroup = np.arange(target_occluded.shape[0]) + for i in range(0, target_occluded.shape[1], query_stride): + mask = target_occluded[:, i] == 0 + query = np.stack( + [ + i * np.ones(target_occluded.shape[0:1]), + target_points[:, i, 1], + target_points[:, i, 0], + ], + axis=-1, + ) + queries.append(query[mask]) + tracks.append(target_points[mask]) + occs.append(target_occluded[mask]) + trackgroups.append(trackgroup[mask]) + total += np.array(np.sum(target_occluded[:, i] == 0)) + + return { + "video": frames[np.newaxis, ...], + "query_points": np.concatenate(queries, axis=0)[np.newaxis, ...], + "target_points": np.concatenate(tracks, axis=0)[np.newaxis, ...], + "occluded": np.concatenate(occs, axis=0)[np.newaxis, ...], + "trackgroup": np.concatenate(trackgroups, axis=0)[np.newaxis, ...], + } + + +class TapVidDataset(torch.utils.data.Dataset): + def __init__( + self, + data_root, + dataset_type="davis", + resize_to_256=True, + queried_first=True, + ): + self.dataset_type = dataset_type + self.resize_to_256 = resize_to_256 + self.queried_first = queried_first + if self.dataset_type == "kinetics": + all_paths = glob.glob(os.path.join(data_root, "*_of_0010.pkl")) + points_dataset = [] + for pickle_path in all_paths: + with open(pickle_path, "rb") as f: + data = pickle.load(f) + points_dataset = points_dataset + data + self.points_dataset = points_dataset + else: + with open(data_root, "rb") as f: + self.points_dataset = pickle.load(f) + if self.dataset_type == "davis": + self.video_names = list(self.points_dataset.keys()) + print("found %d unique videos in %s" % (len(self.points_dataset), data_root)) + + def __getitem__(self, index): + if self.dataset_type == "davis": + video_name = self.video_names[index] + else: + video_name = index + video = self.points_dataset[video_name] + frames = video["video"] + + if isinstance(frames[0], bytes): + # TAP-Vid is stored and JPEG bytes rather than `np.ndarray`s. + def decode(frame): + byteio = io.BytesIO(frame) + img = Image.open(byteio) + return np.array(img) + + frames = np.array([decode(frame) for frame in frames]) + + target_points = self.points_dataset[video_name]["points"] + if self.resize_to_256: + frames = resize_video(frames, [256, 256]) + target_points *= np.array([255, 255]) # 1 should be mapped to 256-1 + else: + target_points *= np.array([frames.shape[2] - 1, frames.shape[1] - 1]) + + target_occ = self.points_dataset[video_name]["occluded"] + if self.queried_first: + converted = sample_queries_first(target_occ, target_points, frames) + else: + converted = sample_queries_strided(target_occ, target_points, frames) + assert converted["target_points"].shape[1] == converted["query_points"].shape[1] + + trajs = torch.from_numpy(converted["target_points"])[0].permute(1, 0, 2).float() # T, N, D + + rgbs = torch.from_numpy(frames).permute(0, 3, 1, 2).float() + visibles = torch.logical_not(torch.from_numpy(converted["occluded"]))[0].permute( + 1, 0 + ) # T, N + query_points = torch.from_numpy(converted["query_points"])[0] # T, N + return CoTrackerData( + rgbs, + trajs, + visibles, + seq_name=str(video_name), + query_points=query_points, + ) + + def __len__(self): + return len(self.points_dataset) diff --git a/cotracker/datasets/utils.py b/cotracker/datasets/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..30149f1e8d6248684ae519dfba964992f7ea77b3 --- /dev/null +++ b/cotracker/datasets/utils.py @@ -0,0 +1,106 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import dataclasses +import torch.nn.functional as F +from dataclasses import dataclass +from typing import Any, Optional + + +@dataclass(eq=False) +class CoTrackerData: + """ + Dataclass for storing video tracks data. + """ + + video: torch.Tensor # B, S, C, H, W + trajectory: torch.Tensor # B, S, N, 2 + visibility: torch.Tensor # B, S, N + # optional data + valid: Optional[torch.Tensor] = None # B, S, N + segmentation: Optional[torch.Tensor] = None # B, S, 1, H, W + seq_name: Optional[str] = None + query_points: Optional[torch.Tensor] = None # TapVID evaluation format + + +def collate_fn(batch): + """ + Collate function for video tracks data. + """ + video = torch.stack([b.video for b in batch], dim=0) + trajectory = torch.stack([b.trajectory for b in batch], dim=0) + visibility = torch.stack([b.visibility for b in batch], dim=0) + query_points = segmentation = None + if batch[0].query_points is not None: + query_points = torch.stack([b.query_points for b in batch], dim=0) + if batch[0].segmentation is not None: + segmentation = torch.stack([b.segmentation for b in batch], dim=0) + seq_name = [b.seq_name for b in batch] + + return CoTrackerData( + video=video, + trajectory=trajectory, + visibility=visibility, + segmentation=segmentation, + seq_name=seq_name, + query_points=query_points, + ) + + +def collate_fn_train(batch): + """ + Collate function for video tracks data during training. + """ + gotit = [gotit for _, gotit in batch] + video = torch.stack([b.video for b, _ in batch], dim=0) + trajectory = torch.stack([b.trajectory for b, _ in batch], dim=0) + visibility = torch.stack([b.visibility for b, _ in batch], dim=0) + valid = torch.stack([b.valid for b, _ in batch], dim=0) + seq_name = [b.seq_name for b, _ in batch] + return ( + CoTrackerData( + video=video, + trajectory=trajectory, + visibility=visibility, + valid=valid, + seq_name=seq_name, + ), + gotit, + ) + + +def try_to_cuda(t: Any) -> Any: + """ + Try to move the input variable `t` to a cuda device. + + Args: + t: Input. + + Returns: + t_cuda: `t` moved to a cuda device, if supported. + """ + try: + t = t.float().cuda() + except AttributeError: + pass + return t + + +def dataclass_to_cuda_(obj): + """ + Move all contents of a dataclass to cuda inplace if supported. + + Args: + batch: Input dataclass. + + Returns: + batch_cuda: `batch` moved to a cuda device, if supported. + """ + for f in dataclasses.fields(obj): + setattr(obj, f.name, try_to_cuda(getattr(obj, f.name))) + return obj diff --git a/cotracker/evaluation/__init__.py b/cotracker/evaluation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/evaluation/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/evaluation/configs/eval_dynamic_replica.yaml b/cotracker/evaluation/configs/eval_dynamic_replica.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7d6fca91f30333b0ef9ff0e7392d481a3edcc270 --- /dev/null +++ b/cotracker/evaluation/configs/eval_dynamic_replica.yaml @@ -0,0 +1,6 @@ +defaults: + - default_config_eval +exp_dir: ./outputs/cotracker +dataset_name: dynamic_replica + + \ No newline at end of file diff --git a/cotracker/evaluation/configs/eval_tapvid_davis_first.yaml b/cotracker/evaluation/configs/eval_tapvid_davis_first.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d37a6c9cb8879c7e09ecd760eaa9fb767ec1d78f --- /dev/null +++ b/cotracker/evaluation/configs/eval_tapvid_davis_first.yaml @@ -0,0 +1,6 @@ +defaults: + - default_config_eval +exp_dir: ./outputs/cotracker +dataset_name: tapvid_davis_first + + \ No newline at end of file diff --git a/cotracker/evaluation/configs/eval_tapvid_davis_strided.yaml b/cotracker/evaluation/configs/eval_tapvid_davis_strided.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e3cf3c1c1d7fe8ad0c5986af4d2ef973dbaa02f --- /dev/null +++ b/cotracker/evaluation/configs/eval_tapvid_davis_strided.yaml @@ -0,0 +1,6 @@ +defaults: + - default_config_eval +exp_dir: ./outputs/cotracker +dataset_name: tapvid_davis_strided + + \ No newline at end of file diff --git a/cotracker/evaluation/configs/eval_tapvid_kinetics_first.yaml b/cotracker/evaluation/configs/eval_tapvid_kinetics_first.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3be89144e1b635a72180532ef31a5512d6d4960f --- /dev/null +++ b/cotracker/evaluation/configs/eval_tapvid_kinetics_first.yaml @@ -0,0 +1,6 @@ +defaults: + - default_config_eval +exp_dir: ./outputs/cotracker +dataset_name: tapvid_kinetics_first + + \ No newline at end of file diff --git a/cotracker/evaluation/core/__init__.py b/cotracker/evaluation/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/evaluation/core/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/evaluation/core/eval_utils.py b/cotracker/evaluation/core/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7002fa557eb4af487cf8536df87b297fd94ae236 --- /dev/null +++ b/cotracker/evaluation/core/eval_utils.py @@ -0,0 +1,138 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np + +from typing import Iterable, Mapping, Tuple, Union + + +def compute_tapvid_metrics( + query_points: np.ndarray, + gt_occluded: np.ndarray, + gt_tracks: np.ndarray, + pred_occluded: np.ndarray, + pred_tracks: np.ndarray, + query_mode: str, +) -> Mapping[str, np.ndarray]: + """Computes TAP-Vid metrics (Jaccard, Pts. Within Thresh, Occ. Acc.) + See the TAP-Vid paper for details on the metric computation. All inputs are + given in raster coordinates. The first three arguments should be the direct + outputs of the reader: the 'query_points', 'occluded', and 'target_points'. + The paper metrics assume these are scaled relative to 256x256 images. + pred_occluded and pred_tracks are your algorithm's predictions. + This function takes a batch of inputs, and computes metrics separately for + each video. The metrics for the full benchmark are a simple mean of the + metrics across the full set of videos. These numbers are between 0 and 1, + but the paper multiplies them by 100 to ease reading. + Args: + query_points: The query points, an in the format [t, y, x]. Its size is + [b, n, 3], where b is the batch size and n is the number of queries + gt_occluded: A boolean array of shape [b, n, t], where t is the number + of frames. True indicates that the point is occluded. + gt_tracks: The target points, of shape [b, n, t, 2]. Each point is + in the format [x, y] + pred_occluded: A boolean array of predicted occlusions, in the same + format as gt_occluded. + pred_tracks: An array of track predictions from your algorithm, in the + same format as gt_tracks. + query_mode: Either 'first' or 'strided', depending on how queries are + sampled. If 'first', we assume the prior knowledge that all points + before the query point are occluded, and these are removed from the + evaluation. + Returns: + A dict with the following keys: + occlusion_accuracy: Accuracy at predicting occlusion. + pts_within_{x} for x in [1, 2, 4, 8, 16]: Fraction of points + predicted to be within the given pixel threshold, ignoring occlusion + prediction. + jaccard_{x} for x in [1, 2, 4, 8, 16]: Jaccard metric for the given + threshold + average_pts_within_thresh: average across pts_within_{x} + average_jaccard: average across jaccard_{x} + """ + + metrics = {} + # Fixed bug is described in: + # https://github.com/facebookresearch/co-tracker/issues/20 + eye = np.eye(gt_tracks.shape[2], dtype=np.int32) + + if query_mode == "first": + # evaluate frames after the query frame + query_frame_to_eval_frames = np.cumsum(eye, axis=1) - eye + elif query_mode == "strided": + # evaluate all frames except the query frame + query_frame_to_eval_frames = 1 - eye + else: + raise ValueError("Unknown query mode " + query_mode) + + query_frame = query_points[..., 0] + query_frame = np.round(query_frame).astype(np.int32) + evaluation_points = query_frame_to_eval_frames[query_frame] > 0 + + # Occlusion accuracy is simply how often the predicted occlusion equals the + # ground truth. + occ_acc = np.sum( + np.equal(pred_occluded, gt_occluded) & evaluation_points, + axis=(1, 2), + ) / np.sum(evaluation_points) + metrics["occlusion_accuracy"] = occ_acc + + # Next, convert the predictions and ground truth positions into pixel + # coordinates. + visible = np.logical_not(gt_occluded) + pred_visible = np.logical_not(pred_occluded) + all_frac_within = [] + all_jaccard = [] + for thresh in [1, 2, 4, 8, 16]: + # True positives are points that are within the threshold and where both + # the prediction and the ground truth are listed as visible. + within_dist = np.sum( + np.square(pred_tracks - gt_tracks), + axis=-1, + ) < np.square(thresh) + is_correct = np.logical_and(within_dist, visible) + + # Compute the frac_within_threshold, which is the fraction of points + # within the threshold among points that are visible in the ground truth, + # ignoring whether they're predicted to be visible. + count_correct = np.sum( + is_correct & evaluation_points, + axis=(1, 2), + ) + count_visible_points = np.sum(visible & evaluation_points, axis=(1, 2)) + frac_correct = count_correct / count_visible_points + metrics["pts_within_" + str(thresh)] = frac_correct + all_frac_within.append(frac_correct) + + true_positives = np.sum( + is_correct & pred_visible & evaluation_points, axis=(1, 2) + ) + + # The denominator of the jaccard metric is the true positives plus + # false positives plus false negatives. However, note that true positives + # plus false negatives is simply the number of points in the ground truth + # which is easier to compute than trying to compute all three quantities. + # Thus we just add the number of points in the ground truth to the number + # of false positives. + # + # False positives are simply points that are predicted to be visible, + # but the ground truth is not visible or too far from the prediction. + gt_positives = np.sum(visible & evaluation_points, axis=(1, 2)) + false_positives = (~visible) & pred_visible + false_positives = false_positives | ((~within_dist) & pred_visible) + false_positives = np.sum(false_positives & evaluation_points, axis=(1, 2)) + jaccard = true_positives / (gt_positives + false_positives) + metrics["jaccard_" + str(thresh)] = jaccard + all_jaccard.append(jaccard) + metrics["average_jaccard"] = np.mean( + np.stack(all_jaccard, axis=1), + axis=1, + ) + metrics["average_pts_within_thresh"] = np.mean( + np.stack(all_frac_within, axis=1), + axis=1, + ) + return metrics diff --git a/cotracker/evaluation/core/evaluator.py b/cotracker/evaluation/core/evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..ffc697ec5458b6bc071cb40abbe4234bd581395f --- /dev/null +++ b/cotracker/evaluation/core/evaluator.py @@ -0,0 +1,253 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from collections import defaultdict +import os +from typing import Optional +import torch +from tqdm import tqdm +import numpy as np + +from torch.utils.tensorboard import SummaryWriter +from cotracker.datasets.utils import dataclass_to_cuda_ +from cotracker.utils.visualizer import Visualizer +from cotracker.models.core.model_utils import reduce_masked_mean +from cotracker.evaluation.core.eval_utils import compute_tapvid_metrics + +import logging + + +class Evaluator: + """ + A class defining the CoTracker evaluator. + """ + + def __init__(self, exp_dir) -> None: + # Visualization + self.exp_dir = exp_dir + os.makedirs(exp_dir, exist_ok=True) + self.visualization_filepaths = defaultdict(lambda: defaultdict(list)) + self.visualize_dir = os.path.join(exp_dir, "visualisations") + + def compute_metrics(self, metrics, sample, pred_trajectory, dataset_name): + if isinstance(pred_trajectory, tuple): + pred_trajectory, pred_visibility = pred_trajectory + else: + pred_visibility = None + if "tapvid" in dataset_name: + B, T, N, D = sample.trajectory.shape + traj = sample.trajectory.clone() + thr = 0.9 + + if pred_visibility is None: + logging.warning("visibility is NONE") + pred_visibility = torch.zeros_like(sample.visibility) + + if not pred_visibility.dtype == torch.bool: + pred_visibility = pred_visibility > thr + + query_points = sample.query_points.clone().cpu().numpy() + + pred_visibility = pred_visibility[:, :, :N] + pred_trajectory = pred_trajectory[:, :, :N] + + gt_tracks = traj.permute(0, 2, 1, 3).cpu().numpy() + gt_occluded = ( + torch.logical_not(sample.visibility.clone().permute(0, 2, 1)).cpu().numpy() + ) + + pred_occluded = ( + torch.logical_not(pred_visibility.clone().permute(0, 2, 1)).cpu().numpy() + ) + pred_tracks = pred_trajectory.permute(0, 2, 1, 3).cpu().numpy() + + out_metrics = compute_tapvid_metrics( + query_points, + gt_occluded, + gt_tracks, + pred_occluded, + pred_tracks, + query_mode="strided" if "strided" in dataset_name else "first", + ) + + metrics[sample.seq_name[0]] = out_metrics + for metric_name in out_metrics.keys(): + if "avg" not in metrics: + metrics["avg"] = {} + metrics["avg"][metric_name] = np.mean( + [v[metric_name] for k, v in metrics.items() if k != "avg"] + ) + + logging.info(f"Metrics: {out_metrics}") + logging.info(f"avg: {metrics['avg']}") + print("metrics", out_metrics) + print("avg", metrics["avg"]) + elif dataset_name == "dynamic_replica" or dataset_name == "pointodyssey": + *_, N, _ = sample.trajectory.shape + B, T, N = sample.visibility.shape + H, W = sample.video.shape[-2:] + device = sample.video.device + + out_metrics = {} + + d_vis_sum = d_occ_sum = d_sum_all = 0.0 + thrs = [1, 2, 4, 8, 16] + sx_ = (W - 1) / 255.0 + sy_ = (H - 1) / 255.0 + sc_py = np.array([sx_, sy_]).reshape([1, 1, 2]) + sc_pt = torch.from_numpy(sc_py).float().to(device) + __, first_visible_inds = torch.max(sample.visibility, dim=1) + + frame_ids_tensor = torch.arange(T, device=device)[None, :, None].repeat(B, 1, N) + start_tracking_mask = frame_ids_tensor > (first_visible_inds.unsqueeze(1)) + + for thr in thrs: + d_ = ( + torch.norm( + pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt, + dim=-1, + ) + < thr + ).float() # B,S-1,N + d_occ = ( + reduce_masked_mean(d_, (1 - sample.visibility) * start_tracking_mask).item() + * 100.0 + ) + d_occ_sum += d_occ + out_metrics[f"accuracy_occ_{thr}"] = d_occ + + d_vis = ( + reduce_masked_mean(d_, sample.visibility * start_tracking_mask).item() * 100.0 + ) + d_vis_sum += d_vis + out_metrics[f"accuracy_vis_{thr}"] = d_vis + + d_all = reduce_masked_mean(d_, start_tracking_mask).item() * 100.0 + d_sum_all += d_all + out_metrics[f"accuracy_{thr}"] = d_all + + d_occ_avg = d_occ_sum / len(thrs) + d_vis_avg = d_vis_sum / len(thrs) + d_all_avg = d_sum_all / len(thrs) + + sur_thr = 50 + dists = torch.norm( + pred_trajectory[..., :2] / sc_pt - sample.trajectory[..., :2] / sc_pt, + dim=-1, + ) # B,S,N + dist_ok = 1 - (dists > sur_thr).float() * sample.visibility # B,S,N + survival = torch.cumprod(dist_ok, dim=1) # B,S,N + out_metrics["survival"] = torch.mean(survival).item() * 100.0 + + out_metrics["accuracy_occ"] = d_occ_avg + out_metrics["accuracy_vis"] = d_vis_avg + out_metrics["accuracy"] = d_all_avg + + metrics[sample.seq_name[0]] = out_metrics + for metric_name in out_metrics.keys(): + if "avg" not in metrics: + metrics["avg"] = {} + metrics["avg"][metric_name] = float( + np.mean([v[metric_name] for k, v in metrics.items() if k != "avg"]) + ) + + logging.info(f"Metrics: {out_metrics}") + logging.info(f"avg: {metrics['avg']}") + print("metrics", out_metrics) + print("avg", metrics["avg"]) + + @torch.no_grad() + def evaluate_sequence( + self, + model, + test_dataloader: torch.utils.data.DataLoader, + dataset_name: str, + train_mode=False, + visualize_every: int = 1, + writer: Optional[SummaryWriter] = None, + step: Optional[int] = 0, + ): + metrics = {} + + vis = Visualizer( + save_dir=self.exp_dir, + fps=7, + ) + + for ind, sample in enumerate(tqdm(test_dataloader)): + if isinstance(sample, tuple): + sample, gotit = sample + if not all(gotit): + print("batch is None") + continue + if torch.cuda.is_available(): + dataclass_to_cuda_(sample) + device = torch.device("cuda") + else: + device = torch.device("cpu") + + if ( + not train_mode + and hasattr(model, "sequence_len") + and (sample.visibility[:, : model.sequence_len].sum() == 0) + ): + print(f"skipping batch {ind}") + continue + + if "tapvid" in dataset_name: + queries = sample.query_points.clone().float() + + queries = torch.stack( + [ + queries[:, :, 0], + queries[:, :, 2], + queries[:, :, 1], + ], + dim=2, + ).to(device) + else: + queries = torch.cat( + [ + torch.zeros_like(sample.trajectory[:, 0, :, :1]), + sample.trajectory[:, 0], + ], + dim=2, + ).to(device) + + pred_tracks = model(sample.video, queries) + if "strided" in dataset_name: + inv_video = sample.video.flip(1).clone() + inv_queries = queries.clone() + inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1 + + pred_trj, pred_vsb = pred_tracks + inv_pred_trj, inv_pred_vsb = model(inv_video, inv_queries) + + inv_pred_trj = inv_pred_trj.flip(1) + inv_pred_vsb = inv_pred_vsb.flip(1) + + mask = pred_trj == 0 + + pred_trj[mask] = inv_pred_trj[mask] + pred_vsb[mask[:, :, :, 0]] = inv_pred_vsb[mask[:, :, :, 0]] + + pred_tracks = pred_trj, pred_vsb + + if dataset_name == "badja" or dataset_name == "fastcapture": + seq_name = sample.seq_name[0] + else: + seq_name = str(ind) + if ind % visualize_every == 0: + vis.visualize( + sample.video, + pred_tracks[0] if isinstance(pred_tracks, tuple) else pred_tracks, + filename=dataset_name + "_" + seq_name, + writer=writer, + step=step, + ) + + self.compute_metrics(metrics, sample, pred_tracks, dataset_name) + return metrics diff --git a/cotracker/evaluation/evaluate.py b/cotracker/evaluation/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d679d2a14250e9daa10a643d357f573ad720cf8 --- /dev/null +++ b/cotracker/evaluation/evaluate.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +from dataclasses import dataclass, field + +import hydra +import numpy as np + +import torch +from omegaconf import OmegaConf + +from cotracker.datasets.tap_vid_datasets import TapVidDataset +from cotracker.datasets.dr_dataset import DynamicReplicaDataset +from cotracker.datasets.utils import collate_fn + +from cotracker.models.evaluation_predictor import EvaluationPredictor + +from cotracker.evaluation.core.evaluator import Evaluator +from cotracker.models.build_cotracker import ( + build_cotracker, +) + + +@dataclass(eq=False) +class DefaultConfig: + # Directory where all outputs of the experiment will be saved. + exp_dir: str = "./outputs" + + # Name of the dataset to be used for the evaluation. + dataset_name: str = "tapvid_davis_first" + # The root directory of the dataset. + dataset_root: str = "./" + + # Path to the pre-trained model checkpoint to be used for the evaluation. + # The default value is the path to a specific CoTracker model checkpoint. + checkpoint: str = "./checkpoints/cotracker2.pth" + + # EvaluationPredictor parameters + # The size (N) of the support grid used in the predictor. + # The total number of points is (N*N). + grid_size: int = 5 + # The size (N) of the local support grid. + local_grid_size: int = 8 + # A flag indicating whether to evaluate one ground truth point at a time. + single_point: bool = True + # The number of iterative updates for each sliding window. + n_iters: int = 6 + + seed: int = 0 + gpu_idx: int = 0 + + # Override hydra's working directory to current working dir, + # also disable storing the .hydra logs: + hydra: dict = field( + default_factory=lambda: { + "run": {"dir": "."}, + "output_subdir": None, + } + ) + + +def run_eval(cfg: DefaultConfig): + """ + The function evaluates CoTracker on a specified benchmark dataset based on a provided configuration. + + Args: + cfg (DefaultConfig): An instance of DefaultConfig class which includes: + - exp_dir (str): The directory path for the experiment. + - dataset_name (str): The name of the dataset to be used. + - dataset_root (str): The root directory of the dataset. + - checkpoint (str): The path to the CoTracker model's checkpoint. + - single_point (bool): A flag indicating whether to evaluate one ground truth point at a time. + - n_iters (int): The number of iterative updates for each sliding window. + - seed (int): The seed for setting the random state for reproducibility. + - gpu_idx (int): The index of the GPU to be used. + """ + # Creating the experiment directory if it doesn't exist + os.makedirs(cfg.exp_dir, exist_ok=True) + + # Saving the experiment configuration to a .yaml file in the experiment directory + cfg_file = os.path.join(cfg.exp_dir, "expconfig.yaml") + with open(cfg_file, "w") as f: + OmegaConf.save(config=cfg, f=f) + + evaluator = Evaluator(cfg.exp_dir) + cotracker_model = build_cotracker(cfg.checkpoint) + + # Creating the EvaluationPredictor object + predictor = EvaluationPredictor( + cotracker_model, + grid_size=cfg.grid_size, + local_grid_size=cfg.local_grid_size, + single_point=cfg.single_point, + n_iters=cfg.n_iters, + ) + if torch.cuda.is_available(): + predictor.model = predictor.model.cuda() + + # Setting the random seeds + torch.manual_seed(cfg.seed) + np.random.seed(cfg.seed) + + # Constructing the specified dataset + curr_collate_fn = collate_fn + if "tapvid" in cfg.dataset_name: + dataset_type = cfg.dataset_name.split("_")[1] + if dataset_type == "davis": + data_root = os.path.join(cfg.dataset_root, "tapvid_davis", "tapvid_davis.pkl") + elif dataset_type == "kinetics": + data_root = os.path.join( + cfg.dataset_root, "/kinetics/kinetics-dataset/k700-2020/tapvid_kinetics" + ) + test_dataset = TapVidDataset( + dataset_type=dataset_type, + data_root=data_root, + queried_first=not "strided" in cfg.dataset_name, + ) + elif cfg.dataset_name == "dynamic_replica": + test_dataset = DynamicReplicaDataset(sample_len=300, only_first_n_samples=1) + + # Creating the DataLoader object + test_dataloader = torch.utils.data.DataLoader( + test_dataset, + batch_size=1, + shuffle=False, + num_workers=14, + collate_fn=curr_collate_fn, + ) + + # Timing and conducting the evaluation + import time + + start = time.time() + evaluate_result = evaluator.evaluate_sequence( + predictor, + test_dataloader, + dataset_name=cfg.dataset_name, + ) + end = time.time() + print(end - start) + + # Saving the evaluation results to a .json file + evaluate_result = evaluate_result["avg"] + print("evaluate_result", evaluate_result) + result_file = os.path.join(cfg.exp_dir, f"result_eval_.json") + evaluate_result["time"] = end - start + print(f"Dumping eval results to {result_file}.") + with open(result_file, "w") as f: + json.dump(evaluate_result, f) + + +cs = hydra.core.config_store.ConfigStore.instance() +cs.store(name="default_config_eval", node=DefaultConfig) + + +@hydra.main(config_path="./configs/", config_name="default_config_eval") +def evaluate(cfg: DefaultConfig) -> None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.gpu_idx) + run_eval(cfg) + + +if __name__ == "__main__": + evaluate() diff --git a/cotracker/models/__init__.py b/cotracker/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/models/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/models/build_cotracker.py b/cotracker/models/build_cotracker.py new file mode 100644 index 0000000000000000000000000000000000000000..1ae5f90413c9df16b7b6640d68a4502a719290c0 --- /dev/null +++ b/cotracker/models/build_cotracker.py @@ -0,0 +1,33 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from cotracker.models.core.cotracker.cotracker import CoTracker2 + + +def build_cotracker( + checkpoint: str, +): + if checkpoint is None: + return build_cotracker() + model_name = checkpoint.split("/")[-1].split(".")[0] + if model_name == "cotracker": + return build_cotracker(checkpoint=checkpoint) + else: + raise ValueError(f"Unknown model name {model_name}") + + +def build_cotracker(checkpoint=None): + cotracker = CoTracker2(stride=4, window_len=8, add_space_attn=True) + + if checkpoint is not None: + with open(checkpoint, "rb") as f: + state_dict = torch.load(f, map_location="cpu") + if "model" in state_dict: + state_dict = state_dict["model"] + cotracker.load_state_dict(state_dict) + return cotracker diff --git a/cotracker/models/core/__init__.py b/cotracker/models/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/models/core/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/models/core/cotracker/__init__.py b/cotracker/models/core/cotracker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/models/core/cotracker/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/models/core/cotracker/blocks.py b/cotracker/models/core/cotracker/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..8d61b2581be967a31f1891fe93c326d5ce7451df --- /dev/null +++ b/cotracker/models/core/cotracker/blocks.py @@ -0,0 +1,367 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial +from typing import Callable +import collections +from torch import Tensor +from itertools import repeat + +from cotracker.models.core.model_utils import bilinear_sampler + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return tuple(x) + return tuple(repeat(x, n)) + + return parse + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +to_2tuple = _ntuple(2) + + +class Mlp(nn.Module): + """MLP as used in Vision Transformer, MLP-Mixer and related networks""" + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0.0, + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class ResidualBlock(nn.Module): + def __init__(self, in_planes, planes, norm_fn="group", stride=1): + super(ResidualBlock, self).__init__() + + self.conv1 = nn.Conv2d( + in_planes, + planes, + kernel_size=3, + padding=1, + stride=stride, + padding_mode="zeros", + ) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, padding_mode="zeros") + self.relu = nn.ReLU(inplace=True) + + num_groups = planes // 8 + + if norm_fn == "group": + self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + if not stride == 1: + self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) + + elif norm_fn == "batch": + self.norm1 = nn.BatchNorm2d(planes) + self.norm2 = nn.BatchNorm2d(planes) + if not stride == 1: + self.norm3 = nn.BatchNorm2d(planes) + + elif norm_fn == "instance": + self.norm1 = nn.InstanceNorm2d(planes) + self.norm2 = nn.InstanceNorm2d(planes) + if not stride == 1: + self.norm3 = nn.InstanceNorm2d(planes) + + elif norm_fn == "none": + self.norm1 = nn.Sequential() + self.norm2 = nn.Sequential() + if not stride == 1: + self.norm3 = nn.Sequential() + + if stride == 1: + self.downsample = None + + else: + self.downsample = nn.Sequential( + nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3 + ) + + def forward(self, x): + y = x + y = self.relu(self.norm1(self.conv1(y))) + y = self.relu(self.norm2(self.conv2(y))) + + if self.downsample is not None: + x = self.downsample(x) + + return self.relu(x + y) + + +class BasicEncoder(nn.Module): + def __init__(self, input_dim=3, output_dim=128, stride=4): + super(BasicEncoder, self).__init__() + self.stride = stride + self.norm_fn = "instance" + self.in_planes = output_dim // 2 + + self.norm1 = nn.InstanceNorm2d(self.in_planes) + self.norm2 = nn.InstanceNorm2d(output_dim * 2) + + self.conv1 = nn.Conv2d( + input_dim, + self.in_planes, + kernel_size=7, + stride=2, + padding=3, + padding_mode="zeros", + ) + self.relu1 = nn.ReLU(inplace=True) + self.layer1 = self._make_layer(output_dim // 2, stride=1) + self.layer2 = self._make_layer(output_dim // 4 * 3, stride=2) + self.layer3 = self._make_layer(output_dim, stride=2) + self.layer4 = self._make_layer(output_dim, stride=2) + + self.conv2 = nn.Conv2d( + output_dim * 3 + output_dim // 4, + output_dim * 2, + kernel_size=3, + padding=1, + padding_mode="zeros", + ) + self.relu2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(output_dim * 2, output_dim, kernel_size=1) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.InstanceNorm2d)): + if m.weight is not None: + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def _make_layer(self, dim, stride=1): + layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) + layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) + layers = (layer1, layer2) + + self.in_planes = dim + return nn.Sequential(*layers) + + def forward(self, x): + _, _, H, W = x.shape + + x = self.conv1(x) + x = self.norm1(x) + x = self.relu1(x) + + a = self.layer1(x) + b = self.layer2(a) + c = self.layer3(b) + d = self.layer4(c) + + def _bilinear_intepolate(x): + return F.interpolate( + x, + (H // self.stride, W // self.stride), + mode="bilinear", + align_corners=True, + ) + + a = _bilinear_intepolate(a) + b = _bilinear_intepolate(b) + c = _bilinear_intepolate(c) + d = _bilinear_intepolate(d) + + x = self.conv2(torch.cat([a, b, c, d], dim=1)) + x = self.norm2(x) + x = self.relu2(x) + x = self.conv3(x) + return x + + +class CorrBlock: + def __init__( + self, + fmaps, + num_levels=4, + radius=4, + multiple_track_feats=False, + padding_mode="zeros", + ): + B, S, C, H, W = fmaps.shape + self.S, self.C, self.H, self.W = S, C, H, W + self.padding_mode = padding_mode + self.num_levels = num_levels + self.radius = radius + self.fmaps_pyramid = [] + self.multiple_track_feats = multiple_track_feats + + self.fmaps_pyramid.append(fmaps) + for i in range(self.num_levels - 1): + fmaps_ = fmaps.reshape(B * S, C, H, W) + fmaps_ = F.avg_pool2d(fmaps_, 2, stride=2) + _, _, H, W = fmaps_.shape + fmaps = fmaps_.reshape(B, S, C, H, W) + self.fmaps_pyramid.append(fmaps) + + def sample(self, coords): + r = self.radius + B, S, N, D = coords.shape + assert D == 2 + + H, W = self.H, self.W + out_pyramid = [] + for i in range(self.num_levels): + corrs = self.corrs_pyramid[i] # B, S, N, H, W + *_, H, W = corrs.shape + + dx = torch.linspace(-r, r, 2 * r + 1) + dy = torch.linspace(-r, r, 2 * r + 1) + delta = torch.stack(torch.meshgrid(dy, dx, indexing="ij"), axis=-1).to(coords.device) + + centroid_lvl = coords.reshape(B * S * N, 1, 1, 2) / 2**i + delta_lvl = delta.view(1, 2 * r + 1, 2 * r + 1, 2) + coords_lvl = centroid_lvl + delta_lvl + + corrs = bilinear_sampler( + corrs.reshape(B * S * N, 1, H, W), + coords_lvl, + padding_mode=self.padding_mode, + ) + corrs = corrs.view(B, S, N, -1) + out_pyramid.append(corrs) + + out = torch.cat(out_pyramid, dim=-1) # B, S, N, LRR*2 + out = out.permute(0, 2, 1, 3).contiguous().view(B * N, S, -1).float() + return out + + def corr(self, targets): + B, S, N, C = targets.shape + if self.multiple_track_feats: + targets_split = targets.split(C // self.num_levels, dim=-1) + B, S, N, C = targets_split[0].shape + + assert C == self.C + assert S == self.S + + fmap1 = targets + + self.corrs_pyramid = [] + for i, fmaps in enumerate(self.fmaps_pyramid): + *_, H, W = fmaps.shape + fmap2s = fmaps.view(B, S, C, H * W) # B S C H W -> B S C (H W) + if self.multiple_track_feats: + fmap1 = targets_split[i] + corrs = torch.matmul(fmap1, fmap2s) + corrs = corrs.view(B, S, N, H, W) # B S N (H W) -> B S N H W + corrs = corrs / torch.sqrt(torch.tensor(C).float()) + self.corrs_pyramid.append(corrs) + + +class Attention(nn.Module): + def __init__(self, query_dim, context_dim=None, num_heads=8, dim_head=48, qkv_bias=False): + super().__init__() + inner_dim = dim_head * num_heads + context_dim = default(context_dim, query_dim) + self.scale = dim_head**-0.5 + self.heads = num_heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=qkv_bias) + self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias=qkv_bias) + self.to_out = nn.Linear(inner_dim, query_dim) + + def forward(self, x, context=None, attn_bias=None): + B, N1, C = x.shape + h = self.heads + + q = self.to_q(x).reshape(B, N1, h, C // h).permute(0, 2, 1, 3) + context = default(context, x) + k, v = self.to_kv(context).chunk(2, dim=-1) + + N2 = context.shape[1] + k = k.reshape(B, N2, h, C // h).permute(0, 2, 1, 3) + v = v.reshape(B, N2, h, C // h).permute(0, 2, 1, 3) + + sim = (q @ k.transpose(-2, -1)) * self.scale + + if attn_bias is not None: + sim = sim + attn_bias + attn = sim.softmax(dim=-1) + + x = (attn @ v).transpose(1, 2).reshape(B, N1, C) + return self.to_out(x) + + +class AttnBlock(nn.Module): + def __init__( + self, + hidden_size, + num_heads, + attn_class: Callable[..., nn.Module] = Attention, + mlp_ratio=4.0, + **block_kwargs + ): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = attn_class(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=approx_gelu, + drop=0, + ) + + def forward(self, x, mask=None): + attn_bias = mask + if mask is not None: + mask = ( + (mask[:, None] * mask[:, :, None]) + .unsqueeze(1) + .expand(-1, self.attn.num_heads, -1, -1) + ) + max_neg_value = -torch.finfo(x.dtype).max + attn_bias = (~mask) * max_neg_value + x = x + self.attn(self.norm1(x), attn_bias=attn_bias) + x = x + self.mlp(self.norm2(x)) + return x diff --git a/cotracker/models/core/cotracker/cotracker.py b/cotracker/models/core/cotracker/cotracker.py new file mode 100644 index 0000000000000000000000000000000000000000..53178fbe067552da46224c5e09760d2c747d8e16 --- /dev/null +++ b/cotracker/models/core/cotracker/cotracker.py @@ -0,0 +1,503 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from cotracker.models.core.model_utils import sample_features4d, sample_features5d +from cotracker.models.core.embeddings import ( + get_2d_embedding, + get_1d_sincos_pos_embed_from_grid, + get_2d_sincos_pos_embed, +) + +from cotracker.models.core.cotracker.blocks import ( + Mlp, + BasicEncoder, + AttnBlock, + CorrBlock, + Attention, +) + +torch.manual_seed(0) + + +class CoTracker2(nn.Module): + def __init__( + self, + window_len=8, + stride=4, + add_space_attn=True, + num_virtual_tracks=64, + model_resolution=(384, 512), + ): + super(CoTracker2, self).__init__() + self.window_len = window_len + self.stride = stride + self.hidden_dim = 256 + self.latent_dim = 128 + self.add_space_attn = add_space_attn + self.fnet = BasicEncoder(output_dim=self.latent_dim) + self.num_virtual_tracks = num_virtual_tracks + self.model_resolution = model_resolution + self.input_dim = 456 + self.updateformer = EfficientUpdateFormer( + space_depth=6, + time_depth=6, + input_dim=self.input_dim, + hidden_size=384, + output_dim=self.latent_dim + 2, + mlp_ratio=4.0, + add_space_attn=add_space_attn, + num_virtual_tracks=num_virtual_tracks, + ) + + time_grid = torch.linspace(0, window_len - 1, window_len).reshape(1, window_len, 1) + + self.register_buffer( + "time_emb", get_1d_sincos_pos_embed_from_grid(self.input_dim, time_grid[0]) + ) + + self.register_buffer( + "pos_emb", + get_2d_sincos_pos_embed( + embed_dim=self.input_dim, + grid_size=( + model_resolution[0] // stride, + model_resolution[1] // stride, + ), + ), + ) + self.norm = nn.GroupNorm(1, self.latent_dim) + self.track_feat_updater = nn.Sequential( + nn.Linear(self.latent_dim, self.latent_dim), + nn.GELU(), + ) + self.vis_predictor = nn.Sequential( + nn.Linear(self.latent_dim, 1), + ) + + def forward_window( + self, + fmaps, + coords, + track_feat=None, + vis=None, + track_mask=None, + attention_mask=None, + iters=4, + ): + # B = batch size + # S = number of frames in the window) + # N = number of tracks + # C = channels of a point feature vector + # E = positional embedding size + # LRR = local receptive field radius + # D = dimension of the transformer input tokens + + # track_feat = B S N C + # vis = B S N 1 + # track_mask = B S N 1 + # attention_mask = B S N + + B, S_init, N, __ = track_mask.shape + B, S, *_ = fmaps.shape + + track_mask = F.pad(track_mask, (0, 0, 0, 0, 0, S - S_init), "constant") + track_mask_vis = ( + torch.cat([track_mask, vis], dim=-1).permute(0, 2, 1, 3).reshape(B * N, S, 2) + ) + + corr_block = CorrBlock( + fmaps, + num_levels=4, + radius=3, + padding_mode="border", + ) + + sampled_pos_emb = ( + sample_features4d(self.pos_emb.repeat(B, 1, 1, 1), coords[:, 0]) + .reshape(B * N, self.input_dim) + .unsqueeze(1) + ) # B E N -> (B N) 1 E + + coord_preds = [] + for __ in range(iters): + coords = coords.detach() # B S N 2 + corr_block.corr(track_feat) + + # Sample correlation features around each point + fcorrs = corr_block.sample(coords) # (B N) S LRR + + # Get the flow embeddings + flows = (coords - coords[:, 0:1]).permute(0, 2, 1, 3).reshape(B * N, S, 2) + flow_emb = get_2d_embedding(flows, 64, cat_coords=True) # N S E + + track_feat_ = track_feat.permute(0, 2, 1, 3).reshape(B * N, S, self.latent_dim) + + transformer_input = torch.cat([flow_emb, fcorrs, track_feat_, track_mask_vis], dim=2) + x = transformer_input + sampled_pos_emb + self.time_emb + x = x.view(B, N, S, -1) # (B N) S D -> B N S D + + delta = self.updateformer( + x, + attention_mask.reshape(B * S, N), # B S N -> (B S) N + ) + + delta_coords = delta[..., :2].permute(0, 2, 1, 3) + coords = coords + delta_coords + coord_preds.append(coords * self.stride) + + delta_feats_ = delta[..., 2:].reshape(B * N * S, self.latent_dim) + track_feat_ = track_feat.permute(0, 2, 1, 3).reshape(B * N * S, self.latent_dim) + track_feat_ = self.track_feat_updater(self.norm(delta_feats_)) + track_feat_ + track_feat = track_feat_.reshape(B, N, S, self.latent_dim).permute( + 0, 2, 1, 3 + ) # (B N S) C -> B S N C + + vis_pred = self.vis_predictor(track_feat).reshape(B, S, N) + return coord_preds, vis_pred + + def get_track_feat(self, fmaps, queried_frames, queried_coords): + sample_frames = queried_frames[:, None, :, None] + sample_coords = torch.cat( + [ + sample_frames, + queried_coords[:, None], + ], + dim=-1, + ) + sample_track_feats = sample_features5d(fmaps, sample_coords) + return sample_track_feats + + def init_video_online_processing(self): + self.online_ind = 0 + self.online_track_feat = None + self.online_coords_predicted = None + self.online_vis_predicted = None + + def forward(self, video, queries, iters=4, is_train=False, is_online=False): + """Predict tracks + + Args: + video (FloatTensor[B, T, 3]): input videos. + queries (FloatTensor[B, N, 3]): point queries. + iters (int, optional): number of updates. Defaults to 4. + is_train (bool, optional): enables training mode. Defaults to False. + is_online (bool, optional): enables online mode. Defaults to False. Before enabling, call model.init_video_online_processing(). + + Returns: + - coords_predicted (FloatTensor[B, T, N, 2]): + - vis_predicted (FloatTensor[B, T, N]): + - train_data: `None` if `is_train` is false, otherwise: + - all_vis_predictions (List[FloatTensor[B, S, N, 1]]): + - all_coords_predictions (List[FloatTensor[B, S, N, 2]]): + - mask (BoolTensor[B, T, N]): + """ + B, T, C, H, W = video.shape + B, N, __ = queries.shape + S = self.window_len + device = queries.device + + # B = batch size + # S = number of frames in the window of the padded video + # S_trimmed = actual number of frames in the window + # N = number of tracks + # C = color channels (3 for RGB) + # E = positional embedding size + # LRR = local receptive field radius + # D = dimension of the transformer input tokens + + # video = B T C H W + # queries = B N 3 + # coords_init = B S N 2 + # vis_init = B S N 1 + + assert S >= 2 # A tracker needs at least two frames to track something + if is_online: + assert T <= S, "Online mode: video chunk must be <= window size." + assert self.online_ind is not None, "Call model.init_video_online_processing() first." + assert not is_train, "Training not supported in online mode." + step = S // 2 # How much the sliding window moves at every step + video = 2 * (video / 255.0) - 1.0 + + # The first channel is the frame number + # The rest are the coordinates of points we want to track + queried_frames = queries[:, :, 0].long() + + queried_coords = queries[..., 1:] + queried_coords = queried_coords / self.stride + + # We store our predictions here + coords_predicted = torch.zeros((B, T, N, 2), device=device) + vis_predicted = torch.zeros((B, T, N), device=device) + if is_online: + if self.online_coords_predicted is None: + # Init online predictions with zeros + self.online_coords_predicted = coords_predicted + self.online_vis_predicted = vis_predicted + else: + # Pad online predictions with zeros for the current window + pad = min(step, T - step) + coords_predicted = F.pad( + self.online_coords_predicted, (0, 0, 0, 0, 0, pad), "constant" + ) + vis_predicted = F.pad(self.online_vis_predicted, (0, 0, 0, pad), "constant") + all_coords_predictions, all_vis_predictions = [], [] + + # Pad the video so that an integer number of sliding windows fit into it + # TODO: we may drop this requirement because the transformer should not care + # TODO: pad the features instead of the video + pad = S - T if is_online else (S - T % S) % S # We don't want to pad if T % S == 0 + video = F.pad(video.reshape(B, 1, T, C * H * W), (0, 0, 0, pad), "replicate").reshape( + B, -1, C, H, W + ) + + # Compute convolutional features for the video or for the current chunk in case of online mode + fmaps = self.fnet(video.reshape(-1, C, H, W)).reshape( + B, -1, self.latent_dim, H // self.stride, W // self.stride + ) + + # We compute track features + track_feat = self.get_track_feat( + fmaps, + queried_frames - self.online_ind if is_online else queried_frames, + queried_coords, + ).repeat(1, S, 1, 1) + if is_online: + # We update track features for the current window + sample_frames = queried_frames[:, None, :, None] # B 1 N 1 + left = 0 if self.online_ind == 0 else self.online_ind + step + right = self.online_ind + S + sample_mask = (sample_frames >= left) & (sample_frames < right) + if self.online_track_feat is None: + self.online_track_feat = torch.zeros_like(track_feat, device=device) + self.online_track_feat += track_feat * sample_mask + track_feat = self.online_track_feat.clone() + # We process ((num_windows - 1) * step + S) frames in total, so there are + # (ceil((T - S) / step) + 1) windows + num_windows = (T - S + step - 1) // step + 1 + # We process only the current video chunk in the online mode + indices = [self.online_ind] if is_online else range(0, step * num_windows, step) + + coords_init = queried_coords.reshape(B, 1, N, 2).expand(B, S, N, 2).float() + vis_init = torch.ones((B, S, N, 1), device=device).float() * 10 + for ind in indices: + # We copy over coords and vis for tracks that are queried + # by the end of the previous window, which is ind + overlap + if ind > 0: + overlap = S - step + copy_over = (queried_frames < ind + overlap)[:, None, :, None] # B 1 N 1 + coords_prev = torch.nn.functional.pad( + coords_predicted[:, ind : ind + overlap] / self.stride, + (0, 0, 0, 0, 0, step), + "replicate", + ) # B S N 2 + vis_prev = torch.nn.functional.pad( + vis_predicted[:, ind : ind + overlap, :, None].clone(), + (0, 0, 0, 0, 0, step), + "replicate", + ) # B S N 1 + coords_init = torch.where( + copy_over.expand_as(coords_init), coords_prev, coords_init + ) + vis_init = torch.where(copy_over.expand_as(vis_init), vis_prev, vis_init) + + # The attention mask is 1 for the spatio-temporal points within + # a track which is updated in the current window + attention_mask = (queried_frames < ind + S).reshape(B, 1, N).repeat(1, S, 1) # B S N + + # The track mask is 1 for the spatio-temporal points that actually + # need updating: only after begin queried, and not if contained + # in a previous window + track_mask = ( + queried_frames[:, None, :, None] + <= torch.arange(ind, ind + S, device=device)[None, :, None, None] + ).contiguous() # B S N 1 + + if ind > 0: + track_mask[:, :overlap, :, :] = False + + # Predict the coordinates and visibility for the current window + coords, vis = self.forward_window( + fmaps=fmaps if is_online else fmaps[:, ind : ind + S], + coords=coords_init, + track_feat=attention_mask.unsqueeze(-1) * track_feat, + vis=vis_init, + track_mask=track_mask, + attention_mask=attention_mask, + iters=iters, + ) + + S_trimmed = T if is_online else min(T - ind, S) # accounts for last window duration + coords_predicted[:, ind : ind + S] = coords[-1][:, :S_trimmed] + vis_predicted[:, ind : ind + S] = vis[:, :S_trimmed] + if is_train: + all_coords_predictions.append([coord[:, :S_trimmed] for coord in coords]) + all_vis_predictions.append(torch.sigmoid(vis[:, :S_trimmed])) + + if is_online: + self.online_ind += step + self.online_coords_predicted = coords_predicted + self.online_vis_predicted = vis_predicted + vis_predicted = torch.sigmoid(vis_predicted) + + if is_train: + mask = queried_frames[:, None] <= torch.arange(0, T, device=device)[None, :, None] + train_data = (all_coords_predictions, all_vis_predictions, mask) + else: + train_data = None + + return coords_predicted, vis_predicted, train_data + + +class EfficientUpdateFormer(nn.Module): + """ + Transformer model that updates track estimates. + """ + + def __init__( + self, + space_depth=6, + time_depth=6, + input_dim=320, + hidden_size=384, + num_heads=8, + output_dim=130, + mlp_ratio=4.0, + add_space_attn=True, + num_virtual_tracks=64, + ): + super().__init__() + self.out_channels = 2 + self.num_heads = num_heads + self.hidden_size = hidden_size + self.add_space_attn = add_space_attn + self.input_transform = torch.nn.Linear(input_dim, hidden_size, bias=True) + self.flow_head = torch.nn.Linear(hidden_size, output_dim, bias=True) + self.num_virtual_tracks = num_virtual_tracks + self.virual_tracks = nn.Parameter(torch.randn(1, num_virtual_tracks, 1, hidden_size)) + self.time_blocks = nn.ModuleList( + [ + AttnBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + attn_class=Attention, + ) + for _ in range(time_depth) + ] + ) + + if add_space_attn: + self.space_virtual_blocks = nn.ModuleList( + [ + AttnBlock( + hidden_size, + num_heads, + mlp_ratio=mlp_ratio, + attn_class=Attention, + ) + for _ in range(space_depth) + ] + ) + self.space_point2virtual_blocks = nn.ModuleList( + [ + CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) + for _ in range(space_depth) + ] + ) + self.space_virtual2point_blocks = nn.ModuleList( + [ + CrossAttnBlock(hidden_size, hidden_size, num_heads, mlp_ratio=mlp_ratio) + for _ in range(space_depth) + ] + ) + assert len(self.time_blocks) >= len(self.space_virtual2point_blocks) + self.initialize_weights() + + def initialize_weights(self): + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + + self.apply(_basic_init) + + def forward(self, input_tensor, mask=None): + tokens = self.input_transform(input_tensor) + B, _, T, _ = tokens.shape + virtual_tokens = self.virual_tracks.repeat(B, 1, T, 1) + tokens = torch.cat([tokens, virtual_tokens], dim=1) + _, N, _, _ = tokens.shape + + j = 0 + for i in range(len(self.time_blocks)): + time_tokens = tokens.contiguous().view(B * N, T, -1) # B N T C -> (B N) T C + time_tokens = self.time_blocks[i](time_tokens) + + tokens = time_tokens.view(B, N, T, -1) # (B N) T C -> B N T C + if self.add_space_attn and ( + i % (len(self.time_blocks) // len(self.space_virtual_blocks)) == 0 + ): + space_tokens = ( + tokens.permute(0, 2, 1, 3).contiguous().view(B * T, N, -1) + ) # B N T C -> (B T) N C + point_tokens = space_tokens[:, : N - self.num_virtual_tracks] + virtual_tokens = space_tokens[:, N - self.num_virtual_tracks :] + + virtual_tokens = self.space_virtual2point_blocks[j]( + virtual_tokens, point_tokens, mask=mask + ) + virtual_tokens = self.space_virtual_blocks[j](virtual_tokens) + point_tokens = self.space_point2virtual_blocks[j]( + point_tokens, virtual_tokens, mask=mask + ) + space_tokens = torch.cat([point_tokens, virtual_tokens], dim=1) + tokens = space_tokens.view(B, T, N, -1).permute(0, 2, 1, 3) # (B T) N C -> B N T C + j += 1 + tokens = tokens[:, : N - self.num_virtual_tracks] + flow = self.flow_head(tokens) + return flow + + +class CrossAttnBlock(nn.Module): + def __init__(self, hidden_size, context_dim, num_heads=1, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.norm_context = nn.LayerNorm(hidden_size) + self.cross_attn = Attention( + hidden_size, context_dim=context_dim, num_heads=num_heads, qkv_bias=True, **block_kwargs + ) + + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp( + in_features=hidden_size, + hidden_features=mlp_hidden_dim, + act_layer=approx_gelu, + drop=0, + ) + + def forward(self, x, context, mask=None): + if mask is not None: + if mask.shape[1] == x.shape[1]: + mask = mask[:, None, :, None].expand( + -1, self.cross_attn.heads, -1, context.shape[1] + ) + else: + mask = mask[:, None, None].expand(-1, self.cross_attn.heads, x.shape[1], -1) + + max_neg_value = -torch.finfo(x.dtype).max + attn_bias = (~mask) * max_neg_value + x = x + self.cross_attn( + self.norm1(x), context=self.norm_context(context), attn_bias=attn_bias + ) + x = x + self.mlp(self.norm2(x)) + return x diff --git a/cotracker/models/core/cotracker/losses.py b/cotracker/models/core/cotracker/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..2bdcc2ead92b31e4aebce77449a108793d6e5425 --- /dev/null +++ b/cotracker/models/core/cotracker/losses.py @@ -0,0 +1,61 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from cotracker.models.core.model_utils import reduce_masked_mean + +EPS = 1e-6 + + +def balanced_ce_loss(pred, gt, valid=None): + total_balanced_loss = 0.0 + for j in range(len(gt)): + B, S, N = gt[j].shape + # pred and gt are the same shape + for (a, b) in zip(pred[j].size(), gt[j].size()): + assert a == b # some shape mismatch! + # if valid is not None: + for (a, b) in zip(pred[j].size(), valid[j].size()): + assert a == b # some shape mismatch! + + pos = (gt[j] > 0.95).float() + neg = (gt[j] < 0.05).float() + + label = pos * 2.0 - 1.0 + a = -label * pred[j] + b = F.relu(a) + loss = b + torch.log(torch.exp(-b) + torch.exp(a - b)) + + pos_loss = reduce_masked_mean(loss, pos * valid[j]) + neg_loss = reduce_masked_mean(loss, neg * valid[j]) + + balanced_loss = pos_loss + neg_loss + total_balanced_loss += balanced_loss / float(N) + return total_balanced_loss + + +def sequence_loss(flow_preds, flow_gt, vis, valids, gamma=0.8): + """Loss function defined over sequence of flow predictions""" + total_flow_loss = 0.0 + for j in range(len(flow_gt)): + B, S, N, D = flow_gt[j].shape + assert D == 2 + B, S1, N = vis[j].shape + B, S2, N = valids[j].shape + assert S == S1 + assert S == S2 + n_predictions = len(flow_preds[j]) + flow_loss = 0.0 + for i in range(n_predictions): + i_weight = gamma ** (n_predictions - i - 1) + flow_pred = flow_preds[j][i] + i_loss = (flow_pred - flow_gt[j]).abs() # B, S, N, 2 + i_loss = torch.mean(i_loss, dim=3) # B, S, N + flow_loss += i_weight * reduce_masked_mean(i_loss, valids[j]) + flow_loss = flow_loss / n_predictions + total_flow_loss += flow_loss / float(N) + return total_flow_loss diff --git a/cotracker/models/core/embeddings.py b/cotracker/models/core/embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..897cd5d9f41121a9692281a719a2d24914293318 --- /dev/null +++ b/cotracker/models/core/embeddings.py @@ -0,0 +1,120 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple, Union +import torch + + +def get_2d_sincos_pos_embed( + embed_dim: int, grid_size: Union[int, Tuple[int, int]] +) -> torch.Tensor: + """ + This function initializes a grid and generates a 2D positional embedding using sine and cosine functions. + It is a wrapper of get_2d_sincos_pos_embed_from_grid. + Args: + - embed_dim: The embedding dimension. + - grid_size: The grid size. + Returns: + - pos_embed: The generated 2D positional embedding. + """ + if isinstance(grid_size, tuple): + grid_size_h, grid_size_w = grid_size + else: + grid_size_h = grid_size_w = grid_size + grid_h = torch.arange(grid_size_h, dtype=torch.float) + grid_w = torch.arange(grid_size_w, dtype=torch.float) + grid = torch.meshgrid(grid_w, grid_h, indexing="xy") + grid = torch.stack(grid, dim=0) + grid = grid.reshape([2, 1, grid_size_h, grid_size_w]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + return pos_embed.reshape(1, grid_size_h, grid_size_w, -1).permute(0, 3, 1, 2) + + +def get_2d_sincos_pos_embed_from_grid( + embed_dim: int, grid: torch.Tensor +) -> torch.Tensor: + """ + This function generates a 2D positional embedding from a given grid using sine and cosine functions. + + Args: + - embed_dim: The embedding dimension. + - grid: The grid to generate the embedding from. + + Returns: + - emb: The generated 2D positional embedding. + """ + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = torch.cat([emb_h, emb_w], dim=2) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid( + embed_dim: int, pos: torch.Tensor +) -> torch.Tensor: + """ + This function generates a 1D positional embedding from a given grid using sine and cosine functions. + + Args: + - embed_dim: The embedding dimension. + - pos: The position to generate the embedding from. + + Returns: + - emb: The generated 1D positional embedding. + """ + assert embed_dim % 2 == 0 + omega = torch.arange(embed_dim // 2, dtype=torch.double) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = torch.sin(out) # (M, D/2) + emb_cos = torch.cos(out) # (M, D/2) + + emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D) + return emb[None].float() + + +def get_2d_embedding(xy: torch.Tensor, C: int, cat_coords: bool = True) -> torch.Tensor: + """ + This function generates a 2D positional embedding from given coordinates using sine and cosine functions. + + Args: + - xy: The coordinates to generate the embedding from. + - C: The size of the embedding. + - cat_coords: A flag to indicate whether to concatenate the original coordinates to the embedding. + + Returns: + - pe: The generated 2D positional embedding. + """ + B, N, D = xy.shape + assert D == 2 + + x = xy[:, :, 0:1] + y = xy[:, :, 1:2] + div_term = ( + torch.arange(0, C, 2, device=xy.device, dtype=torch.float32) * (1000.0 / C) + ).reshape(1, 1, int(C / 2)) + + pe_x = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32) + pe_y = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32) + + pe_x[:, :, 0::2] = torch.sin(x * div_term) + pe_x[:, :, 1::2] = torch.cos(x * div_term) + + pe_y[:, :, 0::2] = torch.sin(y * div_term) + pe_y[:, :, 1::2] = torch.cos(y * div_term) + + pe = torch.cat([pe_x, pe_y], dim=2) # (B, N, C*3) + if cat_coords: + pe = torch.cat([xy, pe], dim=2) # (B, N, C*3+3) + return pe diff --git a/cotracker/models/core/model_utils.py b/cotracker/models/core/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e688e85ac3ebf59cab6aa1a5a5ac5119048386 --- /dev/null +++ b/cotracker/models/core/model_utils.py @@ -0,0 +1,271 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from typing import Optional, Tuple + +EPS = 1e-6 + + +def smart_cat(tensor1, tensor2, dim): + if tensor1 is None: + return tensor2 + return torch.cat([tensor1, tensor2], dim=dim) + + +def get_points_on_a_grid( + size: int, + extent: Tuple[float, ...], + center: Optional[Tuple[float, ...]] = None, + device: Optional[torch.device] = torch.device("cpu"), + shift_grid: bool = False, +): + r"""Get a grid of points covering a rectangular region + + `get_points_on_a_grid(size, extent)` generates a :attr:`size` by + :attr:`size` grid fo points distributed to cover a rectangular area + specified by `extent`. + + The `extent` is a pair of integer :math:`(H,W)` specifying the height + and width of the rectangle. + + Optionally, the :attr:`center` can be specified as a pair :math:`(c_y,c_x)` + specifying the vertical and horizontal center coordinates. The center + defaults to the middle of the extent. + + Points are distributed uniformly within the rectangle leaving a margin + :math:`m=W/64` from the border. + + It returns a :math:`(1, \text{size} \times \text{size}, 2)` tensor of + points :math:`P_{ij}=(x_i, y_i)` where + + .. math:: + P_{ij} = \left( + c_x + m -\frac{W}{2} + \frac{W - 2m}{\text{size} - 1}\, j,~ + c_y + m -\frac{H}{2} + \frac{H - 2m}{\text{size} - 1}\, i + \right) + + Points are returned in row-major order. + + Args: + size (int): grid size. + extent (tuple): height and with of the grid extent. + center (tuple, optional): grid center. + device (str, optional): Defaults to `"cpu"`. + + Returns: + Tensor: grid. + """ + if size == 1: + return torch.tensor([extent[1] / 2, extent[0] / 2], device=device)[None, None] + + if center is None: + center = [extent[0] / 2, extent[1] / 2] + + margin = extent[1] / 64 + range_y = (margin - extent[0] / 2 + center[0], extent[0] / 2 + center[0] - margin) + range_x = (margin - extent[1] / 2 + center[1], extent[1] / 2 + center[1] - margin) + grid_y, grid_x = torch.meshgrid( + torch.linspace(*range_y, size, device=device), + torch.linspace(*range_x, size, device=device), + indexing="ij", + ) + + if shift_grid: + # shift the grid randomly + # grid_x: (10, 10) + # grid_y: (10, 10) + shift_x = (range_x[1] - range_x[0]) / (size - 1) + shift_y = (range_y[1] - range_y[0]) / (size - 1) + grid_x = grid_x + torch.randn_like(grid_x) / 3 * shift_x / 2 + grid_y = grid_y + torch.randn_like(grid_y) / 3 * shift_y / 2 + + # stay within the bounds + grid_x = torch.clamp(grid_x, range_x[0], range_x[1]) + grid_y = torch.clamp(grid_y, range_y[0], range_y[1]) + + return torch.stack([grid_x, grid_y], dim=-1).reshape(1, -1, 2) + + +def reduce_masked_mean(input, mask, dim=None, keepdim=False): + r"""Masked mean + + `reduce_masked_mean(x, mask)` computes the mean of a tensor :attr:`input` + over a mask :attr:`mask`, returning + + .. math:: + \text{output} = + \frac + {\sum_{i=1}^N \text{input}_i \cdot \text{mask}_i} + {\epsilon + \sum_{i=1}^N \text{mask}_i} + + where :math:`N` is the number of elements in :attr:`input` and + :attr:`mask`, and :math:`\epsilon` is a small constant to avoid + division by zero. + + `reduced_masked_mean(x, mask, dim)` computes the mean of a tensor + :attr:`input` over a mask :attr:`mask` along a dimension :attr:`dim`. + Optionally, the dimension can be kept in the output by setting + :attr:`keepdim` to `True`. Tensor :attr:`mask` must be broadcastable to + the same dimension as :attr:`input`. + + The interface is similar to `torch.mean()`. + + Args: + inout (Tensor): input tensor. + mask (Tensor): mask. + dim (int, optional): Dimension to sum over. Defaults to None. + keepdim (bool, optional): Keep the summed dimension. Defaults to False. + + Returns: + Tensor: mean tensor. + """ + + mask = mask.expand_as(input) + + prod = input * mask + + if dim is None: + numer = torch.sum(prod) + denom = torch.sum(mask) + else: + numer = torch.sum(prod, dim=dim, keepdim=keepdim) + denom = torch.sum(mask, dim=dim, keepdim=keepdim) + + mean = numer / (EPS + denom) + return mean + + +def bilinear_sampler(input, coords, align_corners=True, padding_mode="border"): + r"""Sample a tensor using bilinear interpolation + + `bilinear_sampler(input, coords)` samples a tensor :attr:`input` at + coordinates :attr:`coords` using bilinear interpolation. It is the same + as `torch.nn.functional.grid_sample()` but with a different coordinate + convention. + + The input tensor is assumed to be of shape :math:`(B, C, H, W)`, where + :math:`B` is the batch size, :math:`C` is the number of channels, + :math:`H` is the height of the image, and :math:`W` is the width of the + image. The tensor :attr:`coords` of shape :math:`(B, H_o, W_o, 2)` is + interpreted as an array of 2D point coordinates :math:`(x_i,y_i)`. + + Alternatively, the input tensor can be of size :math:`(B, C, T, H, W)`, + in which case sample points are triplets :math:`(t_i,x_i,y_i)`. Note + that in this case the order of the components is slightly different + from `grid_sample()`, which would expect :math:`(x_i,y_i,t_i)`. + + If `align_corners` is `True`, the coordinate :math:`x` is assumed to be + in the range :math:`[0,W-1]`, with 0 corresponding to the center of the + left-most image pixel :math:`W-1` to the center of the right-most + pixel. + + If `align_corners` is `False`, the coordinate :math:`x` is assumed to + be in the range :math:`[0,W]`, with 0 corresponding to the left edge of + the left-most pixel :math:`W` to the right edge of the right-most + pixel. + + Similar conventions apply to the :math:`y` for the range + :math:`[0,H-1]` and :math:`[0,H]` and to :math:`t` for the range + :math:`[0,T-1]` and :math:`[0,T]`. + + Args: + input (Tensor): batch of input images. + coords (Tensor): batch of coordinates. + align_corners (bool, optional): Coordinate convention. Defaults to `True`. + padding_mode (str, optional): Padding mode. Defaults to `"border"`. + + Returns: + Tensor: sampled points. + """ + + sizes = input.shape[2:] + + assert len(sizes) in [2, 3] + + if len(sizes) == 3: + # t x y -> x y t to match dimensions T H W in grid_sample + coords = coords[..., [1, 2, 0]] + + if align_corners: + coords = coords * torch.tensor( + [2 / max(size - 1, 1) for size in reversed(sizes)], device=coords.device + ) + else: + coords = coords * torch.tensor([2 / size for size in reversed(sizes)], device=coords.device) + + coords -= 1 + + return F.grid_sample(input, coords, align_corners=align_corners, padding_mode=padding_mode) + + +def sample_features4d(input, coords): + r"""Sample spatial features + + `sample_features4d(input, coords)` samples the spatial features + :attr:`input` represented by a 4D tensor :math:`(B, C, H, W)`. + + The field is sampled at coordinates :attr:`coords` using bilinear + interpolation. :attr:`coords` is assumed to be of shape :math:`(B, R, + 3)`, where each sample has the format :math:`(x_i, y_i)`. This uses the + same convention as :func:`bilinear_sampler` with `align_corners=True`. + + The output tensor has one feature per point, and has shape :math:`(B, + R, C)`. + + Args: + input (Tensor): spatial features. + coords (Tensor): points. + + Returns: + Tensor: sampled features. + """ + + B, _, _, _ = input.shape + + # B R 2 -> B R 1 2 + coords = coords.unsqueeze(2) + + # B C R 1 + feats = bilinear_sampler(input, coords) + + return feats.permute(0, 2, 1, 3).view( + B, -1, feats.shape[1] * feats.shape[3] + ) # B C R 1 -> B R C + + +def sample_features5d(input, coords): + r"""Sample spatio-temporal features + + `sample_features5d(input, coords)` works in the same way as + :func:`sample_features4d` but for spatio-temporal features and points: + :attr:`input` is a 5D tensor :math:`(B, T, C, H, W)`, :attr:`coords` is + a :math:`(B, R1, R2, 3)` tensor of spatio-temporal point :math:`(t_i, + x_i, y_i)`. The output tensor has shape :math:`(B, R1, R2, C)`. + + Args: + input (Tensor): spatio-temporal features. + coords (Tensor): spatio-temporal points. + + Returns: + Tensor: sampled features. + """ + + B, T, _, _, _ = input.shape + + # B T C H W -> B C T H W + input = input.permute(0, 2, 1, 3, 4) + + # B R1 R2 3 -> B R1 R2 1 3 + coords = coords.unsqueeze(3) + + # B C R1 R2 1 + feats = bilinear_sampler(input, coords) + + return feats.permute(0, 2, 3, 1, 4).view( + B, feats.shape[2], feats.shape[3], feats.shape[1] + ) # B C R1 R2 1 -> B R1 R2 C diff --git a/cotracker/models/evaluation_predictor.py b/cotracker/models/evaluation_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..87f8e18611e88fce4b69346d2210cf3c32d206fe --- /dev/null +++ b/cotracker/models/evaluation_predictor.py @@ -0,0 +1,104 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F +from typing import Tuple + +from cotracker.models.core.cotracker.cotracker import CoTracker2 +from cotracker.models.core.model_utils import get_points_on_a_grid + + +class EvaluationPredictor(torch.nn.Module): + def __init__( + self, + cotracker_model: CoTracker2, + interp_shape: Tuple[int, int] = (384, 512), + grid_size: int = 5, + local_grid_size: int = 8, + single_point: bool = True, + n_iters: int = 6, + ) -> None: + super(EvaluationPredictor, self).__init__() + self.grid_size = grid_size + self.local_grid_size = local_grid_size + self.single_point = single_point + self.interp_shape = interp_shape + self.n_iters = n_iters + + self.model = cotracker_model + self.model.eval() + + def forward(self, video, queries): + queries = queries.clone() + B, T, C, H, W = video.shape + B, N, D = queries.shape + + assert D == 3 + + video = video.reshape(B * T, C, H, W) + video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True) + video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1]) + + device = video.device + + queries[:, :, 1] *= (self.interp_shape[1] - 1) / (W - 1) + queries[:, :, 2] *= (self.interp_shape[0] - 1) / (H - 1) + + if self.single_point: + traj_e = torch.zeros((B, T, N, 2), device=device) + vis_e = torch.zeros((B, T, N), device=device) + for pind in range((N)): + query = queries[:, pind : pind + 1] + + t = query[0, 0, 0].long() + + traj_e_pind, vis_e_pind = self._process_one_point(video, query) + traj_e[:, t:, pind : pind + 1] = traj_e_pind[:, :, :1] + vis_e[:, t:, pind : pind + 1] = vis_e_pind[:, :, :1] + else: + if self.grid_size > 0: + xy = get_points_on_a_grid(self.grid_size, video.shape[3:]) + xy = torch.cat([torch.zeros_like(xy[:, :, :1]), xy], dim=2).to(device) # + queries = torch.cat([queries, xy], dim=1) # + + traj_e, vis_e, __ = self.model( + video=video, + queries=queries, + iters=self.n_iters, + ) + + traj_e[:, :, :, 0] *= (W - 1) / float(self.interp_shape[1] - 1) + traj_e[:, :, :, 1] *= (H - 1) / float(self.interp_shape[0] - 1) + return traj_e, vis_e + + def _process_one_point(self, video, query): + t = query[0, 0, 0].long() + + device = query.device + if self.local_grid_size > 0: + xy_target = get_points_on_a_grid( + self.local_grid_size, + (50, 50), + [query[0, 0, 2].item(), query[0, 0, 1].item()], + ) + + xy_target = torch.cat([torch.zeros_like(xy_target[:, :, :1]), xy_target], dim=2).to( + device + ) # + query = torch.cat([query, xy_target], dim=1) # + + if self.grid_size > 0: + xy = get_points_on_a_grid(self.grid_size, video.shape[3:]) + xy = torch.cat([torch.zeros_like(xy[:, :, :1]), xy], dim=2).to(device) # + query = torch.cat([query, xy], dim=1) # + # crop the video to start from the queried frame + query[0, 0, 0] = 0 + traj_e_pind, vis_e_pind, __ = self.model( + video=video[:, t:], queries=query, iters=self.n_iters + ) + + return traj_e_pind, vis_e_pind diff --git a/cotracker/predictor.py b/cotracker/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..32d943f2e2c4cfb544d1f1e63fd4289f79dce206 --- /dev/null +++ b/cotracker/predictor.py @@ -0,0 +1,470 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import warnings +import numpy as np +import cv2 + +import torch +import torch.nn.functional as F + +from cotracker.models.core.model_utils import smart_cat, get_points_on_a_grid +from cotracker.models.build_cotracker import build_cotracker + + +def gen_gaussian_heatmap(imgSize=200): + circle_img = np.zeros((imgSize, imgSize), np.float32) + circle_mask = cv2.circle(circle_img, (imgSize//2, imgSize//2), imgSize//2, 1, -1) + + isotropicGrayscaleImage = np.zeros((imgSize, imgSize), np.float32) + + # Guass Map + for i in range(imgSize): + for j in range(imgSize): + isotropicGrayscaleImage[i, j] = 1 / 2 / np.pi / (40 ** 2) * np.exp( + -1 / 2 * ((i - imgSize / 2) ** 2 / (40 ** 2) + (j - imgSize / 2) ** 2 / (40 ** 2))) + + isotropicGrayscaleImage = isotropicGrayscaleImage * circle_mask + isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)).astype(np.float32) + isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)*255).astype(np.uint8) + + # isotropicGrayscaleImage = cv2.resize(isotropicGrayscaleImage, (40, 40)) + return isotropicGrayscaleImage + + +def draw_heatmap(img, center_coordinate, heatmap_template, side, width, height): + x1 = max(center_coordinate[0] - side, 1) + x2 = min(center_coordinate[0] + side, width - 1) + y1 = max(center_coordinate[1] - side, 1) + y2 = min(center_coordinate[1] + side, height - 1) + x1, x2, y1, y2 = int(x1), int(x2), int(y1), int(y2) + + if (x2 - x1) < 1 or (y2 - y1) < 1: + print(center_coordinate, "x1, x2, y1, y2", x1, x2, y1, y2) + return img + + need_map = cv2.resize(heatmap_template, (x2-x1, y2-y1)) + + img[y1:y2,x1:x2] = need_map + + return img + + +def generate_gassian_heatmap(pred_tracks, pred_visibility=None, image_size=None, side=20): + width, height = image_size + num_frames, num_points = pred_tracks.shape[:2] + + point_index_list = [point_idx for point_idx in range(num_points)] + heatmap_template = gen_gaussian_heatmap() + + + image_list = [] + for frame_idx in range(num_frames): + + img = np.zeros((height, width), np.float32) + for point_idx in point_index_list: + px, py = pred_tracks[frame_idx, point_idx] + + if px < 0 or py < 0 or px >= width or py >= height: + continue + + if pred_visibility is not None: + if (not pred_visibility[frame_idx, point_idx]): + continue + + img = draw_heatmap(img, (px, py), heatmap_template, side, width, height) + + img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_GRAY2RGB) + img = torch.from_numpy(img).permute(2, 0, 1).contiguous() + image_list.append(img) + + video_gaussion_map = torch.stack(image_list, dim=0) + + return video_gaussion_map + + +# TODO: need further check and investigation +def sample_trajectories( + pred_tracks, pred_visibility, + max_points=10, + motion_threshold=1, + vis_threshold=5, + + ): + # pred_tracks: (b, f, num_points, 2) + # pred_visibility: (b, f, num_points) + batch_size, num_frames, num_points = pred_visibility.shape + + # 1. Remove points with low visibility + mask = pred_visibility.sum(dim=1) > vis_threshold + mask = mask.unsqueeze(1).repeat(1, num_frames, 1) + pred_tracks = pred_tracks[mask].view(batch_size, num_frames, -1, 2) + pred_visibility = pred_visibility[mask].view(batch_size, num_frames, -1) + + # 2. Thresholding: remove points with too small motions + # compute the motion of each point + diff = pred_tracks[:, 1:] - pred_tracks[:, :-1] + # (b, f-1, num_points), sqrt(x^2 + y^2) + motion = torch.norm(diff, dim=-1) + # (b, num_points), mean motion for each point + motion = torch.mean(motion, dim=1) + # apply threshold + mask = motion > motion_threshold # (b, num_points) + assert mask.shape[0] == 1 + num_keeped = mask.sum() + if num_keeped < max_points: + indices = torch.argsort(motion, dim=-1, descending=True)[:, :max_points] # (bs, max_points) + mask = torch.zeros_like(mask) # (bs, num_points) + # set mask to 1 for the top max_points + mask[0, indices] = 1 + num_keeped = mask.sum() # note sometimes mask.sum() < max_points + + motion = motion[mask].view(batch_size, num_keeped) + # keep shape + mask = mask.unsqueeze(1).repeat(1, num_frames, 1) + pred_tracks = pred_tracks[mask].view(batch_size, num_frames, num_keeped, 2) + pred_visibility = pred_visibility[mask].view(batch_size, num_frames, num_keeped) + + + # 3. Sampling with larger prob for large motions + num_points = min(max_points, num_keeped) + if num_points == 0: + warnings.warn("No points left after filtering") + return None, None + + prob = motion / motion.max() + prob = prob / prob.sum() + sampled_indices = torch.multinomial(prob, num_points, replacement=False) + + sampled_indices = sampled_indices.squeeze(0) # (num_points, ) + pred_tracks_sampled = pred_tracks[:, :, sampled_indices] + pred_visibility_sampled = pred_visibility[:, :, sampled_indices] + + return pred_tracks_sampled, pred_visibility_sampled +def sample_trajectories_with_ref( + pred_tracks, pred_visibility, coords0, + max_points=10, + motion_threshold=1, + vis_threshold=5, + ): + + + + batch_size, num_frames, num_points = pred_visibility.shape + + + visibility_sum = pred_visibility.sum(dim=1) + vis_mask = visibility_sum > vis_threshold # (batch_size, num_points) + + + + pred_tracks = pred_tracks * vis_mask.unsqueeze(1).unsqueeze(-1) # (batch_size, num_frames, num_points, 2) + pred_visibility = pred_visibility * vis_mask.unsqueeze(1) + + + indices = vis_mask.nonzero(as_tuple=False) # (num_visible_points, 2) + if indices.size(0) == 0: + warnings.warn("No points left after visibility filtering") + return None, None, None + + batch_indices, point_indices = indices[:, 0], indices[:, 1] + + coords0_filtered = coords0[batch_indices, point_indices] # (num_visible_points, 2) + + + diff = pred_tracks[:, 1:] - pred_tracks[:, :-1] # (batch_size, num_frames-1, num_points, 2) + motion = torch.norm(diff, dim=-1).mean(dim=1) # (batch_size, num_points) + + motion_mask = motion > motion_threshold + combined_mask = vis_mask & motion_mask # (batch_size, num_points) + + + indices = combined_mask.nonzero(as_tuple=False) + if indices.size(0) == 0: + warnings.warn("No points left after motion filtering") + return None, None, None + + batch_indices, point_indices = indices[:, 0], indices[:, 1] + + pred_tracks_filtered = pred_tracks[batch_indices, :, point_indices, :] # (num_filtered_points, num_frames, 2) + pred_visibility_filtered = pred_visibility[batch_indices, :, point_indices] # (num_filtered_points, num_frames) + coords0_filtered = coords0[batch_indices, point_indices, :] # (num_filtered_points, 2) + motion_filtered = motion[batch_indices, point_indices] # (num_filtered_points) + + + num_keeped = motion_filtered.size(0) + num_points_sampled = min(max_points, num_keeped) + if num_points_sampled == 0: + warnings.warn("No points left after filtering") + return None, None, None + + prob = motion_filtered / motion_filtered.max() + prob = prob / prob.sum() + sampled_indices = torch.multinomial(prob, num_points_sampled, replacement=False) + + pred_tracks_sampled = pred_tracks_filtered[sampled_indices] # (num_points_sampled, num_frames, 2) + pred_visibility_sampled = pred_visibility_filtered[sampled_indices] # (num_points_sampled, num_frames) + coords0_sampled = coords0_filtered[sampled_indices] # (num_points_sampled, 2) + + + pred_tracks_sampled = pred_tracks_sampled.view(batch_size, num_points_sampled, num_frames, 2).transpose(1, 2) + pred_visibility_sampled = pred_visibility_sampled.view(batch_size, num_points_sampled, num_frames).transpose(1, 2) + coords0_sampled = coords0_sampled.view(batch_size, num_points_sampled, 2) + + return pred_tracks_sampled, pred_visibility_sampled, coords0_sampled + + +class CoTrackerPredictor(torch.nn.Module): + def __init__( + self, + checkpoint="./checkpoints/cotracker2.pth", + shift_grid=False, + ): + + super().__init__() + self.support_grid_size = 6 + model = build_cotracker(checkpoint) + self.interp_shape = model.model_resolution + self.model = model + self.model.eval() + self.shift_grid = shift_grid + + @torch.no_grad() + def forward( + self, + video, # (B, T, 3, H, W) + # input prompt types: + # - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame. + # *backward_tracking=True* will compute tracks in both directions. + # - queries. Queried points of shape (B, N, 3) in format (t, x, y) for frame index and pixel coordinates. + # - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask. + # You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks. + queries: torch.Tensor = None, + segm_mask: torch.Tensor = None, # Segmentation mask of shape (B, 1, H, W) + grid_size: int = 0, + grid_query_frame: int = 0, # only for dense and regular grid tracks + backward_tracking: bool = False, + ): + if queries is None and grid_size == 0: + tracks, visibilities = self._compute_dense_tracks( + video, + grid_query_frame=grid_query_frame, + backward_tracking=backward_tracking, + ) + else: + tracks, visibilities = self._compute_sparse_tracks( + video, + queries, + segm_mask, + grid_size, + add_support_grid=(grid_size == 0 or segm_mask is not None), + grid_query_frame=grid_query_frame, + backward_tracking=backward_tracking, + ) + + return tracks, visibilities + + def _compute_dense_tracks(self, video, grid_query_frame, grid_size=80, backward_tracking=False): + *_, H, W = video.shape + grid_step = W // grid_size + grid_width = W // grid_step + grid_height = H // grid_step + tracks = visibilities = None + grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to(video.device) + grid_pts[0, :, 0] = grid_query_frame + for offset in range(grid_step * grid_step): + print(f"step {offset} / {grid_step * grid_step}") + ox = offset % grid_step + oy = offset // grid_step + grid_pts[0, :, 1] = torch.arange(grid_width).repeat(grid_height) * grid_step + ox + grid_pts[0, :, 2] = ( + torch.arange(grid_height).repeat_interleave(grid_width) * grid_step + oy + ) + tracks_step, visibilities_step = self._compute_sparse_tracks( + video=video, + queries=grid_pts, + backward_tracking=backward_tracking, + ) + tracks = smart_cat(tracks, tracks_step, dim=2) + visibilities = smart_cat(visibilities, visibilities_step, dim=2) + + return tracks, visibilities + + def _compute_sparse_tracks( + self, + video, + queries, + segm_mask=None, + grid_size=0, + add_support_grid=False, + grid_query_frame=0, + backward_tracking=False, + ): + B, T, C, H, W = video.shape + + video = video.reshape(B * T, C, H, W) + video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True) + video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1]) + + if queries is not None: + B, N, D = queries.shape + assert D == 3 + queries = queries.clone() + queries[:, :, 1:] *= queries.new_tensor( + [ + (self.interp_shape[1] - 1) / (W - 1), + (self.interp_shape[0] - 1) / (H - 1), + ] + ) + elif grid_size > 0: + grid_pts = get_points_on_a_grid(grid_size, self.interp_shape, device=video.device, shift_grid=self.shift_grid) + if segm_mask is not None: + segm_mask = F.interpolate(segm_mask, tuple(self.interp_shape), mode="nearest") + point_mask = segm_mask[0, 0][ + (grid_pts[0, :, 1]).round().long().cpu(), + (grid_pts[0, :, 0]).round().long().cpu(), + ].bool() + grid_pts = grid_pts[:, point_mask] + + queries = torch.cat( + [torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts], + dim=2, + ).repeat(B, 1, 1) + + if add_support_grid: + grid_pts = get_points_on_a_grid( + self.support_grid_size, self.interp_shape, device=video.device, shift_grid=self.shift_grid, + ) + grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2) + grid_pts = grid_pts.repeat(B, 1, 1) + queries = torch.cat([queries, grid_pts], dim=1) + + tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6) + + if backward_tracking: + tracks, visibilities = self._compute_backward_tracks( + video, queries, tracks, visibilities + ) + if add_support_grid: + queries[:, -self.support_grid_size**2 :, 0] = T - 1 + if add_support_grid: + tracks = tracks[:, :, : -self.support_grid_size**2] + visibilities = visibilities[:, :, : -self.support_grid_size**2] + thr = 0.9 + visibilities = visibilities > thr + + # correct query-point predictions + # see https://github.com/facebookresearch/co-tracker/issues/28 + + # TODO: batchify + for i in range(len(queries)): + queries_t = queries[i, : tracks.size(2), 0].to(torch.int64) + arange = torch.arange(0, len(queries_t)) + + # overwrite the predictions with the query points + tracks[i, queries_t, arange] = queries[i, : tracks.size(2), 1:] + + # correct visibilities, the query points should be visible + visibilities[i, queries_t, arange] = True + + tracks *= tracks.new_tensor( + [(W - 1) / (self.interp_shape[1] - 1), (H - 1) / (self.interp_shape[0] - 1)] + ) + return tracks, visibilities + + def _compute_backward_tracks(self, video, queries, tracks, visibilities): + inv_video = video.flip(1).clone() + inv_queries = queries.clone() + inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1 + + inv_tracks, inv_visibilities, __ = self.model(video=inv_video, queries=inv_queries, iters=6) + + inv_tracks = inv_tracks.flip(1) + inv_visibilities = inv_visibilities.flip(1) + arange = torch.arange(video.shape[1], device=queries.device)[None, :, None] + + mask = (arange < queries[:, None, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2) + + tracks[mask] = inv_tracks[mask] + visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]] + return tracks, visibilities + + +class CoTrackerOnlinePredictor(torch.nn.Module): + def __init__(self, checkpoint="./checkpoints/cotracker2.pth"): + super().__init__() + self.support_grid_size = 6 + model = build_cotracker(checkpoint) + self.interp_shape = model.model_resolution + self.step = model.window_len // 2 + self.model = model + self.model.eval() + + @torch.no_grad() + def forward( + self, + video_chunk, + is_first_step: bool = False, + queries: torch.Tensor = None, + grid_size: int = 10, + grid_query_frame: int = 0, + add_support_grid=False, + ): + B, T, C, H, W = video_chunk.shape + # Initialize online video processing and save queried points + # This needs to be done before processing *each new video* + if is_first_step: + self.model.init_video_online_processing() + if queries is not None: + B, N, D = queries.shape + assert D == 3 + queries = queries.clone() + queries[:, :, 1:] *= queries.new_tensor( + [ + (self.interp_shape[1] - 1) / (W - 1), + (self.interp_shape[0] - 1) / (H - 1), + ] + ) + elif grid_size > 0: + grid_pts = get_points_on_a_grid( + grid_size, self.interp_shape, device=video_chunk.device + ) + queries = torch.cat( + [torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts], + dim=2, + ) + if add_support_grid: + grid_pts = get_points_on_a_grid( + self.support_grid_size, self.interp_shape, device=video_chunk.device + ) + grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2) + queries = torch.cat([queries, grid_pts], dim=1) + self.queries = queries + return (None, None) + + video_chunk = video_chunk.reshape(B * T, C, H, W) + video_chunk = F.interpolate( + video_chunk, tuple(self.interp_shape), mode="bilinear", align_corners=True + ) + video_chunk = video_chunk.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1]) + + tracks, visibilities, __ = self.model( + video=video_chunk, + queries=self.queries, + iters=6, + is_online=True, + ) + thr = 0.9 + return ( + tracks + * tracks.new_tensor( + [ + (W - 1) / (self.interp_shape[1] - 1), + (H - 1) / (self.interp_shape[0] - 1), + ] + ), + visibilities > thr, + ) diff --git a/cotracker/project/CODE_OF_CONDUCT.md b/cotracker/project/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..f913b6a55a6c5ab6e1224e11fc039c3d4c3b6283 --- /dev/null +++ b/cotracker/project/CODE_OF_CONDUCT.md @@ -0,0 +1,80 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic +address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a +professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. 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The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq \ No newline at end of file diff --git a/cotracker/project/CONTRIBUTING.md b/cotracker/project/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..f3ed8c2929373655dfdc962d52978708a3cebbaf --- /dev/null +++ b/cotracker/project/CONTRIBUTING.md @@ -0,0 +1,28 @@ +# CoTracker +We want to make contributing to this project as easy and transparent as possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `main`. +2. If you've changed APIs, update the documentation. +3. Make sure your code lints. +4. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Meta's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. \ No newline at end of file diff --git a/cotracker/project/README.md b/cotracker/project/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4f483f226c92d516a1ac96bc2a0f3c21e3d300d0 --- /dev/null +++ b/cotracker/project/README.md @@ -0,0 +1,248 @@ +# CoTracker: It is Better to Track Together + +**[Meta AI Research, GenAI](https://ai.facebook.com/research/)**; **[University of Oxford, VGG](https://www.robots.ox.ac.uk/~vgg/)** + +[Nikita Karaev](https://nikitakaraevv.github.io/), [Ignacio Rocco](https://www.irocco.info/), [Benjamin Graham](https://ai.facebook.com/people/benjamin-graham/), [Natalia Neverova](https://nneverova.github.io/), [Andrea Vedaldi](https://www.robots.ox.ac.uk/~vedaldi/), [Christian Rupprecht](https://chrirupp.github.io/) + +### [Project Page](https://co-tracker.github.io/) | [Paper](https://arxiv.org/abs/2307.07635) | [X Thread](https://twitter.com/n_karaev/status/1742638906355470772) | [BibTeX](#citing-cotracker) + + + Open In Colab + + + Spaces + + + + +**CoTracker** is a fast transformer-based model that can track any point in a video. It brings to tracking some of the benefits of Optical Flow. + +CoTracker can track: + +- **Any pixel** in a video +- A **quasi-dense** set of pixels together +- Points can be manually selected or sampled on a grid in any video frame + +Try these tracking modes for yourself with our [Colab demo](https://colab.research.google.com/github/facebookresearch/co-tracker/blob/master/notebooks/demo.ipynb) or in the [Hugging Face Space 🤗](https://huggingface.co/spaces/facebook/cotracker). + +**Updates:** + +- [December 27, 2023] 📣 CoTracker2 is now available! It can now track many more (up to **265*265**!) points jointly and it has a cleaner and more memory-efficient implementation. It also supports online processing. See the [updated paper](https://arxiv.org/abs/2307.07635) for more details. The old version remains available [here](https://github.com/facebookresearch/co-tracker/tree/8d364031971f6b3efec945dd15c468a183e58212). + +- [September 5, 2023] 📣 You can now run our Gradio demo [locally](./gradio_demo/app.py)! + +## Quick start +The easiest way to use CoTracker is to load a pretrained model from `torch.hub`: + +### Offline mode: +```pip install imageio[ffmpeg]```, then: +```python +import torch +# Download the video +url = 'https://github.com/facebookresearch/co-tracker/blob/main/assets/apple.mp4' + +import imageio.v3 as iio +frames = iio.imread(url, plugin="FFMPEG") # plugin="pyav" + +device = 'cuda' +grid_size = 10 +video = torch.tensor(frames).permute(0, 3, 1, 2)[None].float().to(device) # B T C H W + +# Run Offline CoTracker: +cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker2").to(device) +pred_tracks, pred_visibility = cotracker(video, grid_size=grid_size) # B T N 2, B T N 1 +``` +### Online mode: +```python +cotracker = torch.hub.load("facebookresearch/co-tracker", "cotracker2_online").to(device) + +# Run Online CoTracker, the same model with a different API: +# Initialize online processing +cotracker(video_chunk=video, is_first_step=True, grid_size=grid_size) + +# Process the video +for ind in range(0, video.shape[1] - cotracker.step, cotracker.step): + pred_tracks, pred_visibility = cotracker( + video_chunk=video[:, ind : ind + cotracker.step * 2] + ) # B T N 2, B T N 1 +``` +Online processing is more memory-efficient and allows for the processing of longer videos. However, in the example provided above, the video length is known! See [the online demo](./online_demo.py) for an example of tracking from an online stream with an unknown video length. + +### Visualize predicted tracks: +```pip install matplotlib```, then: +```python +from cotracker.utils.visualizer import Visualizer + +vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3) +vis.visualize(video, pred_tracks, pred_visibility) +``` + +We offer a number of other ways to interact with CoTracker: + +1. Interactive Gradio demo: + - A demo is available in the [`facebook/cotracker` Hugging Face Space 🤗](https://huggingface.co/spaces/facebook/cotracker). + - You can use the gradio demo locally by running [`python -m gradio_demo.app`](./gradio_demo/app.py) after installing the required packages: `pip install -r gradio_demo/requirements.txt`. +2. Jupyter notebook: + - You can run the notebook in + [Google Colab](https://colab.research.google.com/github/facebookresearch/co-tracker/blob/master/notebooks/demo.ipynb). + - Or explore the notebook located at [`notebooks/demo.ipynb`](./notebooks/demo.ipynb). +2. You can [install](#installation-instructions) CoTracker _locally_ and then: + - Run an *offline* demo with 10 ⨉ 10 points sampled on a grid on the first frame of a video (results will be saved to `./saved_videos/demo.mp4`)): + + ```bash + python demo.py --grid_size 10 + ``` + - Run an *online* demo: + + ```bash + python online_demo.py + ``` + +A GPU is strongly recommended for using CoTracker locally. + + + + +## Installation Instructions +You can use a Pretrained Model via PyTorch Hub, as described above, or install CoTracker from this GitHub repo. +This is the best way if you need to run our local demo or evaluate/train CoTracker. + +Ensure you have both _PyTorch_ and _TorchVision_ installed on your system. Follow the instructions [here](https://pytorch.org/get-started/locally/) for the installation. +We strongly recommend installing both PyTorch and TorchVision with CUDA support, although for small tasks CoTracker can be run on CPU. + + + + +### Install a Development Version + +```bash +git clone https://github.com/facebookresearch/co-tracker +cd co-tracker +pip install -e . +pip install matplotlib flow_vis tqdm tensorboard imageio[ffmpeg] +``` + +You can manually download the CoTracker2 checkpoint from the links below and place it in the `checkpoints` folder as follows: + +```bash +mkdir -p checkpoints +cd checkpoints +wget https://huggingface.co/facebook/cotracker/resolve/main/cotracker2.pth +cd .. +``` +For old checkpoints, see [this section](#previous-version). + +After installation, this is how you could run the model on `./assets/apple.mp4` (results will be saved to `./saved_videos/apple.mp4`): +```bash +python demo.py --checkpoint checkpoints/cotracker2.pth +``` + +## Evaluation + +To reproduce the results presented in the paper, download the following datasets: + +- [TAP-Vid](https://github.com/deepmind/tapnet) +- [Dynamic Replica](https://dynamic-stereo.github.io/) + +And install the necessary dependencies: + +```bash +pip install hydra-core==1.1.0 mediapy +``` + +Then, execute the following command to evaluate on TAP-Vid DAVIS: + +```bash +python ./cotracker/evaluation/evaluate.py --config-name eval_tapvid_davis_first exp_dir=./eval_outputs dataset_root=your/tapvid/path +``` + +By default, evaluation will be slow since it is done for one target point at a time, which ensures robustness and fairness, as described in the paper. + +We have fixed some bugs and retrained the model after updating the paper. These are the numbers that you should be able to reproduce using the released checkpoint and the current version of the codebase: +| | DAVIS First, AJ | DAVIS First, $\delta_\text{avg}^\text{vis}$ | DAVIS First, OA | DAVIS Strided, AJ | DAVIS Strided, $\delta_\text{avg}^\text{vis}$ | DAVIS Strided, OA | DR, $\delta_\text{avg}$| DR, $\delta_\text{avg}^\text{vis}$| DR, $\delta_\text{avg}^\text{occ}$| +| :---: |:---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | +| CoTracker2, 27.12.23 | 60.9 | 75.4 | 88.4 | 65.1 | 79.0 | 89.4 | 61.4 | 68.4 | 38.2 + + +## Training + +To train the CoTracker as described in our paper, you first need to generate annotations for [Google Kubric](https://github.com/google-research/kubric) MOVI-f dataset. +Instructions for annotation generation can be found [here](https://github.com/deepmind/tapnet). +You can also find a discussion on dataset generation in [this issue](https://github.com/facebookresearch/co-tracker/issues/8). + +Once you have the annotated dataset, you need to make sure you followed the steps for evaluation setup and install the training dependencies: + +```bash +pip install pytorch_lightning==1.6.0 tensorboard +``` + +Now you can launch training on Kubric. +Our model was trained for 50000 iterations on 32 GPUs (4 nodes with 8 GPUs). +Modify _dataset_root_ and _ckpt_path_ accordingly before running this command. For training on 4 nodes, add `--num_nodes 4`. + +```bash +python train.py --batch_size 1 \ +--num_steps 50000 --ckpt_path ./ --dataset_root ./datasets --model_name cotracker \ +--save_freq 200 --sequence_len 24 --eval_datasets dynamic_replica tapvid_davis_first \ +--traj_per_sample 768 --sliding_window_len 8 \ +--num_virtual_tracks 64 --model_stride 4 +``` + + +## Development + +### Building the documentation + +To build CoTracker documentation, first install the dependencies: + +```bash +pip install sphinx +pip install sphinxcontrib-bibtex +``` + +Then you can use this command to generate the documentation in the `docs/_build/html` folder: + +```bash +make -C docs html +``` + + +## Previous version +You can use CoTracker v1 directly via pytorch hub: +```python +import torch +import einops +import timm +import tqdm + +cotracker = torch.hub.load("facebookresearch/co-tracker:v1.0", "cotracker_w8") +``` +The old version of the code is available [here](https://github.com/facebookresearch/co-tracker/tree/8d364031971f6b3efec945dd15c468a183e58212). +You can also download the corresponding checkpoints: +```bash +wget https://dl.fbaipublicfiles.com/cotracker/cotracker_stride_4_wind_8.pth +wget https://dl.fbaipublicfiles.com/cotracker/cotracker_stride_4_wind_12.pth +wget https://dl.fbaipublicfiles.com/cotracker/cotracker_stride_8_wind_16.pth +``` + + +## License + +The majority of CoTracker is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Particle Video Revisited is licensed under the MIT license, TAP-Vid is licensed under the Apache 2.0 license. + +## Acknowledgments + +We would like to thank [PIPs](https://github.com/aharley/pips) and [TAP-Vid](https://github.com/deepmind/tapnet) for publicly releasing their code and data. We also want to thank [Luke Melas-Kyriazi](https://lukemelas.github.io/) for proofreading the paper, [Jianyuan Wang](https://jytime.github.io/), [Roman Shapovalov](https://shapovalov.ro/) and [Adam W. Harley](https://adamharley.com/) for the insightful discussions. + +## Citing CoTracker + +If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work: + +```bibtex +@article{karaev2023cotracker, + title={CoTracker: It is Better to Track Together}, + author={Nikita Karaev and Ignacio Rocco and Benjamin Graham and Natalia Neverova and Andrea Vedaldi and Christian Rupprecht}, + journal={arXiv:2307.07635}, + year={2023} +} +``` diff --git a/cotracker/project/batch_demo.py b/cotracker/project/batch_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..d76e72fc59173213682ee2c2dfd7759c0dab2793 --- /dev/null +++ b/cotracker/project/batch_demo.py @@ -0,0 +1,106 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import glob +import os +import torch +import argparse +import numpy as np + +from PIL import Image +from cotracker.utils.visualizer import Visualizer, read_video_from_path +from cotracker.predictor import CoTrackerPredictor + +# Unfortunately MPS acceleration does not support all the features we require, +# but we may be able to enable it in the future + +DEFAULT_DEVICE = ( + # "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + "cuda" + if torch.cuda.is_available() + else "cpu" +) + +# if DEFAULT_DEVICE == "mps": +# os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--video_path", + default="./assets/apple.mp4", + help="path to a video", + ) + parser.add_argument( + "--mask_path", + default="./assets/apple_mask.png", + help="path to a segmentation mask", + ) + parser.add_argument( + "--checkpoint", + default="./checkpoints/cotracker2.pth", + # default=None, + help="CoTracker model parameters", + ) + parser.add_argument("--grid_size", type=int, default=10, help="Regular grid size") + parser.add_argument( + "--grid_query_frame", + type=int, + default=0, + help="Compute dense and grid tracks starting from this frame", + ) + + parser.add_argument( + "--backward_tracking", + action="store_true", + help="Compute tracks in both directions, not only forward", + ) + + args = parser.parse_args() + if args.checkpoint is not None: + model = CoTrackerPredictor(checkpoint=args.checkpoint) + else: + model = torch.hub.load("facebookresearch/co-tracker", "cotracker2") + model = model.to(DEFAULT_DEVICE) + + + video_path_list = glob.glob("assets/*.mp4") + # video_path_list = glob.glob("data/vid/*.mp4") + + # sort + # video_path_list.sort() + for video_path in video_path_list: + args.video_path = video_path + + # load the input video frame by frame + video = read_video_from_path(args.video_path) + # (t, h, w, c) -> (t, c, h, w) + video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float() + # segm_mask = np.array(Image.open(os.path.join(args.mask_path))) + # segm_mask = torch.from_numpy(segm_mask)[None, None] + + video = video.to(DEFAULT_DEVICE) + video = video[:, :200] + with torch.no_grad(): + pred_tracks, pred_visibility = model( + video, + grid_size=args.grid_size, # 10 + grid_query_frame=args.grid_query_frame, # 0 + backward_tracking=args.backward_tracking, # False + # segm_mask=segm_mask, + ) + print("computed") + + # save a video with predicted tracks + seq_name = os.path.splitext(args.video_path.split("/")[-1])[0] + vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3) + vis.visualize( + video, + pred_tracks, # (b, f, num_points, 2) + pred_visibility, # (b, f, num_points) + query_frame=0 if args.backward_tracking else args.grid_query_frame, + filename=seq_name, + ) diff --git a/cotracker/project/demo.py b/cotracker/project/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..2f265879f42866593756d0c01d62c442d3a6f792 --- /dev/null +++ b/cotracker/project/demo.py @@ -0,0 +1,94 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import argparse +import numpy as np + +from PIL import Image +from cotracker.utils.visualizer import Visualizer, read_video_from_path +from cotracker.predictor import CoTrackerPredictor + +# Unfortunately MPS acceleration does not support all the features we require, +# but we may be able to enable it in the future + +DEFAULT_DEVICE = ( + # "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + "cuda" + if torch.cuda.is_available() + else "cpu" +) + +# if DEFAULT_DEVICE == "mps": +# os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--video_path", + default="./assets/apple.mp4", + help="path to a video", + ) + parser.add_argument( + "--mask_path", + default="./assets/apple_mask.png", + help="path to a segmentation mask", + ) + parser.add_argument( + "--checkpoint", + # default="./checkpoints/cotracker.pth", + default=None, + help="CoTracker model parameters", + ) + parser.add_argument("--grid_size", type=int, default=10, help="Regular grid size") + parser.add_argument( + "--grid_query_frame", + type=int, + default=0, + help="Compute dense and grid tracks starting from this frame", + ) + + parser.add_argument( + "--backward_tracking", + action="store_true", + help="Compute tracks in both directions, not only forward", + ) + + args = parser.parse_args() + + # load the input video frame by frame + video = read_video_from_path(args.video_path) + video = torch.from_numpy(video).permute(0, 3, 1, 2)[None].float() + segm_mask = np.array(Image.open(os.path.join(args.mask_path))) + segm_mask = torch.from_numpy(segm_mask)[None, None] + + if args.checkpoint is not None: + model = CoTrackerPredictor(checkpoint=args.checkpoint) + else: + model = torch.hub.load("facebookresearch/co-tracker", "cotracker2") + model = model.to(DEFAULT_DEVICE) + video = video.to(DEFAULT_DEVICE) + # video = video[:, :20] + pred_tracks, pred_visibility = model( + video, + grid_size=args.grid_size, + grid_query_frame=args.grid_query_frame, + backward_tracking=args.backward_tracking, + # segm_mask=segm_mask + ) + print("computed") + + # save a video with predicted tracks + seq_name = os.path.splitext(args.video_path.split("/")[-1])[0] + vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3) + vis.visualize( + video, + pred_tracks, + pred_visibility, + query_frame=0 if args.backward_tracking else args.grid_query_frame, + filename=seq_name, + ) diff --git a/cotracker/project/docs/Makefile b/cotracker/project/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..b5d2aef658d769b18a40e127804ffd65b8ba6882 --- /dev/null +++ b/cotracker/project/docs/Makefile @@ -0,0 +1,13 @@ +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = _build +O = -a + +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/cotracker/project/docs/source/apis/models.rst b/cotracker/project/docs/source/apis/models.rst new file mode 100644 index 0000000000000000000000000000000000000000..0f1f3b9f680eebe19ad3a29bf4e62ad04efabb79 --- /dev/null +++ b/cotracker/project/docs/source/apis/models.rst @@ -0,0 +1,14 @@ +Models +====== + +CoTracker models: + +.. currentmodule:: cotracker.models + +Model Utils +----------- + +.. automodule:: cotracker.models.core.model_utils + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/cotracker/project/docs/source/apis/utils.rst b/cotracker/project/docs/source/apis/utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..00130831c916e5107b8301aa912dbf05e2d261cc --- /dev/null +++ b/cotracker/project/docs/source/apis/utils.rst @@ -0,0 +1,11 @@ +Utils +===== + +CoTracker utilizes the following utilities: + +.. currentmodule:: cotracker + +.. automodule:: cotracker.utils.visualizer + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/cotracker/project/docs/source/conf.py b/cotracker/project/docs/source/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..743bcd34604e60cb8cd2cb87bdd83c18101c93ff --- /dev/null +++ b/cotracker/project/docs/source/conf.py @@ -0,0 +1,39 @@ +__version__ = None +exec(open("../../cotracker/version.py", "r").read()) + +project = "CoTracker" +copyright = "2023-24, Meta Platforms, Inc. and affiliates" +author = "Meta Platforms" +release = __version__ + +extensions = [ + "sphinx.ext.napoleon", + "sphinx.ext.duration", + "sphinx.ext.doctest", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.intersphinx", + "sphinxcontrib.bibtex", +] + +intersphinx_mapping = { + "python": ("https://docs.python.org/3/", None), + "sphinx": ("https://www.sphinx-doc.org/en/master/", None), +} +intersphinx_disabled_domains = ["std"] + +# templates_path = ["_templates"] +html_theme = "alabaster" + +# Ignore >>> when copying code +copybutton_prompt_text = r">>> |\.\.\. " +copybutton_prompt_is_regexp = True + +# -- Options for EPUB output +epub_show_urls = "footnote" + +# typehints +autodoc_typehints = "description" + +# citations +bibtex_bibfiles = ["references.bib"] diff --git a/cotracker/project/docs/source/index.rst b/cotracker/project/docs/source/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..2f7a6d06ed2df2cff1090f4e3b8b86cafef8c181 --- /dev/null +++ b/cotracker/project/docs/source/index.rst @@ -0,0 +1,29 @@ +gsplat +=================================== + +.. image:: ../../assets/bmx-bumps.gif + :width: 800 + :alt: Example of cotracker in action + +Overview +-------- + +*CoTracker* is an open-source tracker :cite:p:`karaev2023cotracker`. + +Links +----- + +.. toctree:: + :glob: + :maxdepth: 1 + :caption: Python API + + apis/* + + +Citations +--------- + +.. bibliography:: + :style: unsrt + :filter: docname in docnames diff --git a/cotracker/project/docs/source/references.bib b/cotracker/project/docs/source/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..27e046e1a416354e47ef00bfdb22a08cbfbd4608 --- /dev/null +++ b/cotracker/project/docs/source/references.bib @@ -0,0 +1,6 @@ +@article{karaev2023cotracker, + title = {CoTracker: It is Better to Track Together}, + author = {Nikita Karaev and Ignacio Rocco and Benjamin Graham and Natalia Neverova and Andrea Vedaldi and Christian Rupprecht}, + journal = {arXiv:2307.07635}, + year = {2023} +} diff --git a/cotracker/project/gradio_demo/app.py b/cotracker/project/gradio_demo/app.py new file mode 100644 index 0000000000000000000000000000000000000000..2c59374171e578178511b9f4460075323f578e4f --- /dev/null +++ b/cotracker/project/gradio_demo/app.py @@ -0,0 +1,101 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import os +import torch +import gradio as gr + +from cotracker.utils.visualizer import Visualizer, read_video_from_path + + +def cotracker_demo( + input_video, + grid_size: int = 10, + grid_query_frame: int = 0, + tracks_leave_trace: bool = False, +): + load_video = read_video_from_path(input_video) + + grid_query_frame = min(len(load_video) - 1, grid_query_frame) + load_video = torch.from_numpy(load_video).permute(0, 3, 1, 2)[None].float() + + model = torch.hub.load("facebookresearch/co-tracker", "cotracker2_online") + + if torch.cuda.is_available(): + model = model.cuda() + load_video = load_video.cuda() + + model( + video_chunk=load_video, + is_first_step=True, + grid_size=grid_size, + grid_query_frame=grid_query_frame, + ) + for ind in range(0, load_video.shape[1] - model.step, model.step): + pred_tracks, pred_visibility = model( + video_chunk=load_video[:, ind : ind + model.step * 2] + ) # B T N 2, B T N 1 + + linewidth = 2 + if grid_size < 10: + linewidth = 4 + elif grid_size < 20: + linewidth = 3 + + vis = Visualizer( + save_dir=os.path.join(os.path.dirname(__file__), "results"), + grayscale=False, + pad_value=100, + fps=10, + linewidth=linewidth, + show_first_frame=5, + tracks_leave_trace=-1 if tracks_leave_trace else 0, + ) + import time + + def current_milli_time(): + return round(time.time() * 1000) + + filename = str(current_milli_time()) + vis.visualize( + load_video, + tracks=pred_tracks, + visibility=pred_visibility, + filename=f"{filename}_pred_track", + query_frame=grid_query_frame, + ) + return os.path.join(os.path.dirname(__file__), "results", f"{filename}_pred_track.mp4") + + +app = gr.Interface( + title="🎨 CoTracker: It is Better to Track Together", + description="
\ +

Welcome to CoTracker! This space demonstrates point (pixel) tracking in videos. \ + Points are sampled on a regular grid and are tracked jointly.

\ +

To get started, simply upload your .mp4 video in landscape orientation or click on one of the example videos to load them. The shorter the video, the faster the processing. We recommend submitting short videos of length 2-7 seconds.

\ +
    \ +
  • The total number of grid points is the square of Grid Size.
    • \ +
  • To specify the starting frame for tracking, adjust Grid Query Frame. Tracks will be visualized only after the selected frame.
    • \ +
  • Check Visualize Track Traces to visualize traces of all the tracked points.
    • \ +
\ +

For more details, check out our GitHub Repo ⭐

\ +
", + fn=cotracker_demo, + inputs=[ + gr.Video(label="Input video", interactive=True), + gr.Slider(minimum=1, maximum=30, step=1, value=10, label="Grid Size"), + gr.Slider(minimum=0, maximum=30, step=1, value=0, label="Grid Query Frame"), + gr.Checkbox(label="Visualize Track Traces"), + ], + outputs=gr.Video(label="Video with predicted tracks"), + examples=[ + ["./assets/apple.mp4", 20, 0, False, False], + ["./assets/apple.mp4", 10, 30, True, False], + ], + cache_examples=False, +) +app.launch(share=True) diff --git a/cotracker/project/hubconf.py b/cotracker/project/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..da130309d1647179d1fd85b1ddc3bf7e7d7fca42 --- /dev/null +++ b/cotracker/project/hubconf.py @@ -0,0 +1,38 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +_COTRACKER_URL = "https://huggingface.co/facebook/cotracker/resolve/main/cotracker2.pth" + + +def _make_cotracker_predictor(*, pretrained: bool = True, online=False, **kwargs): + if online: + from cotracker.predictor import CoTrackerOnlinePredictor + + predictor = CoTrackerOnlinePredictor(checkpoint=None) + else: + from cotracker.predictor import CoTrackerPredictor + + predictor = CoTrackerPredictor(checkpoint=None) + if pretrained: + state_dict = torch.hub.load_state_dict_from_url(_COTRACKER_URL, map_location="cpu") + predictor.model.load_state_dict(state_dict) + return predictor + + +def cotracker2(*, pretrained: bool = True, **kwargs): + """ + CoTracker2 with stride 4 and window length 8. Can track up to 265*265 points jointly. + """ + return _make_cotracker_predictor(pretrained=pretrained, online=False, **kwargs) + + +def cotracker2_online(*, pretrained: bool = True, **kwargs): + """ + Online CoTracker2 with stride 4 and window length 8. Can track up to 265*265 points jointly. + """ + return _make_cotracker_predictor(pretrained=pretrained, online=True, **kwargs) diff --git a/cotracker/project/launch_training.sh b/cotracker/project/launch_training.sh new file mode 100644 index 0000000000000000000000000000000000000000..555cfe38bb4657df3db2af381671d2fa5c502ccc --- /dev/null +++ b/cotracker/project/launch_training.sh @@ -0,0 +1,24 @@ +#!/bin/bash + +EXP_DIR=$1 +EXP_NAME=$2 +DATE=$3 +DATASET_ROOT=$4 +NUM_STEPS=$5 + + +echo `which python` + +mkdir -p ${EXP_DIR}/${DATE}_${EXP_NAME}/logs/; + +export PYTHONPATH=`(cd ../ && pwd)`:`pwd`:$PYTHONPATH +sbatch --comment=${EXP_NAME} --partition=learn --time=39:00:00 --gpus-per-node=8 --nodes=4 --ntasks-per-node=8 \ +--job-name=${EXP_NAME} --cpus-per-task=10 --signal=USR1@60 --open-mode=append \ +--output=${EXP_DIR}/${DATE}_${EXP_NAME}/logs/%j_%x_%A_%a_%N.out \ +--error=${EXP_DIR}/${DATE}_${EXP_NAME}/logs/%j_%x_%A_%a_%N.err \ +--wrap="srun --label python ./train.py --batch_size 1 \ +--num_steps ${NUM_STEPS} --ckpt_path ${EXP_DIR}/${DATE}_${EXP_NAME} --model_name cotracker \ +--save_freq 200 --sequence_len 24 --eval_datasets dynamic_replica tapvid_davis_first \ +--traj_per_sample 768 --sliding_window_len 8 \ +--save_every_n_epoch 10 --evaluate_every_n_epoch 10 --model_stride 4 --dataset_root ${DATASET_ROOT} --num_nodes 4 \ +--num_virtual_tracks 64" diff --git a/cotracker/project/online_demo.py b/cotracker/project/online_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f4321994e61e0e6d3c3918c2d606bece8e635d --- /dev/null +++ b/cotracker/project/online_demo.py @@ -0,0 +1,103 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import torch +import argparse +import imageio.v3 as iio +import numpy as np + +from cotracker.utils.visualizer import Visualizer +from cotracker.predictor import CoTrackerOnlinePredictor + +# Unfortunately MPS acceleration does not support all the features we require, +# but we may be able to enable it in the future + +DEFAULT_DEVICE = ( + # "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + "cuda" + if torch.cuda.is_available() + else "cpu" +) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--video_path", + default="./assets/apple.mp4", + help="path to a video", + ) + parser.add_argument( + "--checkpoint", + default=None, + help="CoTracker model parameters", + ) + parser.add_argument("--grid_size", type=int, default=10, help="Regular grid size") + parser.add_argument( + "--grid_query_frame", + type=int, + default=0, + help="Compute dense and grid tracks starting from this frame", + ) + + args = parser.parse_args() + + if not os.path.isfile(args.video_path): + raise ValueError("Video file does not exist") + + if args.checkpoint is not None: + model = CoTrackerOnlinePredictor(checkpoint=args.checkpoint) + else: + model = torch.hub.load("facebookresearch/co-tracker", "cotracker2_online") + model = model.to(DEFAULT_DEVICE) + + window_frames = [] + + def _process_step(window_frames, is_first_step, grid_size, grid_query_frame): + video_chunk = ( + torch.tensor(np.stack(window_frames[-model.step * 2 :]), device=DEFAULT_DEVICE) + .float() + .permute(0, 3, 1, 2)[None] + ) # (1, T, 3, H, W) + return model( + video_chunk, + is_first_step=is_first_step, + grid_size=grid_size, + grid_query_frame=grid_query_frame, + ) + + # Iterating over video frames, processing one window at a time: + is_first_step = True + for i, frame in enumerate( + iio.imiter( + args.video_path, + plugin="FFMPEG", + ) + ): + if i % model.step == 0 and i != 0: + pred_tracks, pred_visibility = _process_step( + window_frames, + is_first_step, + grid_size=args.grid_size, + grid_query_frame=args.grid_query_frame, + ) + is_first_step = False + window_frames.append(frame) + # Processing the final video frames in case video length is not a multiple of model.step + pred_tracks, pred_visibility = _process_step( + window_frames[-(i % model.step) - model.step - 1 :], + is_first_step, + grid_size=args.grid_size, + grid_query_frame=args.grid_query_frame, + ) + + print("Tracks are computed") + + # save a video with predicted tracks + seq_name = os.path.splitext(args.video_path.split("/")[-1])[0] + video = torch.tensor(np.stack(window_frames), device=DEFAULT_DEVICE).permute(0, 3, 1, 2)[None] + vis = Visualizer(save_dir="./saved_videos", pad_value=120, linewidth=3) + vis.visualize(video, pred_tracks, pred_visibility, query_frame=args.grid_query_frame, filename=seq_name) diff --git a/cotracker/project/tests/test_bilinear_sample.py b/cotracker/project/tests/test_bilinear_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..29e5322a0f007a5c4f23f38e509092b8f00e9714 --- /dev/null +++ b/cotracker/project/tests/test_bilinear_sample.py @@ -0,0 +1,51 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +import unittest + +from cotracker.models.core.model_utils import bilinear_sampler + + +class TestBilinearSampler(unittest.TestCase): + # Sample from an image (4d) + def _test4d(self, align_corners): + H, W = 4, 5 + # Construct a grid to obtain indentity sampling + input = torch.randn(H * W).view(1, 1, H, W).float() + coords = torch.meshgrid(torch.arange(H), torch.arange(W)) + coords = torch.stack(coords[::-1], dim=-1).float()[None] + if not align_corners: + coords = coords + 0.5 + sampled_input = bilinear_sampler(input, coords, align_corners=align_corners) + torch.testing.assert_close(input, sampled_input) + + # Sample from a video (5d) + def _test5d(self, align_corners): + T, H, W = 3, 4, 5 + # Construct a grid to obtain indentity sampling + input = torch.randn(H * W).view(1, 1, H, W).float() + input = torch.stack([input, input + 1, input + 2], dim=2) + coords = torch.meshgrid(torch.arange(T), torch.arange(W), torch.arange(H)) + coords = torch.stack(coords, dim=-1).float().permute(0, 2, 1, 3)[None] + + if not align_corners: + coords = coords + 0.5 + sampled_input = bilinear_sampler(input, coords, align_corners=align_corners) + torch.testing.assert_close(input, sampled_input) + + def test4d(self): + self._test4d(align_corners=True) + self._test4d(align_corners=False) + + def test5d(self): + self._test5d(align_corners=True) + self._test5d(align_corners=False) + + +# run the test +unittest.main() diff --git a/cotracker/project/train.py b/cotracker/project/train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2b354f117a825fb66bc88abd5a1cb53d0cd3e60 --- /dev/null +++ b/cotracker/project/train.py @@ -0,0 +1,618 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import random +import torch +import signal +import socket +import sys +import json + +import numpy as np +import argparse +import logging +from pathlib import Path +from tqdm import tqdm +import torch.optim as optim +from torch.utils.data import DataLoader +from torch.cuda.amp import GradScaler + +from torch.utils.tensorboard import SummaryWriter +from pytorch_lightning.lite import LightningLite + +from cotracker.models.evaluation_predictor import EvaluationPredictor +from cotracker.models.core.cotracker.cotracker import CoTracker2 +from cotracker.utils.visualizer import Visualizer +from cotracker.datasets.tap_vid_datasets import TapVidDataset + +from cotracker.datasets.dr_dataset import DynamicReplicaDataset +from cotracker.evaluation.core.evaluator import Evaluator +from cotracker.datasets import kubric_movif_dataset +from cotracker.datasets.utils import collate_fn, collate_fn_train, dataclass_to_cuda_ +from cotracker.models.core.cotracker.losses import sequence_loss, balanced_ce_loss + + +# define the handler function +# for training on a slurm cluster +def sig_handler(signum, frame): + print("caught signal", signum) + print(socket.gethostname(), "USR1 signal caught.") + # do other stuff to cleanup here + print("requeuing job " + os.environ["SLURM_JOB_ID"]) + os.system("scontrol requeue " + os.environ["SLURM_JOB_ID"]) + sys.exit(-1) + + +def term_handler(signum, frame): + print("bypassing sigterm", flush=True) + + +def fetch_optimizer(args, model): + """Create the optimizer and learning rate scheduler""" + optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=1e-8) + scheduler = optim.lr_scheduler.OneCycleLR( + optimizer, + args.lr, + args.num_steps + 100, + pct_start=0.05, + cycle_momentum=False, + anneal_strategy="linear", + ) + + return optimizer, scheduler + + +def forward_batch(batch, model, args): + video = batch.video + trajs_g = batch.trajectory + vis_g = batch.visibility + valids = batch.valid + B, T, C, H, W = video.shape + assert C == 3 + B, T, N, D = trajs_g.shape + device = video.device + + __, first_positive_inds = torch.max(vis_g, dim=1) + # We want to make sure that during training the model sees visible points + # that it does not need to track just yet: they are visible but queried from a later frame + N_rand = N // 4 + # inds of visible points in the 1st frame + nonzero_inds = [[torch.nonzero(vis_g[b, :, i]) for i in range(N)] for b in range(B)] + + for b in range(B): + rand_vis_inds = torch.cat( + [ + nonzero_row[torch.randint(len(nonzero_row), size=(1,))] + for nonzero_row in nonzero_inds[b] + ], + dim=1, + ) + first_positive_inds[b] = torch.cat( + [rand_vis_inds[:, :N_rand], first_positive_inds[b : b + 1, N_rand:]], dim=1 + ) + + ind_array_ = torch.arange(T, device=device) + ind_array_ = ind_array_[None, :, None].repeat(B, 1, N) + assert torch.allclose( + vis_g[ind_array_ == first_positive_inds[:, None, :]], + torch.ones(1, device=device), + ) + gather = torch.gather(trajs_g, 1, first_positive_inds[:, :, None, None].repeat(1, 1, N, D)) + xys = torch.diagonal(gather, dim1=1, dim2=2).permute(0, 2, 1) + + queries = torch.cat([first_positive_inds[:, :, None], xys[:, :, :2]], dim=2) + + predictions, visibility, train_data = model( + video=video, queries=queries, iters=args.train_iters, is_train=True + ) + coord_predictions, vis_predictions, valid_mask = train_data + + vis_gts = [] + traj_gts = [] + valids_gts = [] + + S = args.sliding_window_len + for ind in range(0, args.sequence_len - S // 2, S // 2): + vis_gts.append(vis_g[:, ind : ind + S]) + traj_gts.append(trajs_g[:, ind : ind + S]) + valids_gts.append(valids[:, ind : ind + S] * valid_mask[:, ind : ind + S]) + + seq_loss = sequence_loss(coord_predictions, traj_gts, vis_gts, valids_gts, 0.8) + vis_loss = balanced_ce_loss(vis_predictions, vis_gts, valids_gts) + + output = {"flow": {"predictions": predictions[0].detach()}} + output["flow"]["loss"] = seq_loss.mean() + output["visibility"] = { + "loss": vis_loss.mean() * 10.0, + "predictions": visibility[0].detach(), + } + return output + + +def run_test_eval(evaluator, model, dataloaders, writer, step): + model.eval() + for ds_name, dataloader in dataloaders: + visualize_every = 1 + grid_size = 5 + if ds_name == "dynamic_replica": + visualize_every = 8 + grid_size = 0 + elif "tapvid" in ds_name: + visualize_every = 5 + + predictor = EvaluationPredictor( + model.module.module, + grid_size=grid_size, + local_grid_size=0, + single_point=False, + n_iters=6, + ) + if torch.cuda.is_available(): + predictor.model = predictor.model.cuda() + + metrics = evaluator.evaluate_sequence( + model=predictor, + test_dataloader=dataloader, + dataset_name=ds_name, + train_mode=True, + writer=writer, + step=step, + visualize_every=visualize_every, + ) + + if ds_name == "dynamic_replica" or ds_name == "kubric": + metrics = {f"{ds_name}_avg_{k}": v for k, v in metrics["avg"].items()} + + if "tapvid" in ds_name: + metrics = { + f"{ds_name}_avg_OA": metrics["avg"]["occlusion_accuracy"], + f"{ds_name}_avg_delta": metrics["avg"]["average_pts_within_thresh"], + f"{ds_name}_avg_Jaccard": metrics["avg"]["average_jaccard"], + } + + writer.add_scalars(f"Eval_{ds_name}", metrics, step) + + +class Logger: + SUM_FREQ = 100 + + def __init__(self, model, scheduler): + self.model = model + self.scheduler = scheduler + self.total_steps = 0 + self.running_loss = {} + self.writer = SummaryWriter(log_dir=os.path.join(args.ckpt_path, "runs")) + + def _print_training_status(self): + metrics_data = [ + self.running_loss[k] / Logger.SUM_FREQ for k in sorted(self.running_loss.keys()) + ] + training_str = "[{:6d}] ".format(self.total_steps + 1) + metrics_str = ("{:10.4f}, " * len(metrics_data)).format(*metrics_data) + + # print the training status + logging.info(f"Training Metrics ({self.total_steps}): {training_str + metrics_str}") + + if self.writer is None: + self.writer = SummaryWriter(log_dir=os.path.join(args.ckpt_path, "runs")) + + for k in self.running_loss: + self.writer.add_scalar(k, self.running_loss[k] / Logger.SUM_FREQ, self.total_steps) + self.running_loss[k] = 0.0 + + def push(self, metrics, task): + self.total_steps += 1 + + for key in metrics: + task_key = str(key) + "_" + task + if task_key not in self.running_loss: + self.running_loss[task_key] = 0.0 + + self.running_loss[task_key] += metrics[key] + + if self.total_steps % Logger.SUM_FREQ == Logger.SUM_FREQ - 1: + self._print_training_status() + self.running_loss = {} + + def write_dict(self, results): + if self.writer is None: + self.writer = SummaryWriter(log_dir=os.path.join(args.ckpt_path, "runs")) + + for key in results: + self.writer.add_scalar(key, results[key], self.total_steps) + + def close(self): + self.writer.close() + + +class Lite(LightningLite): + def run(self, args): + def seed_everything(seed: int): + random.seed(seed) + os.environ["PYTHONHASHSEED"] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + seed_everything(0) + + def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + g = torch.Generator() + g.manual_seed(0) + if self.global_rank == 0: + eval_dataloaders = [] + if "dynamic_replica" in args.eval_datasets: + eval_dataset = DynamicReplicaDataset( + sample_len=60, only_first_n_samples=1, rgbd_input=False + ) + eval_dataloader_dr = torch.utils.data.DataLoader( + eval_dataset, + batch_size=1, + shuffle=False, + num_workers=1, + collate_fn=collate_fn, + ) + eval_dataloaders.append(("dynamic_replica", eval_dataloader_dr)) + + if "tapvid_davis_first" in args.eval_datasets: + data_root = os.path.join(args.dataset_root, "tapvid/tapvid_davis/tapvid_davis.pkl") + eval_dataset = TapVidDataset(dataset_type="davis", data_root=data_root) + eval_dataloader_tapvid_davis = torch.utils.data.DataLoader( + eval_dataset, + batch_size=1, + shuffle=False, + num_workers=1, + collate_fn=collate_fn, + ) + eval_dataloaders.append(("tapvid_davis", eval_dataloader_tapvid_davis)) + + evaluator = Evaluator(args.ckpt_path) + + visualizer = Visualizer( + save_dir=args.ckpt_path, + pad_value=80, + fps=1, + show_first_frame=0, + tracks_leave_trace=0, + ) + + if args.model_name == "cotracker": + model = CoTracker2( + stride=args.model_stride, + window_len=args.sliding_window_len, + add_space_attn=not args.remove_space_attn, + num_virtual_tracks=args.num_virtual_tracks, + model_resolution=args.crop_size, + ) + else: + raise ValueError(f"Model {args.model_name} doesn't exist") + + with open(args.ckpt_path + "/meta.json", "w") as file: + json.dump(vars(args), file, sort_keys=True, indent=4) + + model.cuda() + + train_dataset = kubric_movif_dataset.KubricMovifDataset( + data_root=os.path.join(args.dataset_root, "kubric", "kubric_movi_f_tracks"), + crop_size=args.crop_size, + seq_len=args.sequence_len, + traj_per_sample=args.traj_per_sample, + sample_vis_1st_frame=args.sample_vis_1st_frame, + use_augs=not args.dont_use_augs, + ) + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + shuffle=True, + num_workers=args.num_workers, + worker_init_fn=seed_worker, + generator=g, + pin_memory=True, + collate_fn=collate_fn_train, + drop_last=True, + ) + + train_loader = self.setup_dataloaders(train_loader, move_to_device=False) + print("LEN TRAIN LOADER", len(train_loader)) + optimizer, scheduler = fetch_optimizer(args, model) + + total_steps = 0 + if self.global_rank == 0: + logger = Logger(model, scheduler) + + folder_ckpts = [ + f + for f in os.listdir(args.ckpt_path) + if not os.path.isdir(f) and f.endswith(".pth") and not "final" in f + ] + if len(folder_ckpts) > 0: + ckpt_path = sorted(folder_ckpts)[-1] + ckpt = self.load(os.path.join(args.ckpt_path, ckpt_path)) + logging.info(f"Loading checkpoint {ckpt_path}") + if "model" in ckpt: + model.load_state_dict(ckpt["model"]) + else: + model.load_state_dict(ckpt) + if "optimizer" in ckpt: + logging.info("Load optimizer") + optimizer.load_state_dict(ckpt["optimizer"]) + if "scheduler" in ckpt: + logging.info("Load scheduler") + scheduler.load_state_dict(ckpt["scheduler"]) + if "total_steps" in ckpt: + total_steps = ckpt["total_steps"] + logging.info(f"Load total_steps {total_steps}") + + elif args.restore_ckpt is not None: + assert args.restore_ckpt.endswith(".pth") or args.restore_ckpt.endswith(".pt") + logging.info("Loading checkpoint...") + + strict = True + state_dict = self.load(args.restore_ckpt) + if "model" in state_dict: + state_dict = state_dict["model"] + + if list(state_dict.keys())[0].startswith("module."): + state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()} + model.load_state_dict(state_dict, strict=strict) + + logging.info(f"Done loading checkpoint") + model, optimizer = self.setup(model, optimizer, move_to_device=False) + # model.cuda() + model.train() + + save_freq = args.save_freq + scaler = GradScaler(enabled=args.mixed_precision) + + should_keep_training = True + global_batch_num = 0 + epoch = -1 + + while should_keep_training: + epoch += 1 + for i_batch, batch in enumerate(tqdm(train_loader)): + batch, gotit = batch + if not all(gotit): + print("batch is None") + continue + dataclass_to_cuda_(batch) + + optimizer.zero_grad() + + assert model.training + + output = forward_batch(batch, model, args) + + loss = 0 + for k, v in output.items(): + if "loss" in v: + loss += v["loss"] + + if self.global_rank == 0: + for k, v in output.items(): + if "loss" in v: + logger.writer.add_scalar( + f"live_{k}_loss", v["loss"].item(), total_steps + ) + if "metrics" in v: + logger.push(v["metrics"], k) + if total_steps % save_freq == save_freq - 1: + visualizer.visualize( + video=batch.video.clone(), + tracks=batch.trajectory.clone(), + filename="train_gt_traj", + writer=logger.writer, + step=total_steps, + ) + + visualizer.visualize( + video=batch.video.clone(), + tracks=output["flow"]["predictions"][None], + filename="train_pred_traj", + writer=logger.writer, + step=total_steps, + ) + + if len(output) > 1: + logger.writer.add_scalar(f"live_total_loss", loss.item(), total_steps) + logger.writer.add_scalar( + f"learning_rate", optimizer.param_groups[0]["lr"], total_steps + ) + global_batch_num += 1 + + self.barrier() + + self.backward(scaler.scale(loss)) + + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(model.parameters(), 10.0) + + scaler.step(optimizer) + scheduler.step() + scaler.update() + total_steps += 1 + if self.global_rank == 0: + if (i_batch >= len(train_loader) - 1) or ( + total_steps == 1 and args.validate_at_start + ): + if (epoch + 1) % args.save_every_n_epoch == 0: + ckpt_iter = "0" * (6 - len(str(total_steps))) + str(total_steps) + save_path = Path( + f"{args.ckpt_path}/model_{args.model_name}_{ckpt_iter}.pth" + ) + + save_dict = { + "model": model.module.module.state_dict(), + "optimizer": optimizer.state_dict(), + "scheduler": scheduler.state_dict(), + "total_steps": total_steps, + } + + logging.info(f"Saving file {save_path}") + self.save(save_dict, save_path) + + if (epoch + 1) % args.evaluate_every_n_epoch == 0 or ( + args.validate_at_start and epoch == 0 + ): + run_test_eval( + evaluator, + model, + eval_dataloaders, + logger.writer, + total_steps, + ) + model.train() + torch.cuda.empty_cache() + + self.barrier() + if total_steps > args.num_steps: + should_keep_training = False + break + if self.global_rank == 0: + print("FINISHED TRAINING") + + PATH = f"{args.ckpt_path}/{args.model_name}_final.pth" + torch.save(model.module.module.state_dict(), PATH) + run_test_eval(evaluator, model, eval_dataloaders, logger.writer, total_steps) + logger.close() + + +if __name__ == "__main__": + signal.signal(signal.SIGUSR1, sig_handler) + signal.signal(signal.SIGTERM, term_handler) + parser = argparse.ArgumentParser() + parser.add_argument("--model_name", default="cotracker", help="model name") + parser.add_argument("--restore_ckpt", help="path to restore a checkpoint") + parser.add_argument("--ckpt_path", help="path to save checkpoints") + parser.add_argument( + "--batch_size", type=int, default=4, help="batch size used during training." + ) + parser.add_argument("--num_nodes", type=int, default=1) + parser.add_argument("--num_workers", type=int, default=10, help="number of dataloader workers") + + parser.add_argument("--mixed_precision", action="store_true", help="use mixed precision") + parser.add_argument("--lr", type=float, default=0.0005, help="max learning rate.") + parser.add_argument("--wdecay", type=float, default=0.00001, help="Weight decay in optimizer.") + parser.add_argument( + "--num_steps", type=int, default=200000, help="length of training schedule." + ) + parser.add_argument( + "--evaluate_every_n_epoch", + type=int, + default=1, + help="evaluate during training after every n epochs, after every epoch by default", + ) + parser.add_argument( + "--save_every_n_epoch", + type=int, + default=1, + help="save checkpoints during training after every n epochs, after every epoch by default", + ) + parser.add_argument( + "--validate_at_start", + action="store_true", + help="whether to run evaluation before training starts", + ) + parser.add_argument( + "--save_freq", + type=int, + default=100, + help="frequency of trajectory visualization during training", + ) + parser.add_argument( + "--traj_per_sample", + type=int, + default=768, + help="the number of trajectories to sample for training", + ) + parser.add_argument( + "--dataset_root", type=str, help="path lo all the datasets (train and eval)" + ) + + parser.add_argument( + "--train_iters", + type=int, + default=4, + help="number of updates to the disparity field in each forward pass.", + ) + parser.add_argument("--sequence_len", type=int, default=8, help="train sequence length") + parser.add_argument( + "--eval_datasets", + nargs="+", + default=["tapvid_davis_first"], + help="what datasets to use for evaluation", + ) + + parser.add_argument( + "--remove_space_attn", + action="store_true", + help="remove space attention from CoTracker", + ) + parser.add_argument( + "--num_virtual_tracks", + type=int, + default=None, + help="stride of the CoTracker feature network", + ) + parser.add_argument( + "--dont_use_augs", + action="store_true", + help="don't apply augmentations during training", + ) + parser.add_argument( + "--sample_vis_1st_frame", + action="store_true", + help="only sample trajectories with points visible on the first frame", + ) + parser.add_argument( + "--sliding_window_len", + type=int, + default=8, + help="length of the CoTracker sliding window", + ) + parser.add_argument( + "--model_stride", + type=int, + default=8, + help="stride of the CoTracker feature network", + ) + parser.add_argument( + "--crop_size", + type=int, + nargs="+", + default=[384, 512], + help="crop videos to this resolution during training", + ) + parser.add_argument( + "--eval_max_seq_len", + type=int, + default=1000, + help="maximum length of evaluation videos", + ) + args = parser.parse_args() + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s", + ) + + Path(args.ckpt_path).mkdir(exist_ok=True, parents=True) + from pytorch_lightning.strategies import DDPStrategy + + Lite( + strategy=DDPStrategy(find_unused_parameters=False), + devices="auto", + accelerator="gpu", + precision=32, + num_nodes=args.num_nodes, + ).run(args) diff --git a/cotracker/setup.py b/cotracker/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..c67b1e1de5d42c7ff97379c5ec0bb0cee993e93a --- /dev/null +++ b/cotracker/setup.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from setuptools import find_packages, setup + +setup( + name="cotracker", + version="2.0", + install_requires=[], + packages=find_packages(exclude="notebooks"), + extras_require={ + "all": ["matplotlib"], + "dev": ["flake8", "black"], + }, +) diff --git a/cotracker/utils/__init__.py b/cotracker/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5277f46157403e47fd830fc519144b97ef69d4ae --- /dev/null +++ b/cotracker/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/cotracker/utils/visualizer.py b/cotracker/utils/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..c5e78615242c20d192faab616c702f629dcef5b8 --- /dev/null +++ b/cotracker/utils/visualizer.py @@ -0,0 +1,375 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import os +import numpy as np +import imageio +import torch + +from matplotlib import cm +import torch.nn.functional as F +import torchvision.transforms as transforms +import matplotlib.pyplot as plt +from PIL import Image, ImageDraw +# import av +# import decord +import torchvision +from einops import rearrange + + +def read_video_from_path(path): + # try: + # reader = imageio.get_reader(path) + # except Exception as e: + # print("Error opening video file: ", e) + # return None + # frames = [] + # for i, im in enumerate(reader): + # frames.append(np.array(im)) + # return np.stack(frames) + + # # read videe using decord + # video = decord.VideoReader(path) + # frames = video.get_batch(range(len(video))) + # frames = [frame.asnumpy() for frame in frames] + # return np.stack(frames) + + # read video using torchvision + vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='THWC') + vframes = vframes.numpy() + return vframes + + + +def draw_circle(rgb, coord, radius, color=(255, 0, 0), visible=True): + # Create a draw object + draw = ImageDraw.Draw(rgb) + # Calculate the bounding box of the circle + left_up_point = (coord[0] - radius, coord[1] - radius) + right_down_point = (coord[0] + radius, coord[1] + radius) + # Draw the circle + draw.ellipse( + [left_up_point, right_down_point], + fill=tuple(color) if visible else None, + outline=tuple(color), + ) + return rgb + + +def draw_line(rgb, coord_y, coord_x, color, linewidth): + draw = ImageDraw.Draw(rgb) + draw.line( + (coord_y[0], coord_y[1], coord_x[0], coord_x[1]), + fill=tuple(color), + width=linewidth, + ) + return rgb + + +def add_weighted(rgb, alpha, original, beta, gamma): + return (rgb * alpha + original * beta + gamma).astype("uint8") + + +class Visualizer: + def __init__( + self, + save_dir: str = "./results", + grayscale: bool = False, + pad_value: int = 0, + fps: int = 10, + mode: str = "rainbow", # 'cool', 'optical_flow' + linewidth: int = 2, + show_first_frame: int = 10, + tracks_leave_trace: int = 0, # -1 for infinite + ): + self.mode = mode + self.save_dir = save_dir + if mode == "rainbow": + self.color_map = cm.get_cmap("gist_rainbow") + elif mode == "cool": + self.color_map = cm.get_cmap(mode) + self.show_first_frame = show_first_frame + self.grayscale = grayscale + self.tracks_leave_trace = tracks_leave_trace + self.pad_value = pad_value + self.linewidth = linewidth + self.fps = fps + + def visualize( + self, + video: torch.Tensor, # (B,T,C,H,W) + tracks: torch.Tensor, # (B,T,N,2) + visibility: torch.Tensor = None, # (B, T, N, 1) bool + gt_tracks: torch.Tensor = None, # (B,T,N,2) + segm_mask: torch.Tensor = None, # (B,1,H,W) + filename: str = "video", + writer=None, # tensorboard Summary Writer, used for visualization during training + step: int = 0, + query_frame: int = 0, + save_video: bool = True, + compensate_for_camera_motion: bool = False, + ): + if compensate_for_camera_motion: + assert segm_mask is not None + if segm_mask is not None: + coords = tracks[0, query_frame].round().long() + segm_mask = segm_mask[0, query_frame][coords[:, 1], coords[:, 0]].long() + + video = F.pad( + video, + (self.pad_value, self.pad_value, self.pad_value, self.pad_value), + "constant", + 255, + ) + tracks = tracks + self.pad_value + + if self.grayscale: + transform = transforms.Grayscale() + video = transform(video) + video = video.repeat(1, 1, 3, 1, 1) + + res_video = self.draw_tracks_on_video( + video=video, + tracks=tracks, + visibility=visibility, + segm_mask=segm_mask, + gt_tracks=gt_tracks, + query_frame=query_frame, + compensate_for_camera_motion=compensate_for_camera_motion, + ) + if save_video: + self.save_video(res_video, filename=filename, writer=writer, step=step) + return res_video + + def save_video(self, video, filename, writer=None, step=0): + if writer is not None: + writer.add_video( + filename, + video.to(torch.uint8), + global_step=step, + fps=self.fps, + ) + else: + os.makedirs(self.save_dir, exist_ok=True) + + # Prepare the video file path + save_path = os.path.join(self.save_dir, f"{filename}.mp4") + # save video using torchvision + assert video.shape[0] == 1 + video = rearrange(video[0], 'T C H W -> T H W C') + torchvision.io.write_video(save_path, video, fps=self.fps) + + # wide_list = list(video.unbind(1)) + # wide_list = [wide[0].permute(1, 2, 0).cpu().numpy() for wide in wide_list] + + # # Create a writer object + # video_writer = imageio.get_writer(save_path, fps=self.fps) + + # # Write frames to the video file + # for frame in wide_list[2:-1]: + # video_writer.append_data(frame) + + # video_writer.close() + + # # pyav + # container = av.open(save_path, mode="w") + # stream = container.add_stream("h264", rate=self.fps) + # for frame in wide_list[2:-1]: + # frame = Image.fromarray(frame) + # frame = np.array(frame) + # frame = av.VideoFrame.from_ndarray(frame, format="rgb24") + # for packet in stream.encode(frame): + # container.mux(packet) + + print(f"Video saved to {save_path}") + + def draw_tracks_on_video( + self, + video: torch.Tensor, + tracks: torch.Tensor, + visibility: torch.Tensor = None, + segm_mask: torch.Tensor = None, + gt_tracks=None, + query_frame: int = 0, + compensate_for_camera_motion=False, + ): + B, T, C, H, W = video.shape + _, _, N, D = tracks.shape + + assert D == 2 + assert C == 3 + video = video[0].permute(0, 2, 3, 1).byte().detach().cpu().numpy() # S, H, W, C + tracks = tracks[0].long().detach().cpu().numpy() # S, N, 2 + if gt_tracks is not None: + gt_tracks = gt_tracks[0].detach().cpu().numpy() + + res_video = [] + + # process input video + for rgb in video: + res_video.append(rgb.copy()) + vector_colors = np.zeros((T, N, 3)) + + # define vector colors + if self.mode == "optical_flow": + import flow_vis + + vector_colors = flow_vis.flow_to_color(tracks - tracks[query_frame][None]) + elif segm_mask is None: + if self.mode == "rainbow": + y_min, y_max = ( + tracks[query_frame, :, 1].min(), + tracks[query_frame, :, 1].max(), + ) + norm = plt.Normalize(y_min, y_max) + for n in range(N): + color = self.color_map(norm(tracks[query_frame, n, 1])) + color = np.array(color[:3])[None] * 255 + vector_colors[:, n] = np.repeat(color, T, axis=0) + else: + # color changes with time + for t in range(T): + color = np.array(self.color_map(t / T)[:3])[None] * 255 + vector_colors[t] = np.repeat(color, N, axis=0) + else: + if self.mode == "rainbow": + vector_colors[:, segm_mask <= 0, :] = 255 + + y_min, y_max = ( + tracks[0, segm_mask > 0, 1].min(), + tracks[0, segm_mask > 0, 1].max(), + ) + norm = plt.Normalize(y_min, y_max) + for n in range(N): + if segm_mask[n] > 0: + color = self.color_map(norm(tracks[0, n, 1])) + color = np.array(color[:3])[None] * 255 + vector_colors[:, n] = np.repeat(color, T, axis=0) + + else: + # color changes with segm class + segm_mask = segm_mask.cpu() + color = np.zeros((segm_mask.shape[0], 3), dtype=np.float32) + color[segm_mask > 0] = np.array(self.color_map(1.0)[:3]) * 255.0 + color[segm_mask <= 0] = np.array(self.color_map(0.0)[:3]) * 255.0 + vector_colors = np.repeat(color[None], T, axis=0) + + # draw tracks + if self.tracks_leave_trace != 0: + for t in range(query_frame + 1, T): + first_ind = ( + max(0, t - self.tracks_leave_trace) if self.tracks_leave_trace >= 0 else 0 + ) + curr_tracks = tracks[first_ind : t + 1] + curr_colors = vector_colors[first_ind : t + 1] + if compensate_for_camera_motion: + diff = ( + tracks[first_ind : t + 1, segm_mask <= 0] + - tracks[t : t + 1, segm_mask <= 0] + ).mean(1)[:, None] + + curr_tracks = curr_tracks - diff + curr_tracks = curr_tracks[:, segm_mask > 0] + curr_colors = curr_colors[:, segm_mask > 0] + + res_video[t] = self._draw_pred_tracks( + res_video[t], + curr_tracks, + curr_colors, + ) + if gt_tracks is not None: + res_video[t] = self._draw_gt_tracks(res_video[t], gt_tracks[first_ind : t + 1]) + + # draw points + for t in range(query_frame, T): + img = Image.fromarray(np.uint8(res_video[t])) + for i in range(N): + coord = (tracks[t, i, 0], tracks[t, i, 1]) + visibile = True + if visibility is not None: + visibile = visibility[0, t, i] + if coord[0] != 0 and coord[1] != 0: + if not compensate_for_camera_motion or ( + compensate_for_camera_motion and segm_mask[i] > 0 + ): + img = draw_circle( + img, + coord=coord, + radius=int(self.linewidth * 2), + color=vector_colors[t, i].astype(int), + visible=visibile, + ) + res_video[t] = np.array(img) + + # construct the final rgb sequence + if self.show_first_frame > 0: + res_video = [res_video[0]] * self.show_first_frame + res_video[1:] + return torch.from_numpy(np.stack(res_video)).permute(0, 3, 1, 2)[None].byte() + + def _draw_pred_tracks( + self, + rgb: np.ndarray, # H x W x 3 + tracks: np.ndarray, # T x 2 + vector_colors: np.ndarray, + alpha: float = 0.5, + ): + T, N, _ = tracks.shape + rgb = Image.fromarray(np.uint8(rgb)) + for s in range(T - 1): + vector_color = vector_colors[s] + original = rgb.copy() + alpha = (s / T) ** 2 + for i in range(N): + coord_y = (int(tracks[s, i, 0]), int(tracks[s, i, 1])) + coord_x = (int(tracks[s + 1, i, 0]), int(tracks[s + 1, i, 1])) + if coord_y[0] != 0 and coord_y[1] != 0: + rgb = draw_line( + rgb, + coord_y, + coord_x, + vector_color[i].astype(int), + self.linewidth, + ) + if self.tracks_leave_trace > 0: + rgb = Image.fromarray( + np.uint8(add_weighted(np.array(rgb), alpha, np.array(original), 1 - alpha, 0)) + ) + rgb = np.array(rgb) + return rgb + + def _draw_gt_tracks( + self, + rgb: np.ndarray, # H x W x 3, + gt_tracks: np.ndarray, # T x 2 + ): + T, N, _ = gt_tracks.shape + color = np.array((211, 0, 0)) + rgb = Image.fromarray(np.uint8(rgb)) + for t in range(T): + for i in range(N): + gt_tracks = gt_tracks[t][i] + # draw a red cross + if gt_tracks[0] > 0 and gt_tracks[1] > 0: + length = self.linewidth * 3 + coord_y = (int(gt_tracks[0]) + length, int(gt_tracks[1]) + length) + coord_x = (int(gt_tracks[0]) - length, int(gt_tracks[1]) - length) + rgb = draw_line( + rgb, + coord_y, + coord_x, + color, + self.linewidth, + ) + coord_y = (int(gt_tracks[0]) - length, int(gt_tracks[1]) + length) + coord_x = (int(gt_tracks[0]) + length, int(gt_tracks[1]) - length) + rgb = draw_line( + rgb, + coord_y, + coord_x, + color, + self.linewidth, + ) + rgb = np.array(rgb) + return rgb diff --git a/cotracker/version.py b/cotracker/version.py new file mode 100644 index 0000000000000000000000000000000000000000..4bdf9b49a56185f1ee87988877b5b3f1d2c36794 --- /dev/null +++ b/cotracker/version.py @@ -0,0 +1,8 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +__version__ = "2.0.0" diff --git a/data_test/sample1.mp4 b/data_test/sample1.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..f08fcf6130c509f6ea1feb1a9830b515a39a9317 Binary files /dev/null and b/data_test/sample1.mp4 differ diff --git a/data_test/sample1.png b/data_test/sample1.png new file mode 100644 index 0000000000000000000000000000000000000000..fbaa619e45b1dd5a8bd65d3fea0d663fb105aab9 Binary files /dev/null and b/data_test/sample1.png differ diff --git a/data_test/sample2.mp4 b/data_test/sample2.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..2d1955bd490e3c792cf6c8dde5a772a13793f5af Binary files /dev/null and b/data_test/sample2.mp4 differ diff --git a/data_test/sample2.png b/data_test/sample2.png new file mode 100644 index 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files /dev/null and b/data_test/sample4.png differ diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ec96570f85ede7af7ec2689e64fc3d4e3c8f30be --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1,152 @@ +from torchvision import transforms +from datasets import video_transforms +from .ucf101_datasets import UCF101 +from .dummy_datasets import DummyDataset +from .webvid_datasets import WebVid10M +from .videoswap_datasets import VideoSwapDataset +from .dl3dv_datasets import DL3DVDataset +from .pair_datasets import PairDataset +from .metric_datasets import MetricDataset +from .sakuga_ref_datasets import SakugaRefDataset + +def get_dataset(args): + if args.dataset not in ["encdec_images", "pair_dataset"]: + temporal_sample = video_transforms.TemporalRandomCrop(args.num_frames * args.frame_interval) # 16 1 + if args.dataset == 'sakuga_ref': + temporal_sample = video_transforms.TemporalRandomCrop(args.num_frames * args.frame_interval+args.ref_jump_frames) # 16 1 + if args.dataset == 'ucf101': + transform_ucf101 = transforms.Compose([ + video_transforms.ToTensorVideo(), # TCHW + video_transforms.RandomHorizontalFlipVideo(), + video_transforms.UCFCenterCropVideo(args.image_size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + ]) + dataset = UCF101(args, transform=transform_ucf101, temporal_sample=temporal_sample) + return dataset + + elif args.dataset == 'dummy': + size = (args.height, args.width) + transform = transforms.Compose([ + video_transforms.ToTensorVideo(), # TCHW + # video_transforms.RandomHorizontalFlipVideo(), # NOTE + video_transforms.UCFCenterCropVideo(size=size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + ]) + + dataset = DummyDataset( + sample_frames=args.num_frames, + base_folder=args.base_folder, + temporal_sample=temporal_sample, + transform=transform, + seed=args.seed, + file_list=args.file_list, + ) + return dataset + elif args.dataset == 'sakuga_ref': + size = (args.height, args.width) + transform = transforms.Compose([ + video_transforms.ToTensorVideo(), # TCHW + # video_transforms.RandomHorizontalFlipVideo(), # NOTE + video_transforms.UCFCenterCropVideo(size=size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + ]) + + dataset = SakugaRefDataset( + video_frames=args.num_frames, + ref_jump_frames=args.ref_jump_frames, + base_folder=args.base_folder, + temporal_sample=temporal_sample, + transform=transform, + seed=args.seed, + file_list=args.file_list, + ) + return dataset + elif args.dataset == 'webvid': + size = (args.height, args.width) + transform = transforms.Compose([ + video_transforms.ToTensorVideo(), # TCHW + # video_transforms.RandomHorizontalFlipVideo(), # NOTE + video_transforms.UCFCenterCropVideo(size=size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + ]) + + dataset = WebVid10M( + sample_frames=args.num_frames, + base_folder=args.base_folder, + temporal_sample=temporal_sample, + transform=transform, + seed=args.seed, + ) + return dataset + + elif args.dataset == 'videoswap': + size = (args.height, args.width) + transform = transforms.Compose([ + video_transforms.ToTensorVideo(), # TCHW + # video_transforms.RandomHorizontalFlipVideo(), + # video_transforms.UCFCenterCropVideo(size=size), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + ]) + + dataset = VideoSwapDataset( + width=args.width, + height=args.height, + sample_frames=args.num_frames, + base_folder=args.base_folder, + temporal_sample=temporal_sample, + transform=transform, + seed=args.seed + ) + return dataset + + elif args.dataset == 'dl3dv': + size = (args.height, args.width) + # transform = transforms.Compose([ + # video_transforms.ToTensorVideo(), # TCHW + # # video_transforms.RandomHorizontalFlipVideo(), + # # video_transforms.UCFCenterCropVideo(size=size), + # transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + # ]) + + dataset = DL3DVDataset( + width=args.width, + height=args.height, + sample_frames=args.num_frames, + base_folder=args.base_folder, + file_list=args.file_list, + temporal_sample=temporal_sample, + # transform=transform, + seed=args.seed, + ) + return dataset + + elif args.dataset == "pair_dataset": + # size = (args.height, args.width) + # transform = transforms.Compose([ + # video_transforms.ToTensorVideo(), # TCHW + # # video_transforms.RandomHorizontalFlipVideo(), + # video_transforms.UCFCenterCropVideo(size=size), + # # transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=False) + # ]) + + dataset = PairDataset( + # width=args.width, + # height=args.height, + # sample_frames=args.num_frames, + base_folder=args.base_folder, + # temporal_sample=temporal_sample, + # transform=transform, + # seed=args.seed, + with_pair=args.with_pair, + ) + return dataset + + elif args.dataset == "metric_dataset": + + dataset = MetricDataset( + base_folder=args.base_folder, + ) + return dataset + + else: + raise NotImplementedError(args.dataset) diff --git a/datasets/sakuga_ref_datasets.py b/datasets/sakuga_ref_datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..7493d28ae11293cf1440827d9c5c19ef284dae90 --- /dev/null +++ b/datasets/sakuga_ref_datasets.py @@ -0,0 +1,117 @@ +import os +from tracemalloc import start +import warnings +import glob +import random +import numpy as np +from PIL import Image + +import torch +from torch.utils.data import Dataset +import torchvision +import torch.distributed as dist + +from decord import VideoReader +from pcache_fileio import fileio +from pcache_fileio.oss_conf import OssConfigFactory + + +class SakugaRefDataset(Dataset): + def __init__( + self, + # width=1024, height=576, + video_frames=25, + ref_jump_frames=36, + base_folder='data/samples/', + file_list=None, + temporal_sample=None, + transform=None, + seed=42, + ): + """ + Args: + num_samples (int): Number of samples in the dataset. + channels (int): Number of channels, default is 3 for RGB. + """ + # Define the path to the folder containing video frames + # self.base_folder = 'bdd100k/images/track/mini' + self.base_folder = base_folder + + self.file_list = file_list + if file_list is None: + self.video_lists = glob.glob(os.path.join(self.base_folder, '*.mp4')) + else: + # read from file_list.txt + self.video_lists = [] + with open(file_list, 'r') as f: + for line in f: + video_path = line.strip() + self.video_lists.append(os.path.join(self.base_folder, video_path)) + + self.num_samples = len(self.video_lists) + self.channels = 3 + # self.width = width + # self.height = height + self.video_frames = video_frames + self.ref_jump_frames = ref_jump_frames + self.temporal_sample = temporal_sample + self.transform = transform + + self.seed = seed + + def __len__(self): + return self.num_samples + + def get_sample(self, idx): + """ + Args: + idx (int): Index of the sample to return. + + Returns: + dict: A dictionary containing the 'pixel_values' tensor of shape (16, channels, 320, 512). + """ + + # path = random.choice(self.video_lists) + path = self.video_lists[idx] + + if self.file_list is not None: # read from pcache + with open(path, 'rb') as f: + vframes = VideoReader(f) + else: + vframes, aframes, info = torchvision.io.read_video(filename=path, pts_unit='sec', output_format='TCHW') + total_frames = len(vframes) + + # Sampling video frames + ref_frame_ind, end_frame_ind = self.temporal_sample(total_frames) + if not end_frame_ind - ref_frame_ind >= self.video_frames+self.ref_jump_frames: + raise ValueError(f'video {path} does not have enough frames') + start_frame_ind = ref_frame_ind + self.ref_jump_frames + frame_indice = np.linspace(start_frame_ind, end_frame_ind-1, self.video_frames, dtype=int) + frame_indice = np.insert(frame_indice, 0, ref_frame_ind) + if self.file_list is not None: # read from pcache + video = torch.from_numpy(vframes.get_batch(frame_indice).asnumpy()).permute(0, 3, 1, 2).contiguous() + else: + video = vframes[frame_indice] + + # (f c h w) + pixel_values = self.transform(video) + + return {'pixel_values': pixel_values} # the [0] index for pixel_values is the reference image, the other indexes are the video frames + + def __getitem__(self, idx): + # return self.get_sample(idx) + + while(True): + try: + # idx = np.random.randint(0, len(self.video_lists) - 1) + # idx = self.rng.integers(0, len(self.video_lists)) + item = self.get_sample(idx) + return item + except: + # warnings.warn(f'loading {idx} failed, retrying...') + idx = np.random.randint(0, len(self.video_lists) - 1) + + + + # item = self.get_sample(idx) + # return item \ No newline at end of file diff --git a/figure/showcases/image1.gif b/figure/showcases/image1.gif new file mode 100644 index 0000000000000000000000000000000000000000..5528b803996d0c12a7640238ce6ec5b80368ea04 --- /dev/null +++ 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+size 4236348 diff --git a/install.sh b/install.sh new file mode 100644 index 0000000000000000000000000000000000000000..a3e5cf955a8345556b90f49435041a7fe4a7aa69 --- /dev/null +++ b/install.sh @@ -0,0 +1,22 @@ + + +pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116 +pip install diffusers==0.24.0 transformers==4.27.0 xformers==0.0.16 imageio==2.27.0 decord==0.6.0 +pip install huggingface_hub==0.24.7 + +pip install einops +pip install triton==2.1.0 +pip install opencv-python +pip install av scipy +pip install accelerate==0.27.2 + +pip install colorlog +pip install pyparsing==3.0.9 +pip install gradio==3.50.2 +pip install omegaconf +pip install scikit-image + + +cd cotracker && python setup.py install && cd ../ +pip install kornia +pip install moviepy \ No newline at end of file diff --git a/lineart_extractor/annotator/canny/__init__.py b/lineart_extractor/annotator/canny/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cb0da951dc838ec9dec2131007e036113281800b --- /dev/null +++ b/lineart_extractor/annotator/canny/__init__.py @@ -0,0 +1,6 @@ +import cv2 + + +class CannyDetector: + def __call__(self, img, low_threshold, high_threshold): + return cv2.Canny(img, low_threshold, high_threshold) diff --git a/lineart_extractor/annotator/hed/__init__.py b/lineart_extractor/annotator/hed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..003c66768666296ef59bcbd144dc132a2b362dbe --- /dev/null +++ b/lineart_extractor/annotator/hed/__init__.py @@ -0,0 +1,80 @@ +# This is an improved version and model of HED edge detection with Apache License, Version 2.0. +# Please use this implementation in your products +# This implementation may produce slightly different results from Saining Xie's official implementations, +# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations. +# Different from official models and other implementations, this is an RGB-input model (rather than BGR) +# and in this way it works better for gradio's RGB protocol + +import os +import cv2 +import torch +import numpy as np + +from einops import rearrange +from annotator.util import annotator_ckpts_path, safe_step + + +class DoubleConvBlock(torch.nn.Module): + def __init__(self, input_channel, output_channel, layer_number): + super().__init__() + self.convs = torch.nn.Sequential() + self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) + for i in range(1, layer_number): + self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) + self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0) + + def __call__(self, x, down_sampling=False): + h = x + if down_sampling: + h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2)) + for conv in self.convs: + h = conv(h) + h = torch.nn.functional.relu(h) + return h, self.projection(h) + + +class ControlNetHED_Apache2(torch.nn.Module): + def __init__(self): + super().__init__() + self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1))) + self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2) + self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2) + self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3) + self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3) + self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3) + + def __call__(self, x): + h = x - self.norm + h, projection1 = self.block1(h) + h, projection2 = self.block2(h, down_sampling=True) + h, projection3 = self.block3(h, down_sampling=True) + h, projection4 = self.block4(h, down_sampling=True) + h, projection5 = self.block5(h, down_sampling=True) + return projection1, projection2, projection3, projection4, projection5 + + +class HEDdetector: + def __init__(self): + remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth" + modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth") + if not os.path.exists(modelpath): + from basicsr.utils.download_util import load_file_from_url + load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) + self.netNetwork = ControlNetHED_Apache2().float().cuda().eval() + self.netNetwork.load_state_dict(torch.load(modelpath)) + + def __call__(self, input_image, safe=False): + assert input_image.ndim == 3 + H, W, C = input_image.shape + with torch.no_grad(): + image_hed = torch.from_numpy(input_image.copy()).float().cuda() + image_hed = rearrange(image_hed, 'h w c -> 1 c h w') + edges = self.netNetwork(image_hed) + edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] + edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] + edges = np.stack(edges, axis=2) + edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) + if safe: + edge = safe_step(edge) + edge = (edge * 255.0).clip(0, 255).astype(np.uint8) + return edge diff --git a/lineart_extractor/annotator/lineart/LICENSE b/lineart_extractor/annotator/lineart/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..16a9d56a3d4c15e4f34ac5426459c58487b01520 --- /dev/null +++ b/lineart_extractor/annotator/lineart/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Caroline Chan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/lineart_extractor/annotator/lineart/__init__.py b/lineart_extractor/annotator/lineart/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..27b13e0521a094f9d8969b9b7d34d0bbb056dd33 --- /dev/null +++ b/lineart_extractor/annotator/lineart/__init__.py @@ -0,0 +1,125 @@ +# From https://github.com/carolineec/informative-drawings +# MIT License + +import os +import cv2 +import torch +import numpy as np + +import torch.nn as nn +from einops import rearrange +from lineart_extractor.annotator.util import annotator_ckpts_path + + +norm_layer = nn.InstanceNorm2d + + +class ResidualBlock(nn.Module): + def __init__(self, in_features): + super(ResidualBlock, self).__init__() + + conv_block = [ nn.ReflectionPad2d(1), + nn.Conv2d(in_features, in_features, 3), + norm_layer(in_features), + nn.ReLU(inplace=True), + nn.ReflectionPad2d(1), + nn.Conv2d(in_features, in_features, 3), + norm_layer(in_features) + ] + + self.conv_block = nn.Sequential(*conv_block) + + def forward(self, x): + return x + self.conv_block(x) + + +class Generator(nn.Module): + def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True): + super(Generator, self).__init__() + + # Initial convolution block + model0 = [ nn.ReflectionPad2d(3), + nn.Conv2d(input_nc, 64, 7), + norm_layer(64), + nn.ReLU(inplace=True) ] + self.model0 = nn.Sequential(*model0) + + # Downsampling + model1 = [] + in_features = 64 + out_features = in_features*2 + for _ in range(2): + model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), + norm_layer(out_features), + nn.ReLU(inplace=True) ] + in_features = out_features + out_features = in_features*2 + self.model1 = nn.Sequential(*model1) + + model2 = [] + # Residual blocks + for _ in range(n_residual_blocks): + model2 += [ResidualBlock(in_features)] + self.model2 = nn.Sequential(*model2) + + # Upsampling + model3 = [] + out_features = in_features//2 + for _ in range(2): + model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), + norm_layer(out_features), + nn.ReLU(inplace=True) ] + in_features = out_features + out_features = in_features//2 + self.model3 = nn.Sequential(*model3) + + # Output layer + model4 = [ nn.ReflectionPad2d(3), + nn.Conv2d(64, output_nc, 7)] + if sigmoid: + model4 += [nn.Sigmoid()] + + self.model4 = nn.Sequential(*model4) + + def forward(self, x, cond=None): + out = self.model0(x) + out = self.model1(out) + out = self.model2(out) + out = self.model3(out) + out = self.model4(out) + + return out + + +class LineartDetector: + def __init__(self, device): + self.device = device + self.model = self.load_model('sk_model.pth') + self.model_coarse = self.load_model('sk_model2.pth') + + def load_model(self, name): + remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name + modelpath = os.path.join(annotator_ckpts_path, name) + if not os.path.exists(modelpath): + from basicsr.utils.download_util import load_file_from_url + load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) + model = Generator(3, 1, 3) + model.load_state_dict(torch.load(modelpath, map_location=torch.device('cpu'))) + model.eval() + model = model.to(self.device) + return model + + def __call__(self, input_image, coarse): + model = self.model_coarse if coarse else self.model + assert input_image.ndim == 3 + image = input_image + with torch.no_grad(): + image = torch.from_numpy(image).float().to(self.device) + image = image / 255.0 + image = rearrange(image, 'h w c -> 1 c h w') + line = model(image)[0][0] + + line = line.cpu().numpy() + line = (line * 255.0).clip(0, 255).astype(np.uint8) + + return line diff --git a/lineart_extractor/annotator/lineart_anime/LICENSE b/lineart_extractor/annotator/lineart_anime/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..16a9d56a3d4c15e4f34ac5426459c58487b01520 --- /dev/null +++ b/lineart_extractor/annotator/lineart_anime/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Caroline Chan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/lineart_extractor/annotator/lineart_anime/__init__.py b/lineart_extractor/annotator/lineart_anime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7c912917c3b768c68b703c19cefe13686a02ae2c --- /dev/null +++ b/lineart_extractor/annotator/lineart_anime/__init__.py @@ -0,0 +1,151 @@ +# Anime2sketch +# https://github.com/Mukosame/Anime2Sketch + +import numpy as np +import torch +import torch.nn as nn +import functools + +import os +import cv2 +from einops import rearrange +from annotator.util import annotator_ckpts_path + + +class UnetGenerator(nn.Module): + """Create a Unet-based generator""" + + def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet generator + Parameters: + input_nc (int) -- the number of channels in input images + output_nc (int) -- the number of channels in output images + num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7, + image of size 128x128 will become of size 1x1 # at the bottleneck + ngf (int) -- the number of filters in the last conv layer + norm_layer -- normalization layer + We construct the U-Net from the innermost layer to the outermost layer. + It is a recursive process. + """ + super(UnetGenerator, self).__init__() + # construct unet structure + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer + for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters + unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout) + # gradually reduce the number of filters from ngf * 8 to ngf + unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer + + def forward(self, input): + """Standard forward""" + return self.model(input) + + +class UnetSkipConnectionBlock(nn.Module): + """Defines the Unet submodule with skip connection. + X -------------------identity---------------------- + |-- downsampling -- |submodule| -- upsampling --| + """ + + def __init__(self, outer_nc, inner_nc, input_nc=None, + submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False): + """Construct a Unet submodule with skip connections. + Parameters: + outer_nc (int) -- the number of filters in the outer conv layer + inner_nc (int) -- the number of filters in the inner conv layer + input_nc (int) -- the number of channels in input images/features + submodule (UnetSkipConnectionBlock) -- previously defined submodules + outermost (bool) -- if this module is the outermost module + innermost (bool) -- if this module is the innermost module + norm_layer -- normalization layer + use_dropout (bool) -- if use dropout layers. + """ + super(UnetSkipConnectionBlock, self).__init__() + self.outermost = outermost + if type(norm_layer) == functools.partial: + use_bias = norm_layer.func == nn.InstanceNorm2d + else: + use_bias = norm_layer == nn.InstanceNorm2d + if input_nc is None: + input_nc = outer_nc + downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, + stride=2, padding=1, bias=use_bias) + downrelu = nn.LeakyReLU(0.2, True) + downnorm = norm_layer(inner_nc) + uprelu = nn.ReLU(True) + upnorm = norm_layer(outer_nc) + + if outermost: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1) + down = [downconv] + up = [uprelu, upconv, nn.Tanh()] + model = down + [submodule] + up + elif innermost: + upconv = nn.ConvTranspose2d(inner_nc, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv] + up = [uprelu, upconv, upnorm] + model = down + up + else: + upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, + kernel_size=4, stride=2, + padding=1, bias=use_bias) + down = [downrelu, downconv, downnorm] + up = [uprelu, upconv, upnorm] + + if use_dropout: + model = down + [submodule] + up + [nn.Dropout(0.5)] + else: + model = down + [submodule] + up + + self.model = nn.Sequential(*model) + + def forward(self, x): + if self.outermost: + return self.model(x) + else: # add skip connections + return torch.cat([x, self.model(x)], 1) + + +class LineartAnimeDetector: + def __init__(self, device): + remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/netG.pth" + modelpath = os.path.join(annotator_ckpts_path, "netG.pth") + if not os.path.exists(modelpath): + from basicsr.utils.download_util import load_file_from_url + load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path) + norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + net = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False) + ckpt = torch.load(modelpath) + for key in list(ckpt.keys()): + if 'module.' in key: + ckpt[key.replace('module.', '')] = ckpt[key] + del ckpt[key] + net.load_state_dict(ckpt) + net = net.to(device) + net.eval() + self.model = net + self.device = device + + def __call__(self, input_image): + H, W, C = input_image.shape + Hn = 256 * int(np.ceil(float(H) / 256.0)) + Wn = 256 * int(np.ceil(float(W) / 256.0)) + img = cv2.resize(input_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC) + with torch.no_grad(): + image_feed = torch.from_numpy(img).float().to(self.device) + image_feed = image_feed / 127.5 - 1.0 + image_feed = rearrange(image_feed, 'h w c -> 1 c h w') + + line = self.model(image_feed)[0, 0] * 127.5 + 127.5 + line = line.cpu().numpy() + + line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC) + line = line.clip(0, 255).astype(np.uint8) + return line + diff --git a/lineart_extractor/annotator/util.py b/lineart_extractor/annotator/util.py new file mode 100644 index 0000000000000000000000000000000000000000..e0b217ef9adf92dd5b1fe0debcfb07d0f241a4cb --- /dev/null +++ b/lineart_extractor/annotator/util.py @@ -0,0 +1,98 @@ +import random + +import numpy as np +import cv2 +import os + + +annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts') + + +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + + +def resize_image(input_image, resolution): + H, W, C = input_image.shape + H = float(H) + W = float(W) + k = float(resolution) / min(H, W) + H *= k + W *= k + H = int(np.round(H / 64.0)) * 64 + W = int(np.round(W / 64.0)) * 64 + img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) + return img + + +def nms(x, t, s): + x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) + + f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) + f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) + f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) + f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) + + y = np.zeros_like(x) + + for f in [f1, f2, f3, f4]: + np.putmask(y, cv2.dilate(x, kernel=f) == x, x) + + z = np.zeros_like(y, dtype=np.uint8) + z[y > t] = 255 + return z + + +def make_noise_disk(H, W, C, F): + noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C)) + noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC) + noise = noise[F: F + H, F: F + W] + noise -= np.min(noise) + noise /= np.max(noise) + if C == 1: + noise = noise[:, :, None] + return noise + + +def min_max_norm(x): + x -= np.min(x) + x /= np.maximum(np.max(x), 1e-5) + return x + + +def safe_step(x, step=2): + y = x.astype(np.float32) * float(step + 1) + y = y.astype(np.int32).astype(np.float32) / float(step) + return y + + +def img2mask(img, H, W, low=10, high=90): + assert img.ndim == 3 or img.ndim == 2 + assert img.dtype == np.uint8 + + if img.ndim == 3: + y = img[:, :, random.randrange(0, img.shape[2])] + else: + y = img + + y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC) + + if random.uniform(0, 1) < 0.5: + y = 255 - y + + return y < np.percentile(y, random.randrange(low, high)) diff --git a/models_diffusers/adapter_model.py b/models_diffusers/adapter_model.py new file mode 100644 index 0000000000000000000000000000000000000000..361a067db37b008bd125eaaf658d3eeab78fd069 --- /dev/null +++ b/models_diffusers/adapter_model.py @@ -0,0 +1,142 @@ +import random +from typing import List + +import torch +import torch.nn as nn +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin + +# from videoswap.utils.registry import MODEL_REGISTRY + + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, mid_dim=128): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(in_dim, mid_dim, bias=True), + nn.SiLU(inplace=False), + nn.Linear(mid_dim, out_dim, bias=True) + ) + + def forward(self, x): + return self.mlp(x) + + +def bilinear_interpolation(level_adapter_state, x, y, frame_idx, interpolated_value): + # level_adapter_state: (frames, channels, h, w) + # note the boundary + x1 = int(x) + y1 = int(y) + x2 = x1 + 1 + y2 = y1 + 1 + x_frac = x - x1 + y_frac = y - y1 + + x1, x2 = max(min(x1, level_adapter_state.shape[3] - 1), 0), max(min(x2, level_adapter_state.shape[3] - 1), 0) + y1, y2 = max(min(y1, level_adapter_state.shape[2] - 1), 0), max(min(y2, level_adapter_state.shape[2] - 1), 0) + + w11 = (1 - x_frac) * (1 - y_frac) + w21 = x_frac * (1 - y_frac) + w12 = (1 - x_frac) * y_frac + w22 = x_frac * y_frac + + level_adapter_state[frame_idx, :, y1, x1] += interpolated_value * w11 + level_adapter_state[frame_idx, :, y1, x2] += interpolated_value * w21 + level_adapter_state[frame_idx, :, y2, x1] += interpolated_value * w12 + level_adapter_state[frame_idx, :, y2, x2] += interpolated_value * w22 + + return level_adapter_state + + +# @MODEL_REGISTRY.register() +class SparsePointAdapter(ModelMixin, ConfigMixin): + + @register_to_config + def __init__( + self, + embedding_channels=1280, + channels=[320, 640, 1280, 1280], + downsample_rate=[8, 16, 32, 64], + mid_dim=128, + ): + super().__init__() + + self.model_list = nn.ModuleList() + + for ch in channels: + self.model_list.append(MLP(embedding_channels, ch, mid_dim)) + + self.downsample_rate = downsample_rate + self.channels = channels + self.radius = 2 + + def generate_loss_mask(self, point_index_list, point_tracker, num_frames, h, w, loss_type): + if loss_type == 'global': + # True + loss_mask = torch.ones((num_frames, 4, h // self.downsample_rate[0], w // self.downsample_rate[0])) + else: + # only compute loss for visible points, with a radius that is irrelevant of the downsampling scale + loss_mask = torch.zeros((num_frames, 4, h // self.downsample_rate[0], w // self.downsample_rate[0])) + for point_idx in point_index_list: + for frame_idx in range(num_frames): + px, py = point_tracker[frame_idx, point_idx] + + if px < 0 or py < 0: + continue + else: + px, py = px / self.downsample_rate[0], py / self.downsample_rate[0] + + x1 = int(px) - self.radius + y1 = int(py) - self.radius + x2 = int(px) + self.radius + y2 = int(py) + self.radius + + x1, x2 = max(min(x1, loss_mask.shape[3] - 1), 0), max(min(x2, loss_mask.shape[3] - 1), 0) + y1, y2 = max(min(y1, loss_mask.shape[2] - 1), 0), max(min(y2, loss_mask.shape[2] - 1), 0) + + loss_mask[:, :, y1:y2, x1:x2] = 1.0 + return loss_mask + + def forward(self, point_tracker, size, point_embedding, index_list=None, drop_rate=0.0, loss_type='global') -> List[torch.Tensor]: + + # # (1, frames, num_points, 2) -> (frames, num_points, 2) + # point_tracker = point_tracker.squeeze(0) + # # (1, num_points, 1280) -> (num_points, 1280) + # point_embedding = point_embedding.squeeze(0) + + w, h = size + num_frames, num_points = point_tracker.shape[:2] + + if self.training: + point_index_list = [point_idx for point_idx in range(num_points) if random.random() > drop_rate] + loss_mask = self.generate_loss_mask(point_index_list, point_tracker, num_frames, h, w, loss_type) + else: + point_index_list = [point_idx for point_idx in range(num_points) if index_list is None or point_idx in index_list] + + adapter_state = [] + for level_idx, module in enumerate(self.model_list): + + downsample_rate = self.downsample_rate[level_idx] + level_w, level_h = w // downsample_rate, h // downsample_rate + + # e.g. (num_points, 1280) -> (num_points, 320) + point_feat = module(point_embedding) + + level_adapter_state = torch.zeros((num_frames, self.channels[level_idx], level_h, level_w)).to(point_feat.device, dtype=point_feat.dtype) + + for point_idx in point_index_list: + + for frame_idx in range(num_frames): + px, py = point_tracker[frame_idx, point_idx] + + if px < 0 or py < 0: + continue + else: + px, py = px / downsample_rate, py / downsample_rate + level_adapter_state = bilinear_interpolation(level_adapter_state, px, py, frame_idx, point_feat[point_idx]) + adapter_state.append(level_adapter_state) + + if self.training: + return adapter_state, loss_mask + else: + return adapter_state diff --git a/models_diffusers/camera/attention.py b/models_diffusers/camera/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe4c6d49585741c7f0c94ebf7eb0ec3ec04bd9f --- /dev/null +++ b/models_diffusers/camera/attention.py @@ -0,0 +1,71 @@ +import torch +from typing import Optional +from diffusers.models.attention import TemporalBasicTransformerBlock, _chunked_feed_forward +from diffusers.utils.torch_utils import maybe_allow_in_graph + + +@maybe_allow_in_graph +class TemporalPoseCondTransformerBlock(TemporalBasicTransformerBlock): + def forward( + self, + hidden_states: torch.FloatTensor, # [bs * num_frame, h * w, c] + num_frames: int, + encoder_hidden_states: Optional[torch.FloatTensor] = None, # [bs * h * w, 1, c] + pose_feature: Optional[torch.FloatTensor] = None, # [bs, c, n_frame, h, w] + ) -> torch.FloatTensor: + # Notice that normalization is always applied before the real computation in the following blocks. + # 0. Self-Attention + + batch_frames, seq_length, channels = hidden_states.shape + batch_size = batch_frames // num_frames + + hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels) + hidden_states = hidden_states.permute(0, 2, 1, 3) + hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels) # [bs * h * w, frame, c] + + residual = hidden_states + hidden_states = self.norm_in(hidden_states) + + if self._chunk_size is not None: + hidden_states = _chunked_feed_forward(self.ff_in, hidden_states, self._chunk_dim, self._chunk_size) + else: + hidden_states = self.ff_in(hidden_states) + + if self.is_res: + hidden_states = hidden_states + residual + + norm_hidden_states = self.norm1(hidden_states) + if pose_feature is not None: + pose_feature = pose_feature.permute(0, 3, 4, 2, 1).reshape(batch_size * seq_length, num_frames, -1) + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None, pose_feature=pose_feature) + else: + attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None) + hidden_states = attn_output + hidden_states + + # 3. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = self.norm2(hidden_states) + if pose_feature is not None: + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, pose_feature=pose_feature) + else: + attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self._chunk_size is not None: + ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) + else: + ff_output = self.ff(norm_hidden_states) + + if self.is_res: + hidden_states = ff_output + hidden_states + else: + hidden_states = ff_output + + hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels) + hidden_states = hidden_states.permute(0, 2, 1, 3) + hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels) + + return hidden_states \ No newline at end of file diff --git a/models_diffusers/camera/attention_processor.py b/models_diffusers/camera/attention_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..77c6ffcff3765ef2a9486e9acd96fee7ca6bd479 --- /dev/null +++ b/models_diffusers/camera/attention_processor.py @@ -0,0 +1,603 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +import logging +from diffusers.models.attention import Attention +from diffusers.utils import USE_PEFT_BACKEND, is_xformers_available +from typing import Optional, Callable + +from einops import rearrange + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + +logger = logging.getLogger(__name__) + + +class AttnProcessor: + r""" + Default processor for performing attention-related computations. + """ + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + pose_feature=None, # the only difference to the original code + ) -> torch.Tensor: + residual = hidden_states + + args = () if USE_PEFT_BACKEND else (scale,) + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states, *args) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states, *args) + value = attn.to_v(encoder_hidden_states, *args) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states, *args) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class AttnProcessor2_0: + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). + """ + + def __init__(self): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + pose_feature=None + ) -> torch.FloatTensor: + residual = hidden_states + + args = () if USE_PEFT_BACKEND else (scale,) + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + args = () if USE_PEFT_BACKEND else (scale,) + query = attn.to_q(hidden_states, *args) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states, *args) + value = attn.to_v(encoder_hidden_states, *args) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states, *args) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class XFormersAttnProcessor: + r""" + Processor for implementing memory efficient attention using xFormers. + + Args: + attention_op (`Callable`, *optional*, defaults to `None`): + The base + [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to + use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best + operator. + """ + + def __init__(self, attention_op: Optional[Callable] = None): + self.attention_op = attention_op + + def __call__( + self, + attn: Attention, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + pose_feature=None, # the only difference to the original code + ) -> torch.FloatTensor: + residual = hidden_states + + args = () if USE_PEFT_BACKEND else (scale,) + + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, key_tokens, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size) + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = attn.to_q(hidden_states, *args) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states, *args) + value = attn.to_v(encoder_hidden_states, *args) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention( + query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale + ) + hidden_states = hidden_states.to(query.dtype) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states, *args) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if input_ndim == 4: + hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class PoseAdaptorAttnProcessor(nn.Module): + def __init__( + self, + hidden_size, # dimension of hidden state + pose_feature_dim=None, # dimension of the pose feature + cross_attention_dim=None, # dimension of the text embedding + query_condition=False, + key_value_condition=False, + scale=1.0, + ): + super().__init__() + + self.hidden_size = hidden_size + self.pose_feature_dim = pose_feature_dim + self.cross_attention_dim = cross_attention_dim + self.scale = scale + self.query_condition = query_condition + self.key_value_condition = key_value_condition + assert hidden_size == pose_feature_dim + if self.query_condition and self.key_value_condition: + self.qkv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.qkv_merge.weight) + init.zeros_(self.qkv_merge.bias) + elif self.query_condition: + self.q_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.q_merge.weight) + init.zeros_(self.q_merge.bias) + else: + self.kv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.kv_merge.weight) + init.zeros_(self.kv_merge.bias) + + def forward( + self, + attn, + hidden_states, + pose_feature, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + scale=None, + ): + assert pose_feature is not None + pose_embedding_scale = (scale or self.scale) + + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + assert hidden_states.ndim == 3 and pose_feature.ndim == 3 + + if self.query_condition and self.key_value_condition: + assert encoder_hidden_states is None + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + assert encoder_hidden_states.ndim == 3 + + batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape + attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + if attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + if self.query_condition and self.key_value_condition: # only self attention + query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = query_hidden_state + elif self.query_condition: + query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = encoder_hidden_states + else: + key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states + query_hidden_state = hidden_states + + # original attention + query = attn.to_q(query_hidden_state) + key = attn.to_k(key_value_hidden_state) + value = attn.to_v(key_value_hidden_state) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class PoseAdaptorAttnProcessor2_0(nn.Module): + def __init__( + self, + hidden_size, # dimension of hidden state + pose_feature_dim=None, # dimension of the pose feature + cross_attention_dim=None, # dimension of the text embedding + query_condition=False, + key_value_condition=False, + scale=1.0, + ): + super().__init__() + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + + self.hidden_size = hidden_size + self.pose_feature_dim = pose_feature_dim + self.cross_attention_dim = cross_attention_dim + self.scale = scale + self.query_condition = query_condition + self.key_value_condition = key_value_condition + assert hidden_size == pose_feature_dim + if self.query_condition and self.key_value_condition: + self.qkv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.qkv_merge.weight) + init.zeros_(self.qkv_merge.bias) + elif self.query_condition: + self.q_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.q_merge.weight) + init.zeros_(self.q_merge.bias) + else: + self.kv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.kv_merge.weight) + init.zeros_(self.kv_merge.bias) + + def forward( + self, + attn, + hidden_states, + pose_feature, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + scale=None, + ): + assert pose_feature is not None + pose_embedding_scale = (scale or self.scale) + + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + assert hidden_states.ndim == 3 and pose_feature.ndim == 3 + + if self.query_condition and self.key_value_condition: + assert encoder_hidden_states is None + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + assert encoder_hidden_states.ndim == 3 + + batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + if attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + if self.query_condition and self.key_value_condition: # only self attention + query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = query_hidden_state + elif self.query_condition: + query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = encoder_hidden_states + else: + key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states + query_hidden_state = hidden_states + + # original attention + query = attn.to_q(query_hidden_state) + key = attn.to_k(key_value_hidden_state) + value = attn.to_v(key_value_hidden_state) + + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) # [bs, seq_len, nhead, head_dim] -> [bs, nhead, seq_len, head_dim] + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False) # [bs, nhead, seq_len, head_dim] + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) # [bs, seq_len, dim] + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states + + +class PoseAdaptorXFormersAttnProcessor(nn.Module): + def __init__( + self, + hidden_size, # dimension of hidden state + pose_feature_dim=None, # dimension of the pose feature + cross_attention_dim=None, # dimension of the text embedding + query_condition=False, + key_value_condition=False, + scale=1.0, + attention_op: Optional[Callable] = None, + ): + super().__init__() + + self.hidden_size = hidden_size + self.pose_feature_dim = pose_feature_dim + self.cross_attention_dim = cross_attention_dim + self.scale = scale + self.query_condition = query_condition + self.key_value_condition = key_value_condition + self.attention_op = attention_op + assert hidden_size == pose_feature_dim + if self.query_condition and self.key_value_condition: + self.qkv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.qkv_merge.weight) + init.zeros_(self.qkv_merge.bias) + elif self.query_condition: + self.q_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.q_merge.weight) + init.zeros_(self.q_merge.bias) + else: + self.kv_merge = nn.Linear(hidden_size, hidden_size) + init.zeros_(self.kv_merge.weight) + init.zeros_(self.kv_merge.bias) + + def forward( + self, + attn, + hidden_states, + pose_feature, + encoder_hidden_states=None, + attention_mask=None, + temb=None, + scale=None, + ): + assert pose_feature is not None + pose_embedding_scale = (scale or self.scale) + + residual = hidden_states + if attn.spatial_norm is not None: + hidden_states = attn.spatial_norm(hidden_states, temb) + + assert hidden_states.ndim == 3 and pose_feature.ndim == 3 + + if self.query_condition and self.key_value_condition: + assert encoder_hidden_states is None + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + assert encoder_hidden_states.ndim == 3 + + batch_size, ehs_sequence_length, _ = encoder_hidden_states.shape + attention_mask = attn.prepare_attention_mask(attention_mask, ehs_sequence_length, batch_size) + if attention_mask is not None: + # expand our mask's singleton query_tokens dimension: + # [batch*heads, 1, key_tokens] -> + # [batch*heads, query_tokens, key_tokens] + # so that it can be added as a bias onto the attention scores that xformers computes: + # [batch*heads, query_tokens, key_tokens] + # we do this explicitly because xformers doesn't broadcast the singleton dimension for us. + _, query_tokens, _ = hidden_states.shape + attention_mask = attention_mask.expand(-1, query_tokens, -1) + + if attn.group_norm is not None: + hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + if attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + if self.query_condition and self.key_value_condition: # only self attention + query_hidden_state = self.qkv_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = query_hidden_state + elif self.query_condition: + query_hidden_state = self.q_merge(hidden_states + pose_feature) * pose_embedding_scale + hidden_states + key_value_hidden_state = encoder_hidden_states + else: + key_value_hidden_state = self.kv_merge(encoder_hidden_states + pose_feature) * pose_embedding_scale + encoder_hidden_states + query_hidden_state = hidden_states + + # original attention + query = attn.to_q(query_hidden_state) + key = attn.to_k(key_value_hidden_state) + value = attn.to_v(key_value_hidden_state) + + query = attn.head_to_batch_dim(query).contiguous() + key = attn.head_to_batch_dim(key).contiguous() + value = attn.head_to_batch_dim(value).contiguous() + + hidden_states = xformers.ops.memory_efficient_attention( + query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale + ) + hidden_states = hidden_states.to(query.dtype) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + if attn.residual_connection: + hidden_states = hidden_states + residual + + hidden_states = hidden_states / attn.rescale_output_factor + + return hidden_states diff --git a/models_diffusers/camera/motion_module.py b/models_diffusers/camera/motion_module.py new file mode 100644 index 0000000000000000000000000000000000000000..7020504daf003b0d14315085852ff5413bf5aa8e --- /dev/null +++ b/models_diffusers/camera/motion_module.py @@ -0,0 +1,400 @@ +from dataclasses import dataclass +from typing import Callable, Optional + +import torch +from torch import nn + +from diffusers.utils import BaseOutput +from diffusers.models.attention_processor import Attention +from diffusers.models.attention import FeedForward + +from typing import Dict, Any +# from cameractrl.models.attention_processor import PoseAdaptorAttnProcessor +from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor + +from einops import rearrange +import math + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + # return super().forward(x) + + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +def get_motion_module( + in_channels, + motion_module_type: str, + motion_module_kwargs: dict +): + if motion_module_type == "Vanilla": + return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads=8, + num_transformer_block=2, + attention_block_types=("Temporal_Self",), + temporal_position_encoding=True, + temporal_position_encoding_max_len=32, + temporal_attention_dim_div=1, + cross_attention_dim=320, + zero_initialize=True, + encoder_hidden_states_query=(False, False), + attention_activation_scale=1.0, + attention_processor_kwargs: Dict = {}, + causal_temporal_attention=False, + causal_temporal_attention_mask_type="", + rescale_output_factor=1.0 + ): + super().__init__() + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_attention_dim=cross_attention_dim, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + encoder_hidden_states_query=encoder_hidden_states_query, + attention_activation_scale=attention_activation_scale, + attention_processor_kwargs=attention_processor_kwargs, + causal_temporal_attention=causal_temporal_attention, + causal_temporal_attention_mask_type=causal_temporal_attention_mask_type, + rescale_output_factor=rescale_output_factor + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, + cross_attention_kwargs: Dict[str, Any] = {}): + hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask, cross_attention_kwargs=cross_attention_kwargs) + + output = hidden_states + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + num_layers, + attention_block_types=("Temporal_Self", "Temporal_Self",), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=320, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + temporal_position_encoding=False, + temporal_position_encoding_max_len=32, + encoder_hidden_states_query=(False, False), + attention_activation_scale=1.0, + attention_processor_kwargs: Dict = {}, + + causal_temporal_attention=None, + causal_temporal_attention_mask_type="", + rescale_output_factor=1.0 + ): + super().__init__() + assert causal_temporal_attention is not None + self.causal_temporal_attention = causal_temporal_attention + + assert (not causal_temporal_attention) or (causal_temporal_attention_mask_type != "") + self.causal_temporal_attention_mask_type = causal_temporal_attention_mask_type + self.causal_temporal_attention_mask = None + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = InflatedGroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + encoder_hidden_states_query=encoder_hidden_states_query, + attention_activation_scale=attention_activation_scale, + attention_processor_kwargs=attention_processor_kwargs, + rescale_output_factor=rescale_output_factor, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def get_causal_temporal_attention_mask(self, hidden_states): + batch_size, sequence_length, dim = hidden_states.shape + + if self.causal_temporal_attention_mask is None or self.causal_temporal_attention_mask.shape != ( + batch_size, sequence_length, sequence_length): + if self.causal_temporal_attention_mask_type == "causal": + # 1. vanilla causal mask + mask = torch.tril(torch.ones(sequence_length, sequence_length)) + + elif self.causal_temporal_attention_mask_type == "2-seq": + # 2. 2-seq + mask = torch.zeros(sequence_length, sequence_length) + mask[:sequence_length // 2, :sequence_length // 2] = 1 + mask[-sequence_length // 2:, -sequence_length // 2:] = 1 + + elif self.causal_temporal_attention_mask_type == "0-prev": + # attn to the previous frame + indices = torch.arange(sequence_length) + indices_prev = indices - 1 + indices_prev[0] = 0 + mask = torch.zeros(sequence_length, sequence_length) + mask[:, 0] = 1. + mask[indices, indices_prev] = 1. + + elif self.causal_temporal_attention_mask_type == "0": + # only attn to first frame + mask = torch.zeros(sequence_length, sequence_length) + mask[:, 0] = 1 + + elif self.causal_temporal_attention_mask_type == "wo-self": + indices = torch.arange(sequence_length) + mask = torch.ones(sequence_length, sequence_length) + mask[indices, indices] = 0 + + elif self.causal_temporal_attention_mask_type == "circle": + indices = torch.arange(sequence_length) + indices_prev = indices - 1 + indices_prev[0] = 0 + + mask = torch.eye(sequence_length) + mask[indices, indices_prev] = 1 + mask[0, -1] = 1 + + else: + raise ValueError + + # generate attention mask fron binary values + mask = mask.masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) + mask = mask.unsqueeze(0) + mask = mask.repeat(batch_size, 1, 1) + + self.causal_temporal_attention_mask = mask.to(hidden_states.device) + + return self.causal_temporal_attention_mask + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, + cross_attention_kwargs: Dict[str, Any] = {},): + residual = hidden_states + + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + height, width = hidden_states.shape[-2:] + + hidden_states = self.norm(hidden_states) + hidden_states = rearrange(hidden_states, "b c f h w -> (b h w) f c") + hidden_states = self.proj_in(hidden_states) + + attention_mask = self.get_causal_temporal_attention_mask( + hidden_states) if self.causal_temporal_attention else attention_mask + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs) + hidden_states = self.proj_out(hidden_states) + + hidden_states = rearrange(hidden_states, "(b h w) f c -> b c f h w", h=height, w=width) + + output = hidden_states + residual + + return output + + +class TemporalTransformerBlock(nn.Module): + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types=("Temporal_Self", "Temporal_Self",), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + temporal_position_encoding=False, + temporal_position_encoding_max_len=32, + encoder_hidden_states_query=(False, False), + attention_activation_scale=1.0, + attention_processor_kwargs: Dict = {}, + rescale_output_factor=1.0 + ): + super().__init__() + + attention_blocks = [] + norms = [] + self.attention_block_types = attention_block_types + + for block_idx, block_name in enumerate(attention_block_types): + attention_blocks.append( + TemporalSelfAttention( + attention_mode=block_name, + cross_attention_dim=cross_attention_dim if block_name in ['Temporal_Cross', 'Temporal_Pose_Adaptor'] else None, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + rescale_output_factor=rescale_output_factor, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs: Dict[str, Any] = {}): + for attention_block, norm, attention_block_type in zip(self.attention_blocks, self.norms, self.attention_block_types): + norm_hidden_states = norm(hidden_states) + hidden_states = attention_block( + norm_hidden_states, + encoder_hidden_states=norm_hidden_states if attention_block_type == 'Temporal_Self' else encoder_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs + ) + hidden_states + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states + return output + + +class PositionalEncoding(nn.Module): + def __init__( + self, + d_model, + dropout=0., + max_len=32, + ): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer('pe', pe) + + def forward(self, x): + x = x + self.pe[:, :x.size(1)] + return self.dropout(x) + + +class TemporalSelfAttention(Attention): + def __init__( + self, + attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=32, + rescale_output_factor=1.0, + *args, **kwargs + ): + super().__init__(*args, **kwargs) + assert attention_mode == "Temporal_Self" + + self.pos_encoder = PositionalEncoding( + kwargs["query_dim"], + max_len=temporal_position_encoding_max_len + ) if temporal_position_encoding else None + self.rescale_output_factor = rescale_output_factor + + def set_use_memory_efficient_attention_xformers( + self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None + ): + # disable motion module efficient xformers to avoid bad results, don't know why + # TODO: fix this bug + pass + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, **cross_attention_kwargs): + # The `Attention` class can call different attention processors / attention functions + # here we simply pass along all tensors to the selected processor class + # For standard processors that are defined here, `**cross_attention_kwargs` is empty + + # add position encoding + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + if "pose_feature" in cross_attention_kwargs: + pose_feature = cross_attention_kwargs["pose_feature"] + if pose_feature.ndim == 5: + pose_feature = rearrange(pose_feature, "b c f h w -> (b h w) f c") + else: + assert pose_feature.ndim == 3 + cross_attention_kwargs["pose_feature"] = pose_feature + + if isinstance(self.processor, PoseAdaptorAttnProcessor): + return self.processor( + self, + hidden_states, + cross_attention_kwargs.pop('pose_feature'), + encoder_hidden_states=None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + elif hasattr(self.processor, "__call__"): + return self.processor.__call__( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + else: + return self.processor( + self, + hidden_states, + encoder_hidden_states=None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + diff --git a/models_diffusers/camera/pose_adaptor.py b/models_diffusers/camera/pose_adaptor.py new file mode 100644 index 0000000000000000000000000000000000000000..ac9f4ebae295507c04f89d49f3aedaf8a842b68d --- /dev/null +++ b/models_diffusers/camera/pose_adaptor.py @@ -0,0 +1,244 @@ +import math +import torch +import torch.nn as nn +from einops import rearrange +from typing import List, Tuple +# from cameractrl.models.motion_module import TemporalTransformerBlock +from models_diffusers.camera.motion_module import TemporalTransformerBlock + + +def get_parameter_dtype(parameter: torch.nn.Module): + try: + params = tuple(parameter.parameters()) + if len(params) > 0: + return params[0].dtype + + buffers = tuple(parameter.buffers()) + if len(buffers) > 0: + return buffers[0].dtype + + except StopIteration: + # For torch.nn.DataParallel compatibility in PyTorch 1.5 + + def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[str, torch.Tensor]]: + tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)] + return tuples + + gen = parameter._named_members(get_members_fn=find_tensor_attributes) + first_tuple = next(gen) + return first_tuple[1].dtype + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class PoseAdaptor(nn.Module): + def __init__(self, unet, pose_encoder): + super().__init__() + self.unet = unet + self.pose_encoder = pose_encoder + + def forward(self, inp_noisy_latents, timesteps, encoder_hidden_states, added_time_ids, pose_embedding): + assert pose_embedding.ndim == 5 + pose_embedding_features = self.pose_encoder(pose_embedding) # b c f h w + noise_pred = self.unet( + inp_noisy_latents, + timesteps, + encoder_hidden_states, + added_time_ids=added_time_ids, + pose_features=pose_embedding_features, + ).sample + + return noise_pred + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResnetBlock(nn.Module): + + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class PositionalEncoding(nn.Module): + def __init__( + self, + d_model, + dropout=0., + max_len=32, + ): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2, ...] = torch.sin(position * div_term) + pe[0, :, 1::2, ...] = torch.cos(position * div_term) + pe.unsqueeze_(-1).unsqueeze_(-1) + self.register_buffer('pe', pe) + + def forward(self, x): + x = x + self.pe[:, :x.size(1), ...] + return self.dropout(x) + + +class CameraPoseEncoder(nn.Module): + + def __init__(self, + downscale_factor, + channels=[320, 640, 1280, 1280], + nums_rb=3, + cin=64, + ksize=3, + sk=False, + use_conv=True, + compression_factor=1, + temporal_attention_nhead=8, + attention_block_types=("Temporal_Self", ), + temporal_position_encoding=False, + temporal_position_encoding_max_len=16, + rescale_output_factor=1.0): + super(CameraPoseEncoder, self).__init__() + self.unshuffle = nn.PixelUnshuffle(downscale_factor) + self.channels = channels + self.nums_rb = nums_rb + self.encoder_down_conv_blocks = nn.ModuleList() + self.encoder_down_attention_blocks = nn.ModuleList() + for i in range(len(channels)): + conv_layers = nn.ModuleList() + temporal_attention_layers = nn.ModuleList() + for j in range(nums_rb): + if j == 0 and i != 0: + in_dim = channels[i - 1] + out_dim = int(channels[i] / compression_factor) + conv_layer = ResnetBlock(in_dim, out_dim, down=True, ksize=ksize, sk=sk, use_conv=use_conv) + elif j == 0: + in_dim = channels[0] + out_dim = int(channels[i] / compression_factor) + conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv) + elif j == nums_rb - 1: + in_dim = channels[i] / compression_factor + out_dim = channels[i] + conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv) + else: + in_dim = int(channels[i] / compression_factor) + out_dim = int(channels[i] / compression_factor) + conv_layer = ResnetBlock(in_dim, out_dim, down=False, ksize=ksize, sk=sk, use_conv=use_conv) + temporal_attention_layer = TemporalTransformerBlock(dim=out_dim, + num_attention_heads=temporal_attention_nhead, + attention_head_dim=int(out_dim / temporal_attention_nhead), + attention_block_types=attention_block_types, + dropout=0.0, + cross_attention_dim=None, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + rescale_output_factor=rescale_output_factor) + conv_layers.append(conv_layer) + temporal_attention_layers.append(temporal_attention_layer) + self.encoder_down_conv_blocks.append(conv_layers) + self.encoder_down_attention_blocks.append(temporal_attention_layers) + + self.encoder_conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + @property + def dtype(self) -> torch.dtype: + """ + `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype). + """ + return get_parameter_dtype(self) + + def forward(self, x): + # unshuffle + bs = x.shape[0] + x = rearrange(x, "b f c h w -> (b f) c h w") + x = self.unshuffle(x) + # extract features + features = [] + x = self.encoder_conv_in(x) + for res_block, attention_block in zip(self.encoder_down_conv_blocks, self.encoder_down_attention_blocks): + for res_layer, attention_layer in zip(res_block, attention_block): + x = res_layer(x) + h, w = x.shape[-2:] + x = rearrange(x, '(b f) c h w -> (b h w) f c', b=bs) + x = attention_layer(x) + x = rearrange(x, '(b h w) f c -> (b f) c h w', h=h, w=w) + features.append(rearrange(x, '(b f) c h w -> b c f h w', b=bs)) + return features diff --git a/models_diffusers/controlnet_svd.py b/models_diffusers/controlnet_svd.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc4153a05da7b65b3c95949a0d5baa8a6e7ef36 --- /dev/null +++ b/models_diffusers/controlnet_svd.py @@ -0,0 +1,776 @@ + +# Copyright 2023 The HuggingFace Team. 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 dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import torch +from torch import nn +from torch.nn import functional as F + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import FromOriginalControlnetMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.attention_processor import ( + ADDED_KV_ATTENTION_PROCESSORS, + CROSS_ATTENTION_PROCESSORS, + AttentionProcessor, + AttnAddedKVProcessor, + AttnProcessor, +) +from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +# from diffusers.models.unet_3d_blocks import get_down_block, get_up_block, UNetMidBlockSpatioTemporal +from models_diffusers.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block +from diffusers.models import UNetSpatioTemporalConditionModel + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class ControlNetOutput(BaseOutput): + """ + The output of [`ControlNetModel`]. + + Args: + down_block_res_samples (`tuple[torch.Tensor]`): + A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should + be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be + used to condition the original UNet's downsampling activations. + mid_down_block_re_sample (`torch.Tensor`): + The activation of the midde block (the lowest sample resolution). Each tensor should be of shape + `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`. + Output can be used to condition the original UNet's middle block activation. + """ + + down_block_res_samples: Tuple[torch.Tensor] + mid_block_res_sample: torch.Tensor + + +class ControlNetConditioningEmbeddingSVD(nn.Module): + """ + Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN + [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized + training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the + convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides + (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full + model) to encode image-space conditions ... into feature maps ..." + """ + + def __init__( + self, + conditioning_embedding_channels: int, + conditioning_channels: int = 3, + block_out_channels: Tuple[int, ...] = (16, 32, 96, 256), + ): + super().__init__() + self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1) + + self.blocks = nn.ModuleList([]) + + for i in range(len(block_out_channels) - 1): + channel_in = block_out_channels[i] + channel_out = block_out_channels[i + 1] + self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1)) + self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2)) + + self.conv_out = zero_module( + nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1) + ) + + def forward(self, conditioning): + #this seeems appropriate? idk if i should be applying a more complex setup to handle the frames + #combine batch and frames dimensions + batch_size, frames, channels, height, width = conditioning.size() + conditioning = conditioning.view(batch_size * frames, channels, height, width) + + embedding = self.conv_in(conditioning) + embedding = F.silu(embedding) + + for block in self.blocks: + embedding = block(embedding) + embedding = F.silu(embedding) + + embedding = self.conv_out(embedding) + + # # split them apart again + # # actually not needed + # new_channels, new_height, new_width = embedding.shape[1], embedding.shape[2], embedding.shape[3] + # embedding = embedding.view(batch_size, frames, new_channels, new_height, new_width) + + return embedding + + +class ControlNetSVDModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin): + r""" + A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): + The tuple of downsample blocks to use. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): + The tuple of upsample blocks to use. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + addition_time_embed_dim: (`int`, defaults to 256): + Dimension to to encode the additional time ids. + projection_class_embeddings_input_dim (`int`, defaults to 768): + The dimension of the projection of encoded `added_time_ids`. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], + [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. + num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): + The number of attention heads. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 8, + out_channels: int = 4, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "DownBlockSpatioTemporal", + ), + up_block_types: Tuple[str] = ( + "UpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + ), + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + addition_time_embed_dim: int = 256, + projection_class_embeddings_input_dim: int = 768, + layers_per_block: Union[int, Tuple[int]] = 2, + cross_attention_dim: Union[int, Tuple[int]] = 1024, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), + num_frames: int = 25, + conditioning_channels: int = 3, + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + # NOTE: adapter for dift feature + with_id_feature: bool = False, + feature_channels: int = 160, + feature_out_channels: Tuple[int, ...] = (160, 160, 256, 256), + ): + super().__init__() + self.sample_size = sample_size + + print("layers per block is", layers_per_block) + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + # input + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=3, + padding=1, + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + self.down_blocks = nn.ModuleList([]) + self.controlnet_down_blocks = nn.ModuleList([]) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + blocks_time_embed_dim = time_embed_dim + self.controlnet_cond_embedding = ControlNetConditioningEmbeddingSVD( + conditioning_embedding_channels=block_out_channels[0], + block_out_channels=conditioning_embedding_out_channels, + conditioning_channels=conditioning_channels, + ) + + # down + output_channel = block_out_channels[0] + controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + controlnet_block = zero_module(controlnet_block) + self.controlnet_down_blocks.append(controlnet_block) + + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=1e-5, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + resnet_act_fn="silu", + ) + self.down_blocks.append(down_block) + + for _ in range(layers_per_block[i]): + controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + controlnet_block = zero_module(controlnet_block) + self.controlnet_down_blocks.append(controlnet_block) + + if not is_final_block: + controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) + controlnet_block = zero_module(controlnet_block) + self.controlnet_down_blocks.append(controlnet_block) + + # mid + mid_block_channel = block_out_channels[-1] + controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) + controlnet_block = zero_module(controlnet_block) + self.controlnet_mid_block = controlnet_block + + self.mid_block = UNetMidBlockSpatioTemporal( + block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + transformer_layers_per_block=transformer_layers_per_block[-1], + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + ) + + # # out + # self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) + # self.conv_act = nn.SiLU() + + # self.conv_out = nn.Conv2d( + # block_out_channels[0], + # out_channels, + # kernel_size=3, + # padding=1, + # ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking + def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: + """ + Sets the attention processor to use [feed forward + chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). + + Parameters: + chunk_size (`int`, *optional*): + The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually + over each tensor of dim=`dim`. + dim (`int`, *optional*, defaults to `0`): + The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) + or dim=1 (sequence length). + """ + if dim not in [0, 1]: + raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") + + # By default chunk size is 1 + chunk_size = chunk_size or 1 + + def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, chunk_size, dim) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + added_time_ids: torch.Tensor, + controlnet_cond: torch.FloatTensor = None, + image_only_indicator: Optional[torch.Tensor] = None, + return_dict: bool = True, + guess_mode: bool = False, + conditioning_scale: float = 1.0, + ) -> Union[ControlNetOutput, Tuple]: + r""" + The [`UNetSpatioTemporalConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. + added_time_ids: (`torch.FloatTensor`): + The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal + embeddings and added to the time embeddings. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain + tuple. + Returns: + [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + batch_size, num_frames = sample.shape[:2] + timesteps = timesteps.expand(batch_size) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(added_time_ids.flatten()) + time_embeds = time_embeds.reshape((batch_size, -1)) + time_embeds = time_embeds.to(emb.dtype) + aug_emb = self.add_embedding(time_embeds) + emb = emb + aug_emb + + # Flatten the batch and frames dimensions + # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] + sample = sample.flatten(0, 1) + # Repeat the embeddings num_video_frames times + # emb: [batch, channels] -> [batch * frames, channels] + emb = emb.repeat_interleave(num_frames, dim=0) + # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] + encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) + + # 2. pre-process + sample = self.conv_in(sample) + + # controlnet cond + if controlnet_cond != None: + controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) + sample = sample + controlnet_cond + + image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) + + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # print('has_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + ) + else: + # print('no_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.DownBlockSpatioTemporal + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + image_only_indicator=image_only_indicator, + ) + + down_block_res_samples += res_samples + + # 4. mid + sample = self.mid_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + ) + + controlnet_down_block_res_samples = () + + for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks): + down_block_res_sample = controlnet_block(down_block_res_sample) + controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = controlnet_down_block_res_samples + + mid_block_res_sample = self.controlnet_mid_block(sample) + + # 6. scaling + + down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples] + mid_block_res_sample = mid_block_res_sample * conditioning_scale + + if not return_dict: + return (down_block_res_samples, mid_block_res_sample) + + return ControlNetOutput( + down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample + ) + + @classmethod + def from_unet( + cls, + unet: UNetSpatioTemporalConditionModel, + # controlnet_conditioning_channel_order: str = "rgb", + conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), + load_weights_from_unet: bool = True, + conditioning_channels: int = 3, + ): + r""" + Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. + + Parameters: + unet (`UNet2DConditionModel`): + The UNet model weights to copy to the [`ControlNetModel`]. All configuration options are also copied + where applicable. + """ + + # transformer_layers_per_block = ( + # unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 + # ) + # encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None + # encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None + # addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None + # addition_time_embed_dim = ( + # unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None + # ) + print(unet.config) + controlnet = cls( + in_channels=unet.config.in_channels, + down_block_types=unet.config.down_block_types, + block_out_channels=unet.config.block_out_channels, + addition_time_embed_dim=unet.config.addition_time_embed_dim, + transformer_layers_per_block=unet.config.transformer_layers_per_block, + cross_attention_dim=unet.config.cross_attention_dim, + num_attention_heads=unet.config.num_attention_heads, + num_frames=unet.config.num_frames, + sample_size=unet.config.sample_size, # Added based on the dict + layers_per_block=unet.config.layers_per_block, + projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, + conditioning_channels = conditioning_channels, + conditioning_embedding_out_channels = conditioning_embedding_out_channels, + ) + # controlnet rgb channel order ignored, set to not makea difference by default + + if load_weights_from_unet: + controlnet.conv_in.load_state_dict(unet.conv_in.state_dict()) + controlnet.time_proj.load_state_dict(unet.time_proj.state_dict()) + controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) + + # if controlnet.class_embedding: + # controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) + + controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict()) + controlnet.mid_block.load_state_dict(unet.mid_block.state_dict()) + + return controlnet + + @property + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor( + self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False + ): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor, _remove_lora=_remove_lora) + else: + module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnAddedKVProcessor() + elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor, _remove_lora=True) + + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice + def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module splits the input tensor in slices to compute attention in + several steps. This is useful for saving some memory in exchange for a small decrease in speed. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If + `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_sliceable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_sliceable_dims(module) + + num_sliceable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_sliceable_layers * [1] + + slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + # def _set_gradient_checkpointing(self, module, value: bool = False) -> None: + # if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): + # module.gradient_checkpointing = value + + +def zero_module(module): + for p in module.parameters(): + nn.init.zeros_(p) + return module diff --git a/models_diffusers/mutual_self_attention.py b/models_diffusers/mutual_self_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..c067a387db032f21782c47f911e3fc5f8e6a2895 --- /dev/null +++ b/models_diffusers/mutual_self_attention.py @@ -0,0 +1,442 @@ +# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py +from typing import Any, Dict, Optional + +import torch +from einops import rearrange +from models_diffusers.camera.attention import TemporalPoseCondTransformerBlock as TemporalBasicTransformerBlock +from diffusers.models.attention import BasicTransformerBlock +from torch import nn + +def torch_dfs(model: torch.nn.Module): + result = [model] + for child in model.children(): + result += torch_dfs(child) + return result + +def _chunked_feed_forward( + ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None +): + # "feed_forward_chunk_size" can be used to save memory + if hidden_states.shape[chunk_dim] % chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = hidden_states.shape[chunk_dim] // chunk_size + if lora_scale is None: + ff_output = torch.cat( + [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], + dim=chunk_dim, + ) + else: + # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete + ff_output = torch.cat( + [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], + dim=chunk_dim, + ) + + return ff_output + + +class ReferenceAttentionControl: + def __init__( + self, + unet, + mode="write", + do_classifier_free_guidance=False, + attention_auto_machine_weight=float("inf"), + gn_auto_machine_weight=1.0, + style_fidelity=1.0, + reference_attn=True, + reference_adain=False, + fusion_blocks="midup", + batch_size=1, + ) -> None: + # 10. Modify self attention and group norm + self.unet = unet + assert mode in ["read", "write"] + assert fusion_blocks in ["midup", "full"] + self.reference_attn = reference_attn + self.reference_adain = reference_adain + self.fusion_blocks = fusion_blocks + self.register_reference_hooks( + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + fusion_blocks, + batch_size=batch_size, + ) + + def register_reference_hooks( + self, + mode, + do_classifier_free_guidance, + attention_auto_machine_weight, + gn_auto_machine_weight, + style_fidelity, + reference_attn, + reference_adain, + dtype=torch.float16, + batch_size=1, + num_images_per_prompt=1, + device=torch.device("cpu"), + fusion_blocks="midup", + ): + MODE = mode + do_classifier_free_guidance = do_classifier_free_guidance + attention_auto_machine_weight = attention_auto_machine_weight + gn_auto_machine_weight = gn_auto_machine_weight + style_fidelity = style_fidelity + reference_attn = reference_attn + reference_adain = reference_adain + fusion_blocks = fusion_blocks + num_images_per_prompt = num_images_per_prompt + dtype = dtype + if do_classifier_free_guidance: + uc_mask = ( + torch.Tensor( + [1] * batch_size * num_images_per_prompt * 16 + + [0] * batch_size * num_images_per_prompt * 16 + ) + .to(device) + .bool() + ) + else: + uc_mask = ( + torch.Tensor([0] * batch_size * num_images_per_prompt * 2) + .to(device) + .bool() + ) + + def hacked_basic_transformer_inner_forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + video_length=None, + self_attention_additional_feats=None, + mode=None, + ): + batch_size = hidden_states.shape[0] + + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + elif self.use_layer_norm: + norm_hidden_states = self.norm1(hidden_states) + elif self.use_ada_layer_norm_single: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) + ).chunk(6, dim=1) + norm_hidden_states = self.norm1(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa + norm_hidden_states = norm_hidden_states.squeeze(1) + else: + raise ValueError("Incorrect norm used") + + if self.pos_embed is not None: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + # 1. Retrieve lora scale. + lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 + + # 2. Prepare GLIGEN inputs + cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} + gligen_kwargs = cross_attention_kwargs.pop("gligen", None) + + if self.only_cross_attention: + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + else: + if MODE == "write": + # print("this is write") + self.bank.append(norm_hidden_states.clone()) + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states + if self.only_cross_attention + else None, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + + if MODE == "read": + # bank_fea = [ + # rearrange( + # d.unsqueeze(1).repeat(1, video_length, 1, 1), + # "b t l c -> (b t) l c", + # ) + # for d in self.bank + # ] + bank_fea=[] + for d in self.bank: + if d.shape[0]==1: + bank_fea.append(d.repeat(norm_hidden_states.shape[0],1,1)) + else: + bank_fea.append(d) + + modify_norm_hidden_states = torch.cat( + [norm_hidden_states] + bank_fea, dim=1 + ) + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=modify_norm_hidden_states, + attention_mask=attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + elif self.use_ada_layer_norm_single: + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 2.5 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + # 3. Cross-Attention + if self.attn2 is not None: + if self.use_ada_layer_norm: + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.use_ada_layer_norm_zero or self.use_layer_norm: + norm_hidden_states = self.norm2(hidden_states) + elif self.use_ada_layer_norm_single: + # For PixArt norm2 isn't applied here: + # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 + norm_hidden_states = hidden_states + else: + raise ValueError("Incorrect norm") + + if self.pos_embed is not None and self.use_ada_layer_norm_single is False: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + if not self.use_ada_layer_norm_single: + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self.use_ada_layer_norm_single: + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + ff_output = _chunked_feed_forward( + self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale + ) + else: + ff_output = self.ff(norm_hidden_states, scale=lora_scale) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.use_ada_layer_norm_single: + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + + elif self.use_ada_layer_norm_single: + attn_output = gate_msa * attn_output + + hidden_states = attn_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + # 2.5 GLIGEN Control + if gligen_kwargs is not None: + hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) + + # 3. Cross-Attention + if self.attn2 is not None: + if self.use_ada_layer_norm: + norm_hidden_states = self.norm2(hidden_states, timestep) + elif self.use_ada_layer_norm_zero or self.use_layer_norm: + norm_hidden_states = self.norm2(hidden_states) + elif self.use_ada_layer_norm_single: + # For PixArt norm2 isn't applied here: + # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 + norm_hidden_states = hidden_states + else: + raise ValueError("Incorrect norm") + + if self.pos_embed is not None and self.use_ada_layer_norm_single is False: + norm_hidden_states = self.pos_embed(norm_hidden_states) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 4. Feed-forward + if not self.use_ada_layer_norm_single: + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self.use_ada_layer_norm_single: + norm_hidden_states = self.norm2(hidden_states) + norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + ff_output = _chunked_feed_forward( + self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale + ) + else: + ff_output = self.ff(norm_hidden_states, scale=lora_scale) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + elif self.use_ada_layer_norm_single: + ff_output = gate_mlp * ff_output + + hidden_states = ff_output + hidden_states + if hidden_states.ndim == 4: + hidden_states = hidden_states.squeeze(1) + + return hidden_states + + if self.reference_attn: + if self.fusion_blocks == "midup": + attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + # or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + # or isinstance(module, TemporalBasicTransformerBlock) + ] + attn_modules = sorted( + attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + + for i, module in enumerate(attn_modules): + module._original_inner_forward = module.forward + if isinstance(module, BasicTransformerBlock): + module.forward = hacked_basic_transformer_inner_forward.__get__( + module, BasicTransformerBlock + ) + # if isinstance(module, TemporalBasicTransformerBlock): + # module.forward = hacked_basic_transformer_inner_forward.__get__( + # module, TemporalBasicTransformerBlock + # ) + + module.bank = [] + module.attn_weight = float(i) / float(len(attn_modules)) + + def update(self, writer, dtype=torch.float16): + if self.reference_attn: + + + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in ( + torch_dfs(writer.unet.mid_block) + + torch_dfs(writer.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + # reader_attn_modules = [ + # module + # for module in torch_dfs(self.unet) + # if isinstance(module, TemporalBasicTransformerBlock) + # ] + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + ] + writer_attn_modules = [ + module + for module in torch_dfs(writer.unet) + if isinstance(module, BasicTransformerBlock) + ] + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + writer_attn_modules = sorted( + writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r, w in zip(reader_attn_modules, writer_attn_modules): + r.bank = [v.clone().to(dtype) for v in w.bank] + # w.bank.clear() + + def clear(self): + if self.reference_attn: + if self.fusion_blocks == "midup": + reader_attn_modules = [ + module + for module in ( + torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks) + ) + if isinstance(module, BasicTransformerBlock) + # or isinstance(module, TemporalBasicTransformerBlock) + ] + elif self.fusion_blocks == "full": + reader_attn_modules = [ + module + for module in torch_dfs(self.unet) + if isinstance(module, BasicTransformerBlock) + # or isinstance(module, TemporalBasicTransformerBlock) + ] + reader_attn_modules = sorted( + reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0] + ) + for r in reader_attn_modules: + r.bank.clear() diff --git a/models_diffusers/refUnet_spatial_temporal_condition.py b/models_diffusers/refUnet_spatial_temporal_condition.py new file mode 100644 index 0000000000000000000000000000000000000000..95b03e36efa4946b7fe9f28d4547152658a64acb --- /dev/null +++ b/models_diffusers/refUnet_spatial_temporal_condition.py @@ -0,0 +1,1077 @@ +from dataclasses import dataclass +from typing import Dict, Optional, Tuple, Union +from einops import rearrange + +import torch +import torch.nn as nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +# from diffusers.models.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block +from models_diffusers.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block + + +import inspect +import itertools +import os +import re +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, Optional, Tuple, Union + +from diffusers import __version__ +from diffusers.utils import ( + CONFIG_NAME, + DIFFUSERS_CACHE, + FLAX_WEIGHTS_NAME, + HF_HUB_OFFLINE, + MIN_PEFT_VERSION, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, + _add_variant, + _get_model_file, + check_peft_version, + deprecate, + is_accelerate_available, + is_torch_version, + logging, +) +from diffusers.utils.hub_utils import PushToHubMixin +from diffusers.models.modeling_utils import load_model_dict_into_meta, load_state_dict + +if is_torch_version(">=", "1.9.0"): + _LOW_CPU_MEM_USAGE_DEFAULT = True +else: + _LOW_CPU_MEM_USAGE_DEFAULT = False + +if is_accelerate_available(): + import accelerate + from accelerate.utils import set_module_tensor_to_device + from accelerate.utils.versions import is_torch_version + +from models_diffusers.camera.attention_processor import XFormersAttnProcessor as CustomizedXFormerAttnProcessor +from models_diffusers.camera.attention_processor import PoseAdaptorXFormersAttnProcessor + +# if hasattr(F, "scaled_dot_product_attention"): +# from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor2_0 as PoseAdaptorAttnProcessor +# from models_diffusers.camera.attention_processor import AttnProcessor2_0 as CustomizedAttnProcessor +# else: +from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor +from models_diffusers.camera.attention_processor import AttnProcessor as CustomizedAttnProcessor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNetSpatioTemporalConditionOutput(BaseOutput): + """ + The output of [`UNetSpatioTemporalConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): + The tuple of downsample blocks to use. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): + The tuple of upsample blocks to use. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + addition_time_embed_dim: (`int`, defaults to 256): + Dimension to to encode the additional time ids. + projection_class_embeddings_input_dim (`int`, defaults to 768): + The dimension of the projection of encoded `added_time_ids`. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], + [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. + num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): + The number of attention heads. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 8, + out_channels: int = 4, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "DownBlockSpatioTemporal", + ), + up_block_types: Tuple[str] = ( + "UpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + ), + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + addition_time_embed_dim: int = 256, + projection_class_embeddings_input_dim: int = 768, + layers_per_block: Union[int, Tuple[int]] = 2, + cross_attention_dim: Union[int, Tuple[int]] = 1024, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), + num_frames: int = 25, + ): + super().__init__() + + self.sample_size = sample_size + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + self.mask_token = nn.Parameter(torch.randn(1, 1, 4, 1, 1)) + + # input + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=3, + padding=1, + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=1e-5, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + resnet_act_fn="silu", + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlockSpatioTemporal( + block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + transformer_layers_per_block=transformer_layers_per_block[-1], + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=1e-5, + resolution_idx=i, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + resnet_act_fn="silu", + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) + self.conv_act = nn.SiLU() + + self.conv_out = nn.Conv2d( + block_out_channels[0], + out_channels, + kernel_size=3, + padding=1, + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking + def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: + """ + Sets the attention processor to use [feed forward + chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). + + Parameters: + chunk_size (`int`, *optional*): + The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually + over each tensor of dim=`dim`. + dim (`int`, *optional*, defaults to `0`): + The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) + or dim=1 (sequence length). + """ + if dim not in [0, 1]: + raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") + + # By default chunk size is 1 + chunk_size = chunk_size or 1 + + def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, chunk_size, dim) + + def set_pose_cond_attn_processor( + self, + add_spatial=False, + add_temporal=False, + enable_xformers=False, + attn_processor_name='attn1', + pose_feature_dimensions=[320, 640, 1280, 1280], + **attention_processor_kwargs, + ): + all_attn_processors = {} + set_processor_names = attn_processor_name.split(',') + if add_spatial: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorXFormersAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' not in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + if add_temporal: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = (processor_name in set_processor_names) + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + self.set_attn_processor(all_attn_processors) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + added_time_ids: torch.Tensor, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, # for t2i-adaptor or controlnet + mid_block_additional_residual: Optional[torch.Tensor] = None, # for controlnet + pose_features: List[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]: + r""" + The [`UNetSpatioTemporalConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. + added_time_ids: (`torch.FloatTensor`): + The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal + embeddings and added to the time embeddings. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain + tuple. + Returns: + [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + batch_size, num_frames = sample.shape[:2] + timesteps = timesteps.expand(batch_size) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(added_time_ids.flatten()) + time_embeds = time_embeds.reshape((batch_size, -1)) + time_embeds = time_embeds.to(emb.dtype) + aug_emb = self.add_embedding(time_embeds) + emb = emb + aug_emb + + # Flatten the batch and frames dimensions + # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] + sample = sample.flatten(0, 1) + # Repeat the embeddings num_video_frames times + # emb: [batch, channels] -> [batch * frames, channels] + emb = emb.repeat_interleave(num_frames, dim=0) + # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] + encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) + + # 2. pre-process + sample = self.conv_in(sample) + + image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) + + is_adapter = is_controlnet = False + if (down_block_additional_residuals is not None): + if (mid_block_additional_residual is not None): + is_controlnet = True + else: + is_adapter = True + + down_block_res_samples = (sample,) + for block_idx, downsample_block in enumerate(self.down_blocks): + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # print('has_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal + + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals['additional_residuals'] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[block_idx] if pose_features is not None else None, + **additional_residuals, + ) + else: + # print('no_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.DownBlockSpatioTemporal + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + image_only_indicator=image_only_indicator, + ) + + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals = down_block_additional_residuals.pop(0) + if sample.dim() == 5: + additional_residuals = rearrange(additional_residuals, '(b f) c h w -> b c f h w', b=sample.shape[0]) + sample = sample + additional_residuals + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip(down_block_res_samples, down_block_additional_residuals): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + sample = self.mid_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-1] if pose_features is not None else None, + ) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for block_idx, upsample_block in enumerate(self.up_blocks): + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-(block_idx + 1)] if pose_features is not None else None, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + image_only_indicator=image_only_indicator, + ) + + # 6. post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + # 7. Reshape back to original shape + sample = sample.reshape(batch_size, num_frames, *sample.shape[1:]) + + if not return_dict: + return (sample,) + + return UNetSpatioTemporalConditionOutput(sample=sample) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], custom_resume=False, **kwargs): + r""" + Instantiate a pretrained PyTorch model from a pretrained model configuration. + + The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To + train the model, set it back in training mode with `model.train()`. + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): + Can be either: + + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on + the Hub. + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved + with [`~ModelMixin.save_pretrained`]. + + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache + is not used. + torch_dtype (`str` or `torch.dtype`, *optional*): + Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the + dtype is automatically derived from the model's weights. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any + incompletely downloaded files are deleted. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + output_loading_info (`bool`, *optional*, defaults to `False`): + Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether to only load local model weights and configuration files or not. If set to `True`, the model + won't be downloaded from the Hub. + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from + `diffusers-cli login` (stored in `~/.huggingface`) is used. + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier + allowed by Git. + from_flax (`bool`, *optional*, defaults to `False`): + Load the model weights from a Flax checkpoint save file. + subfolder (`str`, *optional*, defaults to `""`): + The subfolder location of a model file within a larger model repository on the Hub or locally. + mirror (`str`, *optional*): + Mirror source to resolve accessibility issues if you're downloading a model in China. We do not + guarantee the timeliness or safety of the source, and you should refer to the mirror site for more + information. + device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): + A map that specifies where each submodule should go. It doesn't need to be defined for each + parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the + same device. + + Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For + more information about each option see [designing a device + map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). + max_memory (`Dict`, *optional*): + A dictionary device identifier for the maximum memory. Will default to the maximum memory available for + each GPU and the available CPU RAM if unset. + offload_folder (`str` or `os.PathLike`, *optional*): + The path to offload weights if `device_map` contains the value `"disk"`. + offload_state_dict (`bool`, *optional*): + If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if + the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` + when there is some disk offload. + low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): + Speed up model loading only loading the pretrained weights and not initializing the weights. This also + tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. + Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this + argument to `True` will raise an error. + variant (`str`, *optional*): + Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when + loading `from_flax`. + use_safetensors (`bool`, *optional*, defaults to `None`): + If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the + `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` + weights. If set to `False`, `safetensors` weights are not loaded. + + + + To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with + `huggingface-cli login`. You can also activate the special + ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a + firewalled environment. + + + + Example: + + ```py + from diffusers import UNet2DConditionModel + + unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + ``` + + If you get the error message below, you need to finetune the weights for your downstream task: + + ```bash + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated + You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. + ``` + """ + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) + ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) + force_download = kwargs.pop("force_download", False) + from_flax = kwargs.pop("from_flax", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + output_loading_info = kwargs.pop("output_loading_info", False) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + torch_dtype = kwargs.pop("torch_dtype", None) + subfolder = kwargs.pop("subfolder", None) + device_map = kwargs.pop("device_map", None) + max_memory = kwargs.pop("max_memory", None) + offload_folder = kwargs.pop("offload_folder", None) + offload_state_dict = kwargs.pop("offload_state_dict", False) + low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) + variant = kwargs.pop("variant", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = True + allow_pickle = True + + if low_cpu_mem_usage and not is_accelerate_available(): + low_cpu_mem_usage = False + logger.warning( + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" + " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" + " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" + " install accelerate\n```\n." + ) + + if device_map is not None and not is_accelerate_available(): + raise NotImplementedError( + "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" + " `device_map=None`. You can install accelerate with `pip install accelerate`." + ) + + # Check if we can handle device_map and dispatching the weights + if device_map is not None and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `device_map=None`." + ) + + if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `low_cpu_mem_usage=False`." + ) + + if low_cpu_mem_usage is False and device_map is not None: + raise ValueError( + f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" + " dispatching. Please make sure to set `low_cpu_mem_usage=True`." + ) + + # Load config if we don't provide a configuration + config_path = pretrained_model_name_or_path + + user_agent = { + "diffusers": __version__, + "file_type": "model", + "framework": "pytorch", + } + + # load config + config, unused_kwargs, commit_hash = cls.load_config( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + return_commit_hash=True, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + device_map=device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + user_agent=user_agent, + **kwargs, + ) + + if not custom_resume: + # NOTE: update in_channels, for additional mask concatentation + config['in_channels'] = config['in_channels'] + 1 + + # load model + model_file = None + if from_flax: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=FLAX_WEIGHTS_NAME, + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + model = cls.from_config(config, **unused_kwargs) + + # Convert the weights + from diffusers.models.modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model + + model = load_flax_checkpoint_in_pytorch_model(model, model_file) + else: + if use_safetensors: + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + except IOError as e: + if not allow_pickle: + raise e + pass + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + + if low_cpu_mem_usage: + # Instantiate model with empty weights + with accelerate.init_empty_weights(): + model = cls.from_config(config, **unused_kwargs) + + # if device_map is None, load the state dict and move the params from meta device to the cpu + if device_map is None: + param_device = "cpu" + state_dict = load_state_dict(model_file, variant=variant) + + if not custom_resume: + # NOTE update conv_in_weight + conv_in_weight = state_dict['conv_in.weight'] + assert conv_in_weight.shape == (320, 8, 3, 3) + conv_in_weight_new = torch.randn(320, 9, 3, 3).to(conv_in_weight.device).to(conv_in_weight.dtype) + conv_in_weight_new[:, :8, :, :] = conv_in_weight + state_dict['conv_in.weight'] = conv_in_weight_new + + # NOTE add mask_token + mask_token = torch.randn(1, 1, 4, 1, 1).to(conv_in_weight.device).to(conv_in_weight.dtype) + state_dict["mask_token"] = mask_token + + model._convert_deprecated_attention_blocks(state_dict) + # move the params from meta device to cpu + missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) + if len(missing_keys) > 0: + raise ValueError( + f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are" + f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass" + " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize" + " those weights or else make sure your checkpoint file is correct." + ) + + unexpected_keys = load_model_dict_into_meta( + model, + state_dict, + device=param_device, + dtype=torch_dtype, + model_name_or_path=pretrained_model_name_or_path, + ) + + if cls._keys_to_ignore_on_load_unexpected is not None: + for pat in cls._keys_to_ignore_on_load_unexpected: + unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] + + if len(unexpected_keys) > 0: + logger.warn( + f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}" + ) + + else: # else let accelerate handle loading and dispatching. + # Load weights and dispatch according to the device_map + # by default the device_map is None and the weights are loaded on the CPU + try: + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + except AttributeError as e: + # When using accelerate loading, we do not have the ability to load the state + # dict and rename the weight names manually. Additionally, accelerate skips + # torch loading conventions and directly writes into `module.{_buffers, _parameters}` + # (which look like they should be private variables?), so we can't use the standard hooks + # to rename parameters on load. We need to mimic the original weight names so the correct + # attributes are available. After we have loaded the weights, we convert the deprecated + # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert + # the weights so we don't have to do this again. + + if "'Attention' object has no attribute" in str(e): + logger.warn( + f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}" + " was saved with deprecated attention block weight names. We will load it with the deprecated attention block" + " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion," + " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint," + " please also re-upload it or open a PR on the original repository." + ) + model._temp_convert_self_to_deprecated_attention_blocks() + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + model._undo_temp_convert_self_to_deprecated_attention_blocks() + else: + raise e + + loading_info = { + "missing_keys": [], + "unexpected_keys": [], + "mismatched_keys": [], + "error_msgs": [], + } + else: + model = cls.from_config(config, **unused_kwargs) + + state_dict = load_state_dict(model_file, variant=variant) + model._convert_deprecated_attention_blocks(state_dict) + + model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( + model, + state_dict, + model_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=ignore_mismatched_sizes, + ) + + loading_info = { + "missing_keys": missing_keys, + "unexpected_keys": unexpected_keys, + "mismatched_keys": mismatched_keys, + "error_msgs": error_msgs, + } + + if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." + ) + elif torch_dtype is not None: + model = model.to(torch_dtype) + + model.register_to_config(_name_or_path=pretrained_model_name_or_path) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + if output_loading_info: + return model, loading_info + + return model diff --git a/models_diffusers/transformer_temporal.py b/models_diffusers/transformer_temporal.py new file mode 100644 index 0000000000000000000000000000000000000000..11906f9ef2a7c00316cc0f5c0aa46fda69922752 --- /dev/null +++ b/models_diffusers/transformer_temporal.py @@ -0,0 +1,386 @@ +# Copyright 2023 The HuggingFace Team. 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 dataclasses import dataclass +from typing import Any, Dict, Optional + +import torch +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.utils import BaseOutput +# from diffusers.models.attention import BasicTransformerBlock, TemporalBasicTransformerBlock +from diffusers.models.attention import BasicTransformerBlock +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.resnet import AlphaBlender + +from models_diffusers.camera.attention import TemporalPoseCondTransformerBlock as TemporalBasicTransformerBlock + + +@dataclass +class TransformerTemporalModelOutput(BaseOutput): + """ + The output of [`TransformerTemporalModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. + """ + + sample: torch.FloatTensor + + +class TransformerTemporalModel(ModelMixin, ConfigMixin): + """ + A Transformer model for video-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + attention_bias (`bool`, *optional*): + Configure if the `TransformerBlock` attention should contain a bias parameter. + sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). + This is fixed during training since it is used to learn a number of position embeddings. + activation_fn (`str`, *optional*, defaults to `"geglu"`): + Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported + activation functions. + norm_elementwise_affine (`bool`, *optional*): + Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization. + double_self_attention (`bool`, *optional*): + Configure if each `TransformerBlock` should contain two self-attention layers. + positional_embeddings: (`str`, *optional*): + The type of positional embeddings to apply to the sequence input before passing use. + num_positional_embeddings: (`int`, *optional*): + The maximum length of the sequence over which to apply positional embeddings. + """ + + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + sample_size: Optional[int] = None, + activation_fn: str = "geglu", + norm_elementwise_affine: bool = True, + double_self_attention: bool = True, + positional_embeddings: Optional[str] = None, + num_positional_embeddings: Optional[int] = None, + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + double_self_attention=double_self_attention, + norm_elementwise_affine=norm_elementwise_affine, + positional_embeddings=positional_embeddings, + num_positional_embeddings=num_positional_embeddings, + ) + for d in range(num_layers) + ] + ) + + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward( + self, + hidden_states: torch.FloatTensor, + encoder_hidden_states: Optional[torch.LongTensor] = None, + timestep: Optional[torch.LongTensor] = None, + class_labels: torch.LongTensor = None, + num_frames: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = True, + ) -> TransformerTemporalModelOutput: + """ + The [`TransformerTemporal`] forward method. + + Args: + hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): + Input hidden_states. + encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + timestep ( `torch.LongTensor`, *optional*): + Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. + class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): + Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in + `AdaLayerZeroNorm`. + num_frames (`int`, *optional*, defaults to 1): + The number of frames to be processed per batch. This is used to reshape the hidden states. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain + tuple. + + Returns: + [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`: + If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is + returned, otherwise a `tuple` where the first element is the sample tensor. + """ + # 1. Input + batch_frames, channel, height, width = hidden_states.shape + batch_size = batch_frames // num_frames + + residual = hidden_states + + hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width) + hidden_states = hidden_states.permute(0, 2, 1, 3, 4) + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel) + + hidden_states = self.proj_in(hidden_states) + + # 2. Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + cross_attention_kwargs=cross_attention_kwargs, + class_labels=class_labels, + ) + + # 3. Output + hidden_states = self.proj_out(hidden_states) + hidden_states = ( + hidden_states[None, None, :] + .reshape(batch_size, height, width, num_frames, channel) + .permute(0, 3, 4, 1, 2) + .contiguous() + ) + hidden_states = hidden_states.reshape(batch_frames, channel, height, width) + + output = hidden_states + residual + + if not return_dict: + return (output,) + + return TransformerTemporalModelOutput(sample=output) + + +class TransformerSpatioTemporalModel(nn.Module): + """ + A Transformer model for video-like data. + + Parameters: + num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. + attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. + in_channels (`int`, *optional*): + The number of channels in the input and output (specify if the input is **continuous**). + out_channels (`int`, *optional*): + The number of channels in the output (specify if the input is **continuous**). + num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. + cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. + """ + + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: int = 320, + out_channels: Optional[int] = None, + num_layers: int = 1, + cross_attention_dim: Optional[int] = None, + ): + super().__init__() + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + + inner_dim = num_attention_heads * attention_head_dim + self.inner_dim = inner_dim + + # 2. Define input layers + self.in_channels = in_channels + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6) + self.proj_in = nn.Linear(in_channels, inner_dim) + + # 3. Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + ) + for d in range(num_layers) + ] + ) + + time_mix_inner_dim = inner_dim + self.temporal_transformer_blocks = nn.ModuleList( + [ + TemporalBasicTransformerBlock( + inner_dim, + time_mix_inner_dim, + num_attention_heads, + attention_head_dim, + cross_attention_dim=cross_attention_dim, + ) + for _ in range(num_layers) + ] + ) + + time_embed_dim = in_channels * 4 + self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels) + self.time_proj = Timesteps(in_channels, True, 0) + self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images") + + # 4. Define output layers + self.out_channels = in_channels if out_channels is None else out_channels + # TODO: should use out_channels for continuous projections + self.proj_out = nn.Linear(inner_dim, in_channels) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + pose_feature: Optional[torch.Tensor] = None, # [bs, c, frame, h, w] + return_dict: bool = True, + ): + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`): + Input hidden_states. + num_frames (`int`): + The number of frames to be processed per batch. This is used to reshape the hidden states. + encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): + Conditional embeddings for cross attention layer. If not given, cross-attention defaults to + self-attention. + image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*): + A tensor indicating whether the input contains only images. 1 indicates that the input contains only + images, 0 indicates that the input contains video frames. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain + tuple. + + Returns: + [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`: + If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is + returned, otherwise a `tuple` where the first element is the sample tensor. + """ + # 1. Input + batch_frames, _, height, width = hidden_states.shape + num_frames = image_only_indicator.shape[-1] + batch_size = batch_frames // num_frames + + time_context = encoder_hidden_states + time_context_first_timestep = time_context[None, :].reshape( + batch_size, num_frames, -1, time_context.shape[-1] + )[:, 0] + time_context = time_context_first_timestep[None, :].broadcast_to( + # height * width, batch_size, 1, time_context.shape[-1] + height * width, batch_size, -1, time_context.shape[-1] + ) + # time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1]) + time_context = time_context.reshape(height * width * batch_size, -1, time_context.shape[-1]) + + residual = hidden_states + + hidden_states = self.norm(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim) + hidden_states = self.proj_in(hidden_states) + + num_frames_emb = torch.arange(num_frames, device=hidden_states.device) + num_frames_emb = num_frames_emb.repeat(batch_size, 1) + num_frames_emb = num_frames_emb.reshape(-1) + t_emb = self.time_proj(num_frames_emb) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=hidden_states.dtype) + + emb = self.time_pos_embed(t_emb) + emb = emb[:, None, :] + + # 2. Blocks + for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks): + if self.training and self.gradient_checkpointing: + hidden_states = torch.utils.checkpoint.checkpoint( + block, + hidden_states, + None, + encoder_hidden_states, + None, + use_reentrant=False, + ) + else: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + ) + + hidden_states_mix = hidden_states + hidden_states_mix = hidden_states_mix + emb + + hidden_states_mix = temporal_block( + hidden_states_mix, + num_frames=num_frames, + encoder_hidden_states=time_context, + pose_feature=pose_feature, + ) + hidden_states = self.time_mixer( + x_spatial=hidden_states, + x_temporal=hidden_states_mix, + image_only_indicator=image_only_indicator, + ) + + # 3. Output + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + + if not return_dict: + return (output,) + + return TransformerTemporalModelOutput(sample=output) diff --git a/models_diffusers/unet_3d_blocks.py b/models_diffusers/unet_3d_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..238bad146eec373c0a7dbe76d1c9912b864571a1 --- /dev/null +++ b/models_diffusers/unet_3d_blocks.py @@ -0,0 +1,2413 @@ +# Copyright 2023 The HuggingFace Team. 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 typing import Any, Dict, Optional, Tuple, Union + +import torch +from torch import nn + +from diffusers.utils import is_torch_version +from diffusers.utils.torch_utils import apply_freeu +from diffusers.models.attention import Attention +from diffusers.models.dual_transformer_2d import DualTransformer2DModel +from diffusers.models.resnet import ( + Downsample2D, + ResnetBlock2D, + SpatioTemporalResBlock, + TemporalConvLayer, + Upsample2D, +) +from diffusers.models.transformer_2d import Transformer2DModel +from .transformer_temporal import ( + TransformerSpatioTemporalModel, + TransformerTemporalModel, +) + +from einops import rearrange + + +def get_down_block( + down_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + temb_channels: int, + add_downsample: bool, + resnet_eps: float, + resnet_act_fn: str, + num_attention_heads: int, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + downsample_padding: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = True, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + temporal_num_attention_heads: int = 8, + temporal_max_seq_length: int = 32, + transformer_layers_per_block: int = 1, +) -> Union[ + "DownBlock3D", + "CrossAttnDownBlock3D", + "DownBlockMotion", + "CrossAttnDownBlockMotion", + "DownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", +]: + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + ) + if down_block_type == "DownBlockMotion": + return DownBlockMotion( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + temporal_num_attention_heads=temporal_num_attention_heads, + temporal_max_seq_length=temporal_max_seq_length, + ) + elif down_block_type == "CrossAttnDownBlockMotion": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion") + return CrossAttnDownBlockMotion( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + temporal_num_attention_heads=temporal_num_attention_heads, + temporal_max_seq_length=temporal_max_seq_length, + ) + elif down_block_type == "DownBlockSpatioTemporal": + # added for SDV + return DownBlockSpatioTemporal( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + ) + elif down_block_type == "CrossAttnDownBlockSpatioTemporal": + # added for SDV + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal") + return CrossAttnDownBlockSpatioTemporal( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + add_downsample=add_downsample, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + ) + + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type: str, + num_layers: int, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + add_upsample: bool, + resnet_eps: float, + resnet_act_fn: str, + num_attention_heads: int, + resolution_idx: Optional[int] = None, + resnet_groups: Optional[int] = None, + cross_attention_dim: Optional[int] = None, + dual_cross_attention: bool = False, + use_linear_projection: bool = True, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + temporal_num_attention_heads: int = 8, + temporal_cross_attention_dim: Optional[int] = None, + temporal_max_seq_length: int = 32, + transformer_layers_per_block: int = 1, + dropout: float = 0.0, +) -> Union[ + "UpBlock3D", + "CrossAttnUpBlock3D", + "UpBlockMotion", + "CrossAttnUpBlockMotion", + "UpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", +]: + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + resolution_idx=resolution_idx, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resolution_idx=resolution_idx, + ) + if up_block_type == "UpBlockMotion": + return UpBlockMotion( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + resolution_idx=resolution_idx, + temporal_num_attention_heads=temporal_num_attention_heads, + temporal_max_seq_length=temporal_max_seq_length, + ) + elif up_block_type == "CrossAttnUpBlockMotion": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion") + return CrossAttnUpBlockMotion( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + resolution_idx=resolution_idx, + temporal_num_attention_heads=temporal_num_attention_heads, + temporal_max_seq_length=temporal_max_seq_length, + ) + elif up_block_type == "UpBlockSpatioTemporal": + # added for SDV + return UpBlockSpatioTemporal( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + resolution_idx=resolution_idx, + add_upsample=add_upsample, + ) + elif up_block_type == "CrossAttnUpBlockSpatioTemporal": + # added for SDV + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal") + return CrossAttnUpBlockSpatioTemporal( + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + num_layers=num_layers, + transformer_layers_per_block=transformer_layers_per_block, + add_upsample=add_upsample, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + resolution_idx=resolution_idx, + ) + + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + dual_cross_attention: bool = False, + use_linear_projection: bool = True, + upcast_attention: bool = False, + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + temp_convs = [ + TemporalConvLayer( + in_channels, + in_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ] + attentions = [] + temp_attentions = [] + + for _ in range(num_layers): + attentions.append( + Transformer2DModel( + in_channels // num_attention_heads, + num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + ) + ) + temp_attentions.append( + TransformerTemporalModel( + in_channels // num_attention_heads, + num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + temp_convs.append( + TemporalConvLayer( + in_channels, + in_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.temp_convs = nn.ModuleList(temp_convs) + self.attentions = nn.ModuleList(attentions) + self.temp_attentions = nn.ModuleList(temp_attentions) + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> torch.FloatTensor: + hidden_states = self.resnets[0](hidden_states, temb) + hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames) + for attn, temp_attn, resnet, temp_conv in zip( + self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:] + ): + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + hidden_states = temp_attn( + hidden_states, + num_frames=num_frames, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + hidden_states = resnet(hidden_states, temb) + hidden_states = temp_conv(hidden_states, num_frames=num_frames) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + add_downsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + ): + super().__init__() + resnets = [] + attentions = [] + temp_attentions = [] + temp_convs = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + temp_convs.append( + TemporalConvLayer( + out_channels, + out_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ) + attentions.append( + Transformer2DModel( + out_channels // num_attention_heads, + num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + ) + ) + temp_attentions.append( + TransformerTemporalModel( + out_channels // num_attention_heads, + num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.resnets = nn.ModuleList(resnets) + self.temp_convs = nn.ModuleList(temp_convs) + self.attentions = nn.ModuleList(attentions) + self.temp_attentions = nn.ModuleList(temp_attentions) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + cross_attention_kwargs: Dict[str, Any] = None, + ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + # TODO(Patrick, William) - attention mask is not used + output_states = () + + for resnet, temp_conv, attn, temp_attn in zip( + self.resnets, self.temp_convs, self.attentions, self.temp_attentions + ): + hidden_states = resnet(hidden_states, temb) + hidden_states = temp_conv(hidden_states, num_frames=num_frames) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + hidden_states = temp_attn( + hidden_states, + num_frames=num_frames, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + ): + super().__init__() + resnets = [] + temp_convs = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + temp_convs.append( + TemporalConvLayer( + out_channels, + out_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.temp_convs = nn.ModuleList(temp_convs) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + for resnet, temp_conv in zip(self.resnets, self.temp_convs): + hidden_states = resnet(hidden_states, temb) + hidden_states = temp_conv(hidden_states, num_frames=num_frames) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + resolution_idx: Optional[int] = None, + ): + super().__init__() + resnets = [] + temp_convs = [] + attentions = [] + temp_attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + temp_convs.append( + TemporalConvLayer( + out_channels, + out_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ) + attentions.append( + Transformer2DModel( + out_channels // num_attention_heads, + num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + ) + ) + temp_attentions.append( + TransformerTemporalModel( + out_channels // num_attention_heads, + num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + self.resnets = nn.ModuleList(resnets) + self.temp_convs = nn.ModuleList(temp_convs) + self.attentions = nn.ModuleList(attentions) + self.temp_attentions = nn.ModuleList(temp_attentions) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + cross_attention_kwargs: Dict[str, Any] = None, + ) -> torch.FloatTensor: + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + # TODO(Patrick, William) - attention mask is not used + for resnet, temp_conv, attn, temp_attn in zip( + self.resnets, self.temp_convs, self.attentions, self.temp_attentions + ): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + hidden_states = resnet(hidden_states, temb) + hidden_states = temp_conv(hidden_states, num_frames=num_frames) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + hidden_states = temp_attn( + hidden_states, + num_frames=num_frames, + cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, + )[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + resolution_idx: Optional[int] = None, + ): + super().__init__() + resnets = [] + temp_convs = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + temp_convs.append( + TemporalConvLayer( + out_channels, + out_channels, + dropout=0.1, + norm_num_groups=resnet_groups, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.temp_convs = nn.ModuleList(temp_convs) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size: Optional[int] = None, + num_frames: int = 1, + ) -> torch.FloatTensor: + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + for resnet, temp_conv in zip(self.resnets, self.temp_convs): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + hidden_states = resnet(hidden_states, temb) + hidden_states = temp_conv(hidden_states, num_frames=num_frames) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class DownBlockMotion(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_downsample: bool = True, + downsample_padding: int = 1, + temporal_num_attention_heads: int = 1, + temporal_cross_attention_dim: Optional[int] = None, + temporal_max_seq_length: int = 32, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + motion_modules.append( + TransformerTemporalModel( + num_attention_heads=temporal_num_attention_heads, + in_channels=out_channels, + norm_num_groups=resnet_groups, + cross_attention_dim=temporal_cross_attention_dim, + attention_bias=False, + activation_fn="geglu", + positional_embeddings="sinusoidal", + num_positional_embeddings=temporal_max_seq_length, + attention_head_dim=out_channels // temporal_num_attention_heads, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + scale: float = 1.0, + num_frames: int = 1, + ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + blocks = zip(self.resnets, self.motion_modules) + for resnet, motion_module in blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb, scale + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + num_frames, + ) + + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + hidden_states = motion_module(hidden_states, num_frames=num_frames)[0] + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnDownBlockMotion(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + downsample_padding: int = 1, + add_downsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + temporal_cross_attention_dim: Optional[int] = None, + temporal_num_attention_heads: int = 8, + temporal_max_seq_length: int = 32, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + + motion_modules.append( + TransformerTemporalModel( + num_attention_heads=temporal_num_attention_heads, + in_channels=out_channels, + norm_num_groups=resnet_groups, + cross_attention_dim=temporal_cross_attention_dim, + attention_bias=False, + activation_fn="geglu", + positional_embeddings="sinusoidal", + num_positional_embeddings=temporal_max_seq_length, + attention_head_dim=out_channels // temporal_num_attention_heads, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=downsample_padding, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + additional_residuals: Optional[torch.FloatTensor] = None, + ): + output_states = () + + lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 + + blocks = list(zip(self.resnets, self.attentions, self.motion_modules)) + for i, (resnet, attn, motion_module) in enumerate(blocks): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = motion_module( + hidden_states, + num_frames=num_frames, + )[0] + + # apply additional residuals to the output of the last pair of resnet and attention blocks + if i == len(blocks) - 1 and additional_residuals is not None: + hidden_states = hidden_states + additional_residuals + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states, scale=lora_scale) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlockMotion(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + attention_type: str = "default", + temporal_cross_attention_dim: Optional[int] = None, + temporal_num_attention_heads: int = 8, + temporal_max_seq_length: int = 32, + ): + super().__init__() + resnets = [] + attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + motion_modules.append( + TransformerTemporalModel( + num_attention_heads=temporal_num_attention_heads, + in_channels=out_channels, + norm_num_groups=resnet_groups, + cross_attention_dim=temporal_cross_attention_dim, + attention_bias=False, + activation_fn="geglu", + positional_embeddings="sinusoidal", + num_positional_embeddings=temporal_max_seq_length, + attention_head_dim=out_channels // temporal_num_attention_heads, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + upsample_size: Optional[int] = None, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + ) -> torch.FloatTensor: + lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + blocks = zip(self.resnets, self.attentions, self.motion_modules) + for resnet, attn, motion_module in blocks: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + else: + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = motion_module( + hidden_states, + num_frames=num_frames, + )[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale) + + return hidden_states + + +class UpBlockMotion(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor: float = 1.0, + add_upsample: bool = True, + temporal_norm_num_groups: int = 32, + temporal_cross_attention_dim: Optional[int] = None, + temporal_num_attention_heads: int = 8, + temporal_max_seq_length: int = 32, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock2D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + + motion_modules.append( + TransformerTemporalModel( + num_attention_heads=temporal_num_attention_heads, + in_channels=out_channels, + norm_num_groups=temporal_norm_num_groups, + cross_attention_dim=temporal_cross_attention_dim, + attention_bias=False, + activation_fn="geglu", + positional_embeddings="sinusoidal", + num_positional_embeddings=temporal_max_seq_length, + attention_head_dim=out_channels // temporal_num_attention_heads, + ) + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + upsample_size=None, + scale: float = 1.0, + num_frames: int = 1, + ) -> torch.FloatTensor: + is_freeu_enabled = ( + getattr(self, "s1", None) + and getattr(self, "s2", None) + and getattr(self, "b1", None) + and getattr(self, "b2", None) + ) + + blocks = zip(self.resnets, self.motion_modules) + + for resnet, motion_module in blocks: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + # FreeU: Only operate on the first two stages + if is_freeu_enabled: + hidden_states, res_hidden_states = apply_freeu( + self.resolution_idx, + hidden_states, + res_hidden_states, + s1=self.s1, + s2=self.s2, + b1=self.b1, + b2=self.b2, + ) + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), hidden_states, temb + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + ) + + else: + hidden_states = resnet(hidden_states, temb, scale=scale) + hidden_states = motion_module(hidden_states, num_frames=num_frames)[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size, scale=scale) + + return hidden_states + + +class UNetMidBlockCrossAttnMotion(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + num_attention_heads: int = 1, + output_scale_factor: float = 1.0, + cross_attention_dim: int = 1280, + dual_cross_attention: float = False, + use_linear_projection: float = False, + upcast_attention: float = False, + attention_type: str = "default", + temporal_num_attention_heads: int = 1, + temporal_cross_attention_dim: Optional[int] = None, + temporal_max_seq_length: int = 32, + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ] + attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if not dual_cross_attention: + attentions.append( + Transformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + attention_type=attention_type, + ) + ) + else: + attentions.append( + DualTransformer2DModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + ) + ) + resnets.append( + ResnetBlock2D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + ) + ) + motion_modules.append( + TransformerTemporalModel( + num_attention_heads=temporal_num_attention_heads, + attention_head_dim=in_channels // temporal_num_attention_heads, + in_channels=in_channels, + norm_num_groups=resnet_groups, + cross_attention_dim=temporal_cross_attention_dim, + attention_bias=False, + positional_embeddings="sinusoidal", + num_positional_embeddings=temporal_max_seq_length, + activation_fn="geglu", + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + num_frames: int = 1, + ) -> torch.FloatTensor: + lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 + hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) + + blocks = zip(self.attentions, self.resnets[1:], self.motion_modules) + for attn, resnet, motion_module in blocks: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states, + temb, + **ckpt_kwargs, + ) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + **ckpt_kwargs, + ) + else: + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + cross_attention_kwargs=cross_attention_kwargs, + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + return_dict=False, + )[0] + hidden_states = motion_module( + hidden_states, + num_frames=num_frames, + )[0] + hidden_states = resnet(hidden_states, temb, scale=lora_scale) + + return hidden_states + + +class MidBlockTemporalDecoder(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + attention_head_dim: int = 512, + num_layers: int = 1, + upcast_attention: bool = False, + ): + super().__init__() + + resnets = [] + attentions = [] + for i in range(num_layers): + input_channels = in_channels if i == 0 else out_channels + resnets.append( + SpatioTemporalResBlock( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=None, + eps=1e-6, + temporal_eps=1e-5, + merge_factor=0.0, + merge_strategy="learned", + switch_spatial_to_temporal_mix=True, + ) + ) + + attentions.append( + Attention( + query_dim=in_channels, + heads=in_channels // attention_head_dim, + dim_head=attention_head_dim, + eps=1e-6, + upcast_attention=upcast_attention, + norm_num_groups=32, + bias=True, + residual_connection=True, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + def forward( + self, + hidden_states: torch.FloatTensor, + image_only_indicator: torch.FloatTensor, + ): + hidden_states = self.resnets[0]( + hidden_states, + image_only_indicator=image_only_indicator, + ) + for resnet, attn in zip(self.resnets[1:], self.attentions): + hidden_states = attn(hidden_states) + hidden_states = resnet( + hidden_states, + image_only_indicator=image_only_indicator, + ) + + return hidden_states + + +class UpBlockTemporalDecoder(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_layers: int = 1, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + for i in range(num_layers): + input_channels = in_channels if i == 0 else out_channels + + resnets.append( + SpatioTemporalResBlock( + in_channels=input_channels, + out_channels=out_channels, + temb_channels=None, + eps=1e-6, + temporal_eps=1e-5, + merge_factor=0.0, + merge_strategy="learned", + switch_spatial_to_temporal_mix=True, + ) + ) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + def forward( + self, + hidden_states: torch.FloatTensor, + image_only_indicator: torch.FloatTensor, + ) -> torch.FloatTensor: + for resnet in self.resnets: + hidden_states = resnet( + hidden_states, + image_only_indicator=image_only_indicator, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class UNetMidBlockSpatioTemporal(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + ): + super().__init__() + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + # support for variable transformer layers per block + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + # there is always at least one resnet + resnets = [ + SpatioTemporalResBlock( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=1e-5, + ) + ] + attentions = [] + + for i in range(num_layers): + attentions.append( + TransformerSpatioTemporalModel( + num_attention_heads, + in_channels // num_attention_heads, + in_channels=in_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + ) + ) + + resnets.append( + SpatioTemporalResBlock( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=1e-5, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + pose_feature: Optional[torch.Tensor] = None # [bs, c, frame, h, w] + ) -> torch.FloatTensor: + hidden_states = self.resnets[0]( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + + for attn, resnet in zip(self.attentions, self.resnets[1:]): + if self.training and self.gradient_checkpointing: # TODO + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + **ckpt_kwargs, + ) + else: + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + hidden_states = resnet( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + + return hidden_states + + +class DownBlockSpatioTemporal(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + num_layers: int = 1, + add_downsample: bool = True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + SpatioTemporalResBlock( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=1e-5, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + for resnet in self.resnets: + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + ) + else: + hidden_states = resnet( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + + output_states = output_states + (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnDownBlockSpatioTemporal(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + add_downsample: bool = True, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + SpatioTemporalResBlock( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=1e-6, + ) + ) + attentions.append( + TransformerSpatioTemporalModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample2D( + out_channels, + use_conv=True, + out_channels=out_channels, + padding=1, + name="op", + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + additional_residuals: Optional[torch.FloatTensor] = None, + pose_feature: Optional[torch.Tensor] = None # [bs, c, frame, h, w] + ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: + output_states = () + + blocks = list(zip(self.resnets, self.attentions)) + for block_idx, (resnet, attn) in enumerate(blocks): + if self.training and self.gradient_checkpointing: # TODO + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + **ckpt_kwargs, + ) + + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + else: + hidden_states = resnet( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + + output_states = output_states + (hidden_states,) + + # NOTE + if block_idx == len(blocks) - 1 and additional_residuals is not None: + if hidden_states.dim() == 5: + additional_residuals = rearrange(additional_residuals, '(b f) c h w -> b c f h w', b=hidden_states.shape[0]) + hidden_states = hidden_states + additional_residuals + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states = output_states + (hidden_states,) + + return hidden_states, output_states + + +class UpBlockSpatioTemporal(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + num_layers: int = 1, + resnet_eps: float = 1e-6, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + SpatioTemporalResBlock( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + ) + ) + + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + ) -> torch.FloatTensor: + for resnet in self.resnets: + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + if is_torch_version(">=", "1.11.0"): + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + use_reentrant=False, + ) + else: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + ) + else: + hidden_states = resnet( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states + + +class CrossAttnUpBlockSpatioTemporal(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + resolution_idx: Optional[int] = None, + num_layers: int = 1, + transformer_layers_per_block: Union[int, Tuple[int]] = 1, + resnet_eps: float = 1e-6, + num_attention_heads: int = 1, + cross_attention_dim: int = 1280, + add_upsample: bool = True, + ): + super().__init__() + resnets = [] + attentions = [] + + self.has_cross_attention = True + self.num_attention_heads = num_attention_heads + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * num_layers + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + SpatioTemporalResBlock( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + ) + ) + attentions.append( + TransformerSpatioTemporalModel( + num_attention_heads, + out_channels // num_attention_heads, + in_channels=out_channels, + num_layers=transformer_layers_per_block[i], + cross_attention_dim=cross_attention_dim, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.resnets = nn.ModuleList(resnets) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + self.resolution_idx = resolution_idx + + def forward( + self, + hidden_states: torch.FloatTensor, + res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], + temb: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + image_only_indicator: Optional[torch.Tensor] = None, + pose_feature: Optional[torch.Tensor] = None # [bs, c, frame, h, w] + ) -> torch.FloatTensor: + for resnet, attn in zip(self.resnets, self.attentions): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: # TODO + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(resnet), + hidden_states, + temb, + image_only_indicator, + **ckpt_kwargs, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + else: + hidden_states = resnet( + hidden_states, + temb, + image_only_indicator=image_only_indicator, + ) + hidden_states = attn( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_feature, + return_dict=False, + )[0] + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states) + + return hidden_states diff --git a/models_diffusers/unet_spatio_temporal_condition.py b/models_diffusers/unet_spatio_temporal_condition.py new file mode 100644 index 0000000000000000000000000000000000000000..3d4d11feada504f523a5ec0574ed84a1a204d515 --- /dev/null +++ b/models_diffusers/unet_spatio_temporal_condition.py @@ -0,0 +1,1077 @@ +from dataclasses import dataclass +from typing import Dict, Optional, Tuple, Union +from einops import rearrange + +import torch +import torch.nn as nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +# from diffusers.models.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block +from models_diffusers.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block + + +import inspect +import itertools +import os +import re +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, Optional, Tuple, Union + +from diffusers import __version__ +from diffusers.utils import ( + CONFIG_NAME, + DIFFUSERS_CACHE, + FLAX_WEIGHTS_NAME, + HF_HUB_OFFLINE, + MIN_PEFT_VERSION, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, + _add_variant, + _get_model_file, + check_peft_version, + deprecate, + is_accelerate_available, + is_torch_version, + logging, +) +from diffusers.utils.hub_utils import PushToHubMixin +from diffusers.models.modeling_utils import load_model_dict_into_meta, load_state_dict + +if is_torch_version(">=", "1.9.0"): + _LOW_CPU_MEM_USAGE_DEFAULT = True +else: + _LOW_CPU_MEM_USAGE_DEFAULT = False + +if is_accelerate_available(): + import accelerate + from accelerate.utils import set_module_tensor_to_device + from accelerate.utils.versions import is_torch_version + +from models_diffusers.camera.attention_processor import XFormersAttnProcessor as CustomizedXFormerAttnProcessor +from models_diffusers.camera.attention_processor import PoseAdaptorXFormersAttnProcessor + +# if hasattr(F, "scaled_dot_product_attention"): +# from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor2_0 as PoseAdaptorAttnProcessor +# from models_diffusers.camera.attention_processor import AttnProcessor2_0 as CustomizedAttnProcessor +# else: +from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor +from models_diffusers.camera.attention_processor import AttnProcessor as CustomizedAttnProcessor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNetSpatioTemporalConditionOutput(BaseOutput): + """ + The output of [`UNetSpatioTemporalConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): + The tuple of downsample blocks to use. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): + The tuple of upsample blocks to use. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + addition_time_embed_dim: (`int`, defaults to 256): + Dimension to to encode the additional time ids. + projection_class_embeddings_input_dim (`int`, defaults to 768): + The dimension of the projection of encoded `added_time_ids`. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], + [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. + num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): + The number of attention heads. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 8, + out_channels: int = 4, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "DownBlockSpatioTemporal", + ), + up_block_types: Tuple[str] = ( + "UpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + ), + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + addition_time_embed_dim: int = 256, + projection_class_embeddings_input_dim: int = 768, + layers_per_block: Union[int, Tuple[int]] = 2, + cross_attention_dim: Union[int, Tuple[int]] = 1024, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), + num_frames: int = 25, + ): + super().__init__() + + self.sample_size = sample_size + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + # self.mask_token = nn.Parameter(torch.randn(1, 1, 4, 1, 1)) + + # input + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=3, + padding=1, + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=1e-5, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + resnet_act_fn="silu", + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlockSpatioTemporal( + block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + transformer_layers_per_block=transformer_layers_per_block[-1], + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=1e-5, + resolution_idx=i, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + resnet_act_fn="silu", + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) + self.conv_act = nn.SiLU() + + self.conv_out = nn.Conv2d( + block_out_channels[0], + out_channels, + kernel_size=3, + padding=1, + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking + def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: + """ + Sets the attention processor to use [feed forward + chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). + + Parameters: + chunk_size (`int`, *optional*): + The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually + over each tensor of dim=`dim`. + dim (`int`, *optional*, defaults to `0`): + The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) + or dim=1 (sequence length). + """ + if dim not in [0, 1]: + raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") + + # By default chunk size is 1 + chunk_size = chunk_size or 1 + + def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, chunk_size, dim) + + def set_pose_cond_attn_processor( + self, + add_spatial=False, + add_temporal=False, + enable_xformers=False, + attn_processor_name='attn1', + pose_feature_dimensions=[320, 640, 1280, 1280], + **attention_processor_kwargs, + ): + all_attn_processors = {} + set_processor_names = attn_processor_name.split(',') + if add_spatial: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorXFormersAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' not in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + if add_temporal: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = (processor_name in set_processor_names) + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + self.set_attn_processor(all_attn_processors) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + added_time_ids: torch.Tensor, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, # for t2i-adaptor or controlnet + mid_block_additional_residual: Optional[torch.Tensor] = None, # for controlnet + pose_features: List[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]: + r""" + The [`UNetSpatioTemporalConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. + added_time_ids: (`torch.FloatTensor`): + The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal + embeddings and added to the time embeddings. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain + tuple. + Returns: + [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + batch_size, num_frames = sample.shape[:2] + timesteps = timesteps.expand(batch_size) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(added_time_ids.flatten()) + time_embeds = time_embeds.reshape((batch_size, -1)) + time_embeds = time_embeds.to(emb.dtype) + aug_emb = self.add_embedding(time_embeds) + emb = emb + aug_emb + + # Flatten the batch and frames dimensions + # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] + sample = sample.flatten(0, 1) + # Repeat the embeddings num_video_frames times + # emb: [batch, channels] -> [batch * frames, channels] + emb = emb.repeat_interleave(num_frames, dim=0) + # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] + encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) + + # 2. pre-process + sample = self.conv_in(sample) + + image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) + + is_adapter = is_controlnet = False + if (down_block_additional_residuals is not None): + if (mid_block_additional_residual is not None): + is_controlnet = True + else: + is_adapter = True + + down_block_res_samples = (sample,) + for block_idx, downsample_block in enumerate(self.down_blocks): + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # print('has_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal + + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals['additional_residuals'] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[block_idx] if pose_features is not None else None, + **additional_residuals, + ) + else: + # print('no_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.DownBlockSpatioTemporal + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + image_only_indicator=image_only_indicator, + ) + + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals = down_block_additional_residuals.pop(0) + if sample.dim() == 5: + additional_residuals = rearrange(additional_residuals, '(b f) c h w -> b c f h w', b=sample.shape[0]) + sample = sample + additional_residuals + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip(down_block_res_samples, down_block_additional_residuals): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + sample = self.mid_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-1] if pose_features is not None else None, + ) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for block_idx, upsample_block in enumerate(self.up_blocks): + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-(block_idx + 1)] if pose_features is not None else None, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + image_only_indicator=image_only_indicator, + ) + + # 6. post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + # 7. Reshape back to original shape + sample = sample.reshape(batch_size, num_frames, *sample.shape[1:]) + + if not return_dict: + return (sample,) + + return UNetSpatioTemporalConditionOutput(sample=sample) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], custom_resume=False, **kwargs): + r""" + Instantiate a pretrained PyTorch model from a pretrained model configuration. + + The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To + train the model, set it back in training mode with `model.train()`. + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): + Can be either: + + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on + the Hub. + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved + with [`~ModelMixin.save_pretrained`]. + + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache + is not used. + torch_dtype (`str` or `torch.dtype`, *optional*): + Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the + dtype is automatically derived from the model's weights. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any + incompletely downloaded files are deleted. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + output_loading_info (`bool`, *optional*, defaults to `False`): + Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether to only load local model weights and configuration files or not. If set to `True`, the model + won't be downloaded from the Hub. + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from + `diffusers-cli login` (stored in `~/.huggingface`) is used. + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier + allowed by Git. + from_flax (`bool`, *optional*, defaults to `False`): + Load the model weights from a Flax checkpoint save file. + subfolder (`str`, *optional*, defaults to `""`): + The subfolder location of a model file within a larger model repository on the Hub or locally. + mirror (`str`, *optional*): + Mirror source to resolve accessibility issues if you're downloading a model in China. We do not + guarantee the timeliness or safety of the source, and you should refer to the mirror site for more + information. + device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): + A map that specifies where each submodule should go. It doesn't need to be defined for each + parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the + same device. + + Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For + more information about each option see [designing a device + map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). + max_memory (`Dict`, *optional*): + A dictionary device identifier for the maximum memory. Will default to the maximum memory available for + each GPU and the available CPU RAM if unset. + offload_folder (`str` or `os.PathLike`, *optional*): + The path to offload weights if `device_map` contains the value `"disk"`. + offload_state_dict (`bool`, *optional*): + If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if + the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` + when there is some disk offload. + low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): + Speed up model loading only loading the pretrained weights and not initializing the weights. This also + tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. + Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this + argument to `True` will raise an error. + variant (`str`, *optional*): + Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when + loading `from_flax`. + use_safetensors (`bool`, *optional*, defaults to `None`): + If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the + `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` + weights. If set to `False`, `safetensors` weights are not loaded. + + + + To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with + `huggingface-cli login`. You can also activate the special + ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a + firewalled environment. + + + + Example: + + ```py + from diffusers import UNet2DConditionModel + + unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + ``` + + If you get the error message below, you need to finetune the weights for your downstream task: + + ```bash + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated + You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. + ``` + """ + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) + ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) + force_download = kwargs.pop("force_download", False) + from_flax = kwargs.pop("from_flax", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + output_loading_info = kwargs.pop("output_loading_info", False) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + torch_dtype = kwargs.pop("torch_dtype", None) + subfolder = kwargs.pop("subfolder", None) + device_map = kwargs.pop("device_map", None) + max_memory = kwargs.pop("max_memory", None) + offload_folder = kwargs.pop("offload_folder", None) + offload_state_dict = kwargs.pop("offload_state_dict", False) + low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) + variant = kwargs.pop("variant", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = True + allow_pickle = True + + if low_cpu_mem_usage and not is_accelerate_available(): + low_cpu_mem_usage = False + logger.warning( + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" + " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" + " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" + " install accelerate\n```\n." + ) + + if device_map is not None and not is_accelerate_available(): + raise NotImplementedError( + "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" + " `device_map=None`. You can install accelerate with `pip install accelerate`." + ) + + # Check if we can handle device_map and dispatching the weights + if device_map is not None and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `device_map=None`." + ) + + if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `low_cpu_mem_usage=False`." + ) + + if low_cpu_mem_usage is False and device_map is not None: + raise ValueError( + f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" + " dispatching. Please make sure to set `low_cpu_mem_usage=True`." + ) + + # Load config if we don't provide a configuration + config_path = pretrained_model_name_or_path + + user_agent = { + "diffusers": __version__, + "file_type": "model", + "framework": "pytorch", + } + + # load config + config, unused_kwargs, commit_hash = cls.load_config( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + return_commit_hash=True, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + device_map=device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + user_agent=user_agent, + **kwargs, + ) + + # if not custom_resume: + # # NOTE: update in_channels, for additional mask concatentation + # config['in_channels'] = config['in_channels'] + 1 + + # load model + model_file = None + if from_flax: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=FLAX_WEIGHTS_NAME, + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + model = cls.from_config(config, **unused_kwargs) + + # Convert the weights + from diffusers.models.modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model + + model = load_flax_checkpoint_in_pytorch_model(model, model_file) + else: + if use_safetensors: + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + except IOError as e: + if not allow_pickle: + raise e + pass + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + + if low_cpu_mem_usage: + # Instantiate model with empty weights + with accelerate.init_empty_weights(): + model = cls.from_config(config, **unused_kwargs) + + # if device_map is None, load the state dict and move the params from meta device to the cpu + if device_map is None: + param_device = "cpu" + state_dict = load_state_dict(model_file, variant=variant) + + # if not custom_resume: + # # NOTE update conv_in_weight + # conv_in_weight = state_dict['conv_in.weight'] + # assert conv_in_weight.shape == (320, 8, 3, 3) + # conv_in_weight_new = torch.randn(320, 9, 3, 3).to(conv_in_weight.device).to(conv_in_weight.dtype) + # conv_in_weight_new[:, :8, :, :] = conv_in_weight + # state_dict['conv_in.weight'] = conv_in_weight_new + + # # NOTE add mask_token + # mask_token = torch.randn(1, 1, 4, 1, 1).to(conv_in_weight.device).to(conv_in_weight.dtype) + # state_dict["mask_token"] = mask_token + + model._convert_deprecated_attention_blocks(state_dict) + # move the params from meta device to cpu + missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) + if len(missing_keys) > 0: + raise ValueError( + f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are" + f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass" + " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize" + " those weights or else make sure your checkpoint file is correct." + ) + + unexpected_keys = load_model_dict_into_meta( + model, + state_dict, + device=param_device, + dtype=torch_dtype, + model_name_or_path=pretrained_model_name_or_path, + ) + + if cls._keys_to_ignore_on_load_unexpected is not None: + for pat in cls._keys_to_ignore_on_load_unexpected: + unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] + + if len(unexpected_keys) > 0: + logger.warn( + f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}" + ) + + else: # else let accelerate handle loading and dispatching. + # Load weights and dispatch according to the device_map + # by default the device_map is None and the weights are loaded on the CPU + try: + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + except AttributeError as e: + # When using accelerate loading, we do not have the ability to load the state + # dict and rename the weight names manually. Additionally, accelerate skips + # torch loading conventions and directly writes into `module.{_buffers, _parameters}` + # (which look like they should be private variables?), so we can't use the standard hooks + # to rename parameters on load. We need to mimic the original weight names so the correct + # attributes are available. After we have loaded the weights, we convert the deprecated + # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert + # the weights so we don't have to do this again. + + if "'Attention' object has no attribute" in str(e): + logger.warn( + f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}" + " was saved with deprecated attention block weight names. We will load it with the deprecated attention block" + " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion," + " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint," + " please also re-upload it or open a PR on the original repository." + ) + model._temp_convert_self_to_deprecated_attention_blocks() + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + model._undo_temp_convert_self_to_deprecated_attention_blocks() + else: + raise e + + loading_info = { + "missing_keys": [], + "unexpected_keys": [], + "mismatched_keys": [], + "error_msgs": [], + } + else: + model = cls.from_config(config, **unused_kwargs) + + state_dict = load_state_dict(model_file, variant=variant) + model._convert_deprecated_attention_blocks(state_dict) + + model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( + model, + state_dict, + model_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=ignore_mismatched_sizes, + ) + + loading_info = { + "missing_keys": missing_keys, + "unexpected_keys": unexpected_keys, + "mismatched_keys": mismatched_keys, + "error_msgs": error_msgs, + } + + if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." + ) + elif torch_dtype is not None: + model = model.to(torch_dtype) + + model.register_to_config(_name_or_path=pretrained_model_name_or_path) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + if output_loading_info: + return model, loading_info + + return model diff --git a/models_diffusers/unet_spatio_temporal_condition_interp.py b/models_diffusers/unet_spatio_temporal_condition_interp.py new file mode 100644 index 0000000000000000000000000000000000000000..95b03e36efa4946b7fe9f28d4547152658a64acb --- /dev/null +++ b/models_diffusers/unet_spatio_temporal_condition_interp.py @@ -0,0 +1,1077 @@ +from dataclasses import dataclass +from typing import Dict, Optional, Tuple, Union +from einops import rearrange + +import torch +import torch.nn as nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.loaders import UNet2DConditionLoadersMixin +from diffusers.utils import BaseOutput, logging +from diffusers.models.attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from diffusers.models.modeling_utils import ModelMixin +# from diffusers.models.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block +from models_diffusers.unet_3d_blocks import UNetMidBlockSpatioTemporal, get_down_block, get_up_block + + +import inspect +import itertools +import os +import re +from collections import OrderedDict +from functools import partial +from typing import Any, Callable, List, Optional, Tuple, Union + +from diffusers import __version__ +from diffusers.utils import ( + CONFIG_NAME, + DIFFUSERS_CACHE, + FLAX_WEIGHTS_NAME, + HF_HUB_OFFLINE, + MIN_PEFT_VERSION, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, + _add_variant, + _get_model_file, + check_peft_version, + deprecate, + is_accelerate_available, + is_torch_version, + logging, +) +from diffusers.utils.hub_utils import PushToHubMixin +from diffusers.models.modeling_utils import load_model_dict_into_meta, load_state_dict + +if is_torch_version(">=", "1.9.0"): + _LOW_CPU_MEM_USAGE_DEFAULT = True +else: + _LOW_CPU_MEM_USAGE_DEFAULT = False + +if is_accelerate_available(): + import accelerate + from accelerate.utils import set_module_tensor_to_device + from accelerate.utils.versions import is_torch_version + +from models_diffusers.camera.attention_processor import XFormersAttnProcessor as CustomizedXFormerAttnProcessor +from models_diffusers.camera.attention_processor import PoseAdaptorXFormersAttnProcessor + +# if hasattr(F, "scaled_dot_product_attention"): +# from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor2_0 as PoseAdaptorAttnProcessor +# from models_diffusers.camera.attention_processor import AttnProcessor2_0 as CustomizedAttnProcessor +# else: +from models_diffusers.camera.attention_processor import PoseAdaptorAttnProcessor +from models_diffusers.camera.attention_processor import AttnProcessor as CustomizedAttnProcessor + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNetSpatioTemporalConditionOutput(BaseOutput): + """ + The output of [`UNetSpatioTemporalConditionModel`]. + + Args: + sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): + The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. + """ + + sample: torch.FloatTensor = None + + +class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): + r""" + A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample + shaped output. + + This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented + for all models (such as downloading or saving). + + Parameters: + sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): + Height and width of input/output sample. + in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample. + out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. + down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`): + The tuple of downsample blocks to use. + up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`): + The tuple of upsample blocks to use. + block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): + The tuple of output channels for each block. + addition_time_embed_dim: (`int`, defaults to 256): + Dimension to to encode the additional time ids. + projection_class_embeddings_input_dim (`int`, defaults to 768): + The dimension of the projection of encoded `added_time_ids`. + layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. + cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): + The dimension of the cross attention features. + transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): + The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for + [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`], + [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`]. + num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`): + The number of attention heads. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + """ + + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 8, + out_channels: int = 4, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "CrossAttnDownBlockSpatioTemporal", + "DownBlockSpatioTemporal", + ), + up_block_types: Tuple[str] = ( + "UpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + "CrossAttnUpBlockSpatioTemporal", + ), + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + addition_time_embed_dim: int = 256, + projection_class_embeddings_input_dim: int = 768, + layers_per_block: Union[int, Tuple[int]] = 2, + cross_attention_dim: Union[int, Tuple[int]] = 1024, + transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, + num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20), + num_frames: int = 25, + ): + super().__init__() + + self.sample_size = sample_size + + # Check inputs + if len(down_block_types) != len(up_block_types): + raise ValueError( + f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." + ) + + if len(block_out_channels) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." + ) + + if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." + ) + + if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): + raise ValueError( + f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." + ) + + self.mask_token = nn.Parameter(torch.randn(1, 1, 4, 1, 1)) + + # input + self.conv_in = nn.Conv2d( + in_channels, + block_out_channels[0], + kernel_size=3, + padding=1, + ) + + # time + time_embed_dim = block_out_channels[0] * 4 + + self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0) + self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) + + self.down_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + if isinstance(num_attention_heads, int): + num_attention_heads = (num_attention_heads,) * len(down_block_types) + + if isinstance(cross_attention_dim, int): + cross_attention_dim = (cross_attention_dim,) * len(down_block_types) + + if isinstance(layers_per_block, int): + layers_per_block = [layers_per_block] * len(down_block_types) + + if isinstance(transformer_layers_per_block, int): + transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) + + blocks_time_embed_dim = time_embed_dim + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block[i], + transformer_layers_per_block=transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + temb_channels=blocks_time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=1e-5, + cross_attention_dim=cross_attention_dim[i], + num_attention_heads=num_attention_heads[i], + resnet_act_fn="silu", + ) + self.down_blocks.append(down_block) + + # mid + self.mid_block = UNetMidBlockSpatioTemporal( + block_out_channels[-1], + temb_channels=blocks_time_embed_dim, + transformer_layers_per_block=transformer_layers_per_block[-1], + cross_attention_dim=cross_attention_dim[-1], + num_attention_heads=num_attention_heads[-1], + ) + + # count how many layers upsample the images + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_num_attention_heads = list(reversed(num_attention_heads)) + reversed_layers_per_block = list(reversed(layers_per_block)) + reversed_cross_attention_dim = list(reversed(cross_attention_dim)) + reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block)) + + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=reversed_layers_per_block[i] + 1, + transformer_layers_per_block=reversed_transformer_layers_per_block[i], + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=blocks_time_embed_dim, + add_upsample=add_upsample, + resnet_eps=1e-5, + resolution_idx=i, + cross_attention_dim=reversed_cross_attention_dim[i], + num_attention_heads=reversed_num_attention_heads[i], + resnet_act_fn="silu", + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5) + self.conv_act = nn.SiLU() + + self.conv_out = nn.Conv2d( + block_out_channels[0], + out_channels, + kernel_size=3, + padding=1, + ) + + @property + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors( + name: str, + module: torch.nn.Module, + processors: Dict[str, AttentionProcessor], + ): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): + processor = AttnProcessor() + else: + raise ValueError( + f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" + ) + + self.set_attn_processor(processor) + + def _set_gradient_checkpointing(self, module, value=False): + if hasattr(module, "gradient_checkpointing"): + module.gradient_checkpointing = value + + # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking + def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: + """ + Sets the attention processor to use [feed forward + chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). + + Parameters: + chunk_size (`int`, *optional*): + The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually + over each tensor of dim=`dim`. + dim (`int`, *optional*, defaults to `0`): + The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) + or dim=1 (sequence length). + """ + if dim not in [0, 1]: + raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") + + # By default chunk size is 1 + chunk_size = chunk_size or 1 + + def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): + if hasattr(module, "set_chunk_feed_forward"): + module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) + + for child in module.children(): + fn_recursive_feed_forward(child, chunk_size, dim) + + for module in self.children(): + fn_recursive_feed_forward(module, chunk_size, dim) + + def set_pose_cond_attn_processor( + self, + add_spatial=False, + add_temporal=False, + enable_xformers=False, + attn_processor_name='attn1', + pose_feature_dimensions=[320, 640, 1280, 1280], + **attention_processor_kwargs, + ): + all_attn_processors = {} + set_processor_names = attn_processor_name.split(',') + if add_spatial: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorXFormersAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' not in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + if add_temporal: + for processor_key in self.attn_processors.keys(): + if 'temporal' not in processor_key: + continue + processor_name = processor_key.split('.')[-2] + cross_attention_dim = None if processor_name == 'attn1' else self.config.cross_attention_dim + if processor_key.startswith("mid_block"): + hidden_size = self.config.block_out_channels[-1] + block_id = -1 + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + elif processor_key.startswith("up_blocks"): + block_id = int(processor_key[len("up_blocks.")]) + hidden_size = list(reversed(self.config.block_out_channels))[block_id] + add_pose_adaptor = (processor_name in set_processor_names) + pose_feature_dim = list(reversed(pose_feature_dimensions))[block_id] if add_pose_adaptor else None + else: + block_id = int(processor_key[len("down_blocks.")]) + hidden_size = self.config.block_out_channels[block_id] + add_pose_adaptor = processor_name in set_processor_names + pose_feature_dim = pose_feature_dimensions[block_id] if add_pose_adaptor else None + if add_pose_adaptor and enable_xformers: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif add_pose_adaptor: + all_attn_processors[processor_key] = PoseAdaptorAttnProcessor(hidden_size=hidden_size, + pose_feature_dim=pose_feature_dim, + cross_attention_dim=cross_attention_dim, + **attention_processor_kwargs) + elif enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + else: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + else: + for processor_key in self.attn_processors.keys(): + if 'temporal' in processor_key and enable_xformers: + all_attn_processors[processor_key] = CustomizedXFormerAttnProcessor() + elif 'temporal' in processor_key: + all_attn_processors[processor_key] = CustomizedAttnProcessor() + + self.set_attn_processor(all_attn_processors) + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + added_time_ids: torch.Tensor, + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, # for t2i-adaptor or controlnet + mid_block_additional_residual: Optional[torch.Tensor] = None, # for controlnet + pose_features: List[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]: + r""" + The [`UNetSpatioTemporalConditionModel`] forward method. + + Args: + sample (`torch.FloatTensor`): + The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`. + timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. + encoder_hidden_states (`torch.FloatTensor`): + The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`. + added_time_ids: (`torch.FloatTensor`): + The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal + embeddings and added to the time embeddings. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] instead of a plain + tuple. + Returns: + [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`: + If `return_dict` is True, an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, otherwise + a `tuple` is returned where the first element is the sample tensor. + """ + # 1. time + timesteps = timestep + if not torch.is_tensor(timesteps): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + batch_size, num_frames = sample.shape[:2] + timesteps = timesteps.expand(batch_size) + + t_emb = self.time_proj(timesteps) + + # `Timesteps` does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=sample.dtype) + + emb = self.time_embedding(t_emb) + + time_embeds = self.add_time_proj(added_time_ids.flatten()) + time_embeds = time_embeds.reshape((batch_size, -1)) + time_embeds = time_embeds.to(emb.dtype) + aug_emb = self.add_embedding(time_embeds) + emb = emb + aug_emb + + # Flatten the batch and frames dimensions + # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width] + sample = sample.flatten(0, 1) + # Repeat the embeddings num_video_frames times + # emb: [batch, channels] -> [batch * frames, channels] + emb = emb.repeat_interleave(num_frames, dim=0) + # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels] + encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0) + + # 2. pre-process + sample = self.conv_in(sample) + + image_only_indicator = torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device) + + is_adapter = is_controlnet = False + if (down_block_additional_residuals is not None): + if (mid_block_additional_residual is not None): + is_controlnet = True + else: + is_adapter = True + + down_block_res_samples = (sample,) + for block_idx, downsample_block in enumerate(self.down_blocks): + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + # print('has_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal + + additional_residuals = {} + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals['additional_residuals'] = down_block_additional_residuals.pop(0) + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[block_idx] if pose_features is not None else None, + **additional_residuals, + ) + else: + # print('no_cross_attention', type(downsample_block)) + # models_diffusers.unet_3d_blocks.DownBlockSpatioTemporal + + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + image_only_indicator=image_only_indicator, + ) + + if is_adapter and len(down_block_additional_residuals) > 0: + additional_residuals = down_block_additional_residuals.pop(0) + if sample.dim() == 5: + additional_residuals = rearrange(additional_residuals, '(b f) c h w -> b c f h w', b=sample.shape[0]) + sample = sample + additional_residuals + + down_block_res_samples += res_samples + + if is_controlnet: + new_down_block_res_samples = () + + for down_block_res_sample, down_block_additional_residual in zip(down_block_res_samples, down_block_additional_residuals): + down_block_res_sample = down_block_res_sample + down_block_additional_residual + new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) + + down_block_res_samples = new_down_block_res_samples + + # 4. mid + sample = self.mid_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-1] if pose_features is not None else None, + ) + + if is_controlnet: + sample = sample + mid_block_additional_residual + + # 5. up + for block_idx, upsample_block in enumerate(self.up_blocks): + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + image_only_indicator=image_only_indicator, + pose_feature=pose_features[-(block_idx + 1)] if pose_features is not None else None, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + image_only_indicator=image_only_indicator, + ) + + # 6. post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + # 7. Reshape back to original shape + sample = sample.reshape(batch_size, num_frames, *sample.shape[1:]) + + if not return_dict: + return (sample,) + + return UNetSpatioTemporalConditionOutput(sample=sample) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], custom_resume=False, **kwargs): + r""" + Instantiate a pretrained PyTorch model from a pretrained model configuration. + + The model is set in evaluation mode - `model.eval()` - by default, and dropout modules are deactivated. To + train the model, set it back in training mode with `model.train()`. + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*): + Can be either: + + - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on + the Hub. + - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved + with [`~ModelMixin.save_pretrained`]. + + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory where a downloaded pretrained model configuration is cached if the standard cache + is not used. + torch_dtype (`str` or `torch.dtype`, *optional*): + Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the + dtype is automatically derived from the model's weights. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to resume downloading the model weights and configuration files. If set to `False`, any + incompletely downloaded files are deleted. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + output_loading_info (`bool`, *optional*, defaults to `False`): + Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether to only load local model weights and configuration files or not. If set to `True`, the model + won't be downloaded from the Hub. + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from + `diffusers-cli login` (stored in `~/.huggingface`) is used. + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier + allowed by Git. + from_flax (`bool`, *optional*, defaults to `False`): + Load the model weights from a Flax checkpoint save file. + subfolder (`str`, *optional*, defaults to `""`): + The subfolder location of a model file within a larger model repository on the Hub or locally. + mirror (`str`, *optional*): + Mirror source to resolve accessibility issues if you're downloading a model in China. We do not + guarantee the timeliness or safety of the source, and you should refer to the mirror site for more + information. + device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*): + A map that specifies where each submodule should go. It doesn't need to be defined for each + parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the + same device. + + Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For + more information about each option see [designing a device + map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). + max_memory (`Dict`, *optional*): + A dictionary device identifier for the maximum memory. Will default to the maximum memory available for + each GPU and the available CPU RAM if unset. + offload_folder (`str` or `os.PathLike`, *optional*): + The path to offload weights if `device_map` contains the value `"disk"`. + offload_state_dict (`bool`, *optional*): + If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if + the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True` + when there is some disk offload. + low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): + Speed up model loading only loading the pretrained weights and not initializing the weights. This also + tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. + Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this + argument to `True` will raise an error. + variant (`str`, *optional*): + Load weights from a specified `variant` filename such as `"fp16"` or `"ema"`. This is ignored when + loading `from_flax`. + use_safetensors (`bool`, *optional*, defaults to `None`): + If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the + `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors` + weights. If set to `False`, `safetensors` weights are not loaded. + + + + To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with + `huggingface-cli login`. You can also activate the special + ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use this method in a + firewalled environment. + + + + Example: + + ```py + from diffusers import UNet2DConditionModel + + unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet") + ``` + + If you get the error message below, you need to finetune the weights for your downstream task: + + ```bash + Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match: + - conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated + You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. + ``` + """ + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) + ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False) + force_download = kwargs.pop("force_download", False) + from_flax = kwargs.pop("from_flax", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + output_loading_info = kwargs.pop("output_loading_info", False) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + torch_dtype = kwargs.pop("torch_dtype", None) + subfolder = kwargs.pop("subfolder", None) + device_map = kwargs.pop("device_map", None) + max_memory = kwargs.pop("max_memory", None) + offload_folder = kwargs.pop("offload_folder", None) + offload_state_dict = kwargs.pop("offload_state_dict", False) + low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT) + variant = kwargs.pop("variant", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = True + allow_pickle = True + + if low_cpu_mem_usage and not is_accelerate_available(): + low_cpu_mem_usage = False + logger.warning( + "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" + " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" + " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" + " install accelerate\n```\n." + ) + + if device_map is not None and not is_accelerate_available(): + raise NotImplementedError( + "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set" + " `device_map=None`. You can install accelerate with `pip install accelerate`." + ) + + # Check if we can handle device_map and dispatching the weights + if device_map is not None and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `device_map=None`." + ) + + if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): + raise NotImplementedError( + "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" + " `low_cpu_mem_usage=False`." + ) + + if low_cpu_mem_usage is False and device_map is not None: + raise ValueError( + f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and" + " dispatching. Please make sure to set `low_cpu_mem_usage=True`." + ) + + # Load config if we don't provide a configuration + config_path = pretrained_model_name_or_path + + user_agent = { + "diffusers": __version__, + "file_type": "model", + "framework": "pytorch", + } + + # load config + config, unused_kwargs, commit_hash = cls.load_config( + config_path, + cache_dir=cache_dir, + return_unused_kwargs=True, + return_commit_hash=True, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + device_map=device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + user_agent=user_agent, + **kwargs, + ) + + if not custom_resume: + # NOTE: update in_channels, for additional mask concatentation + config['in_channels'] = config['in_channels'] + 1 + + # load model + model_file = None + if from_flax: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=FLAX_WEIGHTS_NAME, + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + model = cls.from_config(config, **unused_kwargs) + + # Convert the weights + from diffusers.models.modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model + + model = load_flax_checkpoint_in_pytorch_model(model, model_file) + else: + if use_safetensors: + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + except IOError as e: + if not allow_pickle: + raise e + pass + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=_add_variant(WEIGHTS_NAME, variant), + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + commit_hash=commit_hash, + ) + + if low_cpu_mem_usage: + # Instantiate model with empty weights + with accelerate.init_empty_weights(): + model = cls.from_config(config, **unused_kwargs) + + # if device_map is None, load the state dict and move the params from meta device to the cpu + if device_map is None: + param_device = "cpu" + state_dict = load_state_dict(model_file, variant=variant) + + if not custom_resume: + # NOTE update conv_in_weight + conv_in_weight = state_dict['conv_in.weight'] + assert conv_in_weight.shape == (320, 8, 3, 3) + conv_in_weight_new = torch.randn(320, 9, 3, 3).to(conv_in_weight.device).to(conv_in_weight.dtype) + conv_in_weight_new[:, :8, :, :] = conv_in_weight + state_dict['conv_in.weight'] = conv_in_weight_new + + # NOTE add mask_token + mask_token = torch.randn(1, 1, 4, 1, 1).to(conv_in_weight.device).to(conv_in_weight.dtype) + state_dict["mask_token"] = mask_token + + model._convert_deprecated_attention_blocks(state_dict) + # move the params from meta device to cpu + missing_keys = set(model.state_dict().keys()) - set(state_dict.keys()) + if len(missing_keys) > 0: + raise ValueError( + f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are" + f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass" + " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize" + " those weights or else make sure your checkpoint file is correct." + ) + + unexpected_keys = load_model_dict_into_meta( + model, + state_dict, + device=param_device, + dtype=torch_dtype, + model_name_or_path=pretrained_model_name_or_path, + ) + + if cls._keys_to_ignore_on_load_unexpected is not None: + for pat in cls._keys_to_ignore_on_load_unexpected: + unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] + + if len(unexpected_keys) > 0: + logger.warn( + f"Some weights of the model checkpoint were not used when initializing {cls.__name__}: \n {[', '.join(unexpected_keys)]}" + ) + + else: # else let accelerate handle loading and dispatching. + # Load weights and dispatch according to the device_map + # by default the device_map is None and the weights are loaded on the CPU + try: + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + except AttributeError as e: + # When using accelerate loading, we do not have the ability to load the state + # dict and rename the weight names manually. Additionally, accelerate skips + # torch loading conventions and directly writes into `module.{_buffers, _parameters}` + # (which look like they should be private variables?), so we can't use the standard hooks + # to rename parameters on load. We need to mimic the original weight names so the correct + # attributes are available. After we have loaded the weights, we convert the deprecated + # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert + # the weights so we don't have to do this again. + + if "'Attention' object has no attribute" in str(e): + logger.warn( + f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}" + " was saved with deprecated attention block weight names. We will load it with the deprecated attention block" + " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion," + " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint," + " please also re-upload it or open a PR on the original repository." + ) + model._temp_convert_self_to_deprecated_attention_blocks() + accelerate.load_checkpoint_and_dispatch( + model, + model_file, + device_map, + max_memory=max_memory, + offload_folder=offload_folder, + offload_state_dict=offload_state_dict, + dtype=torch_dtype, + ) + model._undo_temp_convert_self_to_deprecated_attention_blocks() + else: + raise e + + loading_info = { + "missing_keys": [], + "unexpected_keys": [], + "mismatched_keys": [], + "error_msgs": [], + } + else: + model = cls.from_config(config, **unused_kwargs) + + state_dict = load_state_dict(model_file, variant=variant) + model._convert_deprecated_attention_blocks(state_dict) + + model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model( + model, + state_dict, + model_file, + pretrained_model_name_or_path, + ignore_mismatched_sizes=ignore_mismatched_sizes, + ) + + loading_info = { + "missing_keys": missing_keys, + "unexpected_keys": unexpected_keys, + "mismatched_keys": mismatched_keys, + "error_msgs": error_msgs, + } + + if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype): + raise ValueError( + f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}." + ) + elif torch_dtype is not None: + model = model.to(torch_dtype) + + model.register_to_config(_name_or_path=pretrained_model_name_or_path) + + # Set model in evaluation mode to deactivate DropOut modules by default + model.eval() + if output_loading_info: + return model, loading_info + + return model diff --git a/pipelines/AniDoc.py b/pipelines/AniDoc.py new file mode 100644 index 0000000000000000000000000000000000000000..fe52b06054c2c18bae6f7eb7f791bf84a2169e4d --- /dev/null +++ b/pipelines/AniDoc.py @@ -0,0 +1,740 @@ +# Copyright 2023 The HuggingFace Team. 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. + +import inspect +from dataclasses import dataclass +from typing import Callable, Dict, List, Optional, Union + +import numpy as np +import PIL.Image +import torch +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +from diffusers.image_processor import VaeImageProcessor +# from diffusers.models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel +from diffusers.models import AutoencoderKLTemporalDecoder +from models_diffusers.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel +from diffusers.schedulers import EulerDiscreteScheduler +from diffusers.utils import BaseOutput, logging +from diffusers.utils.torch_utils import randn_tensor +from diffusers.pipelines.pipeline_utils import DiffusionPipeline + +# from models_diffusers.adapter_model import SparsePointAdapter +from models_diffusers.controlnet_svd import ControlNetSVDModel +from cotracker.predictor import CoTrackerPredictor, sample_trajectories, generate_gassian_heatmap + +from models_diffusers.camera.pose_adaptor import CameraPoseEncoder + +from einops import rearrange + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def _get_add_time_ids( + noise_aug_strength, + dtype, + batch_size, + fps=4, + motion_bucket_id=128, + unet=None, + ): + add_time_ids = [fps, motion_bucket_id, noise_aug_strength] + + passed_add_embed_dim = unet.config.addition_time_embed_dim * len(add_time_ids) + expected_add_embed_dim = unet.add_embedding.linear_1.in_features + + if expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + + return add_time_ids + + +def _append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") + return x[(...,) + (None,) * dims_to_append] + + +def tensor2vid(video: torch.Tensor, processor, output_type="np"): + # Based on: + # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 + + batch_size, channels, num_frames, height, width = video.shape + outputs = [] + for batch_idx in range(batch_size): + batch_vid = video[batch_idx].permute(1, 0, 2, 3) + batch_output = processor.postprocess(batch_vid, output_type) + + outputs.append(batch_output) + + return outputs + + +@dataclass +class AniDocPipelineOutput(BaseOutput): + r""" + Output class for zero-shot text-to-video pipeline. + + Args: + frames (`[List[PIL.Image.Image]`, `np.ndarray`]): + List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, + num_channels)`. + """ + + frames: Union[List[PIL.Image.Image], np.ndarray] + + +class AniDocPipeline(DiffusionPipeline): + r""" + Pipeline to generate video from an input image using Stable Video Diffusion. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. + image_encoder ([`~transformers.CLIPVisionModelWithProjection`]): + Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)). + unet ([`UNetSpatioTemporalConditionModel`]): + A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents. + scheduler ([`EulerDiscreteScheduler`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. + feature_extractor ([`~transformers.CLIPImageProcessor`]): + A `CLIPImageProcessor` to extract features from generated images. + """ + + model_cpu_offload_seq = "image_encoder->unet->vae" + _callback_tensor_inputs = ["latents"] + + def __init__( + self, + vae: AutoencoderKLTemporalDecoder, + image_encoder: CLIPVisionModelWithProjection, + unet: UNetSpatioTemporalConditionModel, + scheduler: EulerDiscreteScheduler, + feature_extractor: CLIPImageProcessor, + controlnet: Optional[ControlNetSVDModel] = None, + pose_encoder: Optional[CameraPoseEncoder] = None, + ): + super().__init__() + + self.register_modules( + vae=vae, + image_encoder=image_encoder, + unet=unet, + scheduler=scheduler, + feature_extractor=feature_extractor, + controlnet=controlnet, + pose_encoder=pose_encoder, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + + def _encode_image(self, image, device, num_videos_per_prompt, do_classifier_free_guidance): + dtype = next(self.image_encoder.parameters()).dtype + + if not isinstance(image, torch.Tensor): + image = self.image_processor.pil_to_numpy(image) + image = self.image_processor.numpy_to_pt(image) + + # We normalize the image before resizing to match with the original implementation. + # Then we unnormalize it after resizing. + image = image * 2.0 - 1.0 + image = _resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + + # Normalize the image with for CLIP input + image = self.feature_extractor( + images=image, + do_normalize=True, + do_center_crop=False, + do_resize=False, + do_rescale=False, + return_tensors="pt", + ).pixel_values + + image = image.to(device=device, dtype=dtype) + image_embeddings = self.image_encoder(image).image_embeds + image_embeddings = image_embeddings.unsqueeze(1) + + # duplicate image embeddings for each generation per prompt, using mps friendly method + bs_embed, seq_len, _ = image_embeddings.shape + image_embeddings = image_embeddings.repeat(1, num_videos_per_prompt, 1) + image_embeddings = image_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1) + + if do_classifier_free_guidance: + negative_image_embeddings = torch.zeros_like(image_embeddings) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + image_embeddings = torch.cat([negative_image_embeddings, image_embeddings]) + + return image_embeddings + + def _encode_vae_image( + self, + image: torch.Tensor, + device, + num_videos_per_prompt, + do_classifier_free_guidance, + ): + image = image.to(device=device) + image_latents = self.vae.encode(image).latent_dist.mode() + + if do_classifier_free_guidance: + negative_image_latents = torch.zeros_like(image_latents) + + # For classifier free guidance, we need to do two forward passes. + # Here we concatenate the unconditional and text embeddings into a single batch + # to avoid doing two forward passes + image_latents = torch.cat([negative_image_latents, image_latents]) + + # duplicate image_latents for each generation per prompt, using mps friendly method + image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1) + + return image_latents + + def _get_add_time_ids( + self, + fps, + motion_bucket_id, + noise_aug_strength, + dtype, + batch_size, + num_videos_per_prompt, + do_classifier_free_guidance, + ): + add_time_ids = [fps, motion_bucket_id, noise_aug_strength] + + passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids) + expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features + + if expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1) + + if do_classifier_free_guidance: + add_time_ids = torch.cat([add_time_ids, add_time_ids]) + + return add_time_ids + + def decode_latents(self, latents, num_frames, decode_chunk_size=14): + # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width] + latents = latents.flatten(0, 1) + + latents = 1 / self.vae.config.scaling_factor * latents + + accepts_num_frames = "num_frames" in set(inspect.signature(self.vae.forward).parameters.keys()) + + # decode decode_chunk_size frames at a time to avoid OOM + frames = [] + for i in range(0, latents.shape[0], decode_chunk_size): + num_frames_in = latents[i : i + decode_chunk_size].shape[0] + decode_kwargs = {} + if accepts_num_frames: + # we only pass num_frames_in if it's expected + decode_kwargs["num_frames"] = num_frames_in + + frame = self.vae.decode(latents[i : i + decode_chunk_size], **decode_kwargs).sample + frames.append(frame) + frames = torch.cat(frames, dim=0) + + # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width] + frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4) + + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + frames = frames.float() + return frames + + def check_inputs(self, image, height, width): + if ( + not isinstance(image, torch.Tensor) + and not isinstance(image, PIL.Image.Image) + and not isinstance(image, list) + ): + raise ValueError( + "`image` has to be of type `torch.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is" + f" {type(image)}" + ) + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + def prepare_latents( + self, + batch_size, + num_frames, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): + shape = ( + batch_size, + num_frames, + num_channels_latents // 2, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @property + def guidance_scale(self): + return self._guidance_scale + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None + + @property + def num_timesteps(self): + return self._num_timesteps + + @torch.no_grad() + def __call__( + self, + image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor], + controlnet_condition: torch.FloatTensor = None, + height: int = 576, + width: int = 1024, + num_frames: Optional[int] = None, + num_inference_steps: int = 25, + min_guidance_scale: float = 1.0, + max_guidance_scale: float = 3.0, + fps: int = 7, + motion_bucket_id: int = 127, + noise_aug_strength: int = 0.02, + decode_chunk_size: Optional[int] = None, + num_videos_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + return_dict: bool = True, + controlnet_cond_scale=1.0, + batch_size=1, + ): + r""" + The call function to the pipeline for generation. + + Args: + image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`): + Image or images to guide image generation. If you provide a tensor, it needs to be compatible with + [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json). + height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): + The width in pixels of the generated image. + num_frames (`int`, *optional*): + The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` and to 25 for `stable-video-diffusion-img2vid-xt` + num_inference_steps (`int`, *optional*, defaults to 25): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. This parameter is modulated by `strength`. + min_guidance_scale (`float`, *optional*, defaults to 1.0): + The minimum guidance scale. Used for the classifier free guidance with first frame. + max_guidance_scale (`float`, *optional*, defaults to 3.0): + The maximum guidance scale. Used for the classifier free guidance with last frame. + fps (`int`, *optional*, defaults to 7): + Frames per second. The rate at which the generated images shall be exported to a video after generation. + Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training. + motion_bucket_id (`int`, *optional*, defaults to 127): + The motion bucket ID. Used as conditioning for the generation. The higher the number the more motion will be in the video. + noise_aug_strength (`int`, *optional*, defaults to 0.02): + The amount of noise added to the init image, the higher it is the less the video will look like the init image. Increase it for more motion. + decode_chunk_size (`int`, *optional*): + The number of frames to decode at a time. The higher the chunk size, the higher the temporal consistency + between frames, but also the higher the memory consumption. By default, the decoder will decode all frames at once + for maximal quality. Reduce `decode_chunk_size` to reduce memory usage. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor is generated by sampling using the supplied random `generator`. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generated image. Choose between `PIL.Image` or `np.array`. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a + plain tuple. + + Returns: + [`~pipelines.stable_diffusion.StableVideoDiffusionInterpControlPipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableVideoDiffusionInterpControlPipelineOutput`] is returned, + otherwise a `tuple` is returned where the first element is a list of list with the generated frames. + + Examples: + + ```py + from diffusers import StableVideoDiffusionPipeline + from diffusers.utils import load_image, export_to_video + + pipe = StableVideoDiffusionPipeline.from_pretrained("stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16") + pipe.to("cuda") + + image = load_image("https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200") + image = image.resize((1024, 576)) + + frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0] + export_to_video(frames, "generated.mp4", fps=7) + ``` + """ + # 0. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + num_frames = num_frames if num_frames is not None else self.unet.config.num_frames + decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames + + # 1. Check inputs. Raise error if not correct + self.check_inputs(image, height, width) + + + # 2. Define call parameters + #if isinstance(image, PIL.Image.Image): + # batch_size = 1 + #elif isinstance(image, list): + # batch_size = len(image) + #else: + # batch_size = image.shape[0] + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = max_guidance_scale > 1.0 + + # 3. Encode input image + image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance) + + + # NOTE: Stable Diffusion Video was conditioned on fps - 1, which + # is why it is reduced here. + # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188 + fps = fps - 1 + + # 4. Encode input image using VAE + image = self.image_processor.preprocess(image, height=height, width=width) + noise = randn_tensor(image.shape, generator=generator, device=image.device, dtype=image.dtype) + image = image + noise_aug_strength * noise + + + + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + if needs_upcasting: + self.vae.to(dtype=torch.float32) + + image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance) + image_latents = image_latents.to(image_embeddings.dtype) + + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + + # Repeat the image latents for each frame so we can concatenate them with the noise + # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width] + # image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1) + #image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents + + # 5. Get Added Time IDs + added_time_ids = self._get_add_time_ids( + fps, + motion_bucket_id, + noise_aug_strength, + image_embeddings.dtype, + batch_size, + num_videos_per_prompt, + do_classifier_free_guidance, + ) + added_time_ids = added_time_ids.to(device) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_frames, + num_channels_latents, + height, + width, + image_embeddings.dtype, + device, + generator, + latents, + ) + + image_latents = image_latents.unsqueeze(1) # (1, 1, 4, h, w) + bsz, num_frames, _, latent_h, latent_w = latents.shape + bsz_cfg = bsz * 2 + image_latents=image_latents.repeat(1, num_frames, 1, 1, 1) + # image_end_latents = image_end_latents.unsqueeze(1) + #image_latents = torch.cat([image_latents, conditional_latents_mask, image_end_latents], dim=1) + + + + # concate the conditions + image_embeddings = image_embeddings + + # prepare controlnet condition + assert controlnet_condition.shape[2]==8, "Controlnet condition should have 8 channels" + # controlnet_condition = self.image_processor.preprocess(controlnet_condition, height=height, width=width) + # controlnet_condition = controlnet_condition.unsqueeze(0) + + controlnet_condition=controlnet_condition + controlnet_condition = torch.cat([controlnet_condition] * 2) + controlnet_condition = controlnet_condition.to(device, latents.dtype) + + # 7. Prepare guidance scale + guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0) + guidance_scale = guidance_scale.to(device, latents.dtype) + guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1) + guidance_scale = _append_dims(guidance_scale, latents.ndim) + + self._guidance_scale = guidance_scale + + noise_aug_strength = 0.02 + added_time_ids = _get_add_time_ids( + noise_aug_strength, + image_embeddings.dtype, + batch_size, + 6, + 128, + unet=self.unet, + ) + added_time_ids = torch.cat([added_time_ids] * 2) + added_time_ids = added_time_ids.to(latents.device) + + # 8. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + self._num_timesteps = len(timesteps) + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # Concatenate image_latents over channels dimention + latent_model_input = torch.cat([latent_model_input, image_latents], dim=2) + down_block_res_samples, mid_block_res_sample = self.controlnet( + latent_model_input, + t, + encoder_hidden_states=image_embeddings, + controlnet_cond=controlnet_condition, + added_time_ids=added_time_ids, + conditioning_scale=controlnet_cond_scale, + guess_mode=False, + return_dict=False, + ) + + # predict the noise residual + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=image_embeddings, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + added_time_ids=added_time_ids, + return_dict=False, + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents).prev_sample + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if not output_type == "latent": + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + frames = self.decode_latents(latents, num_frames, decode_chunk_size) + frames = tensor2vid(frames, self.image_processor, output_type=output_type) + else: + frames = latents + + self.maybe_free_model_hooks() + + if not return_dict: + return frames + + return AniDocPipelineOutput(frames=frames) + + +# resizing utils +# TODO: clean up later +def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True): + h, w = input.shape[-2:] + factors = (h / size[0], w / size[1]) + + # First, we have to determine sigma + # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 + sigmas = ( + max((factors[0] - 1.0) / 2.0, 0.001), + max((factors[1] - 1.0) / 2.0, 0.001), + ) + + # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma + # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 + # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now + ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) + + # Make sure it is odd + if (ks[0] % 2) == 0: + ks = ks[0] + 1, ks[1] + + if (ks[1] % 2) == 0: + ks = ks[0], ks[1] + 1 + + input = _gaussian_blur2d(input, ks, sigmas) + + output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) + return output + + +def _compute_padding(kernel_size): + """Compute padding tuple.""" + # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) + # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad + if len(kernel_size) < 2: + raise AssertionError(kernel_size) + computed = [k - 1 for k in kernel_size] + + # for even kernels we need to do asymmetric padding :( + out_padding = 2 * len(kernel_size) * [0] + + for i in range(len(kernel_size)): + computed_tmp = computed[-(i + 1)] + + pad_front = computed_tmp // 2 + pad_rear = computed_tmp - pad_front + + out_padding[2 * i + 0] = pad_front + out_padding[2 * i + 1] = pad_rear + + return out_padding + + +def _filter2d(input, kernel): + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) + + tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) + + height, width = tmp_kernel.shape[-2:] + + padding_shape: list[int] = _compute_padding([height, width]) + input = torch.nn.functional.pad(input, padding_shape, mode="reflect") + + # kernel and input tensor reshape to align element-wise or batch-wise params + tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) + input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) + + # convolve the tensor with the kernel. + output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) + + out = output.view(b, c, h, w) + return out + + +def _gaussian(window_size: int, sigma): + if isinstance(sigma, float): + sigma = torch.tensor([[sigma]]) + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + +def _gaussian_blur2d(input, kernel_size, sigma): + if isinstance(sigma, tuple): + sigma = torch.tensor([sigma], dtype=input.dtype) + else: + sigma = sigma.to(dtype=input.dtype) + + ky, kx = int(kernel_size[0]), int(kernel_size[1]) + bs = sigma.shape[0] + kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1)) + kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1)) + out_x = _filter2d(input, kernel_x[..., None, :]) + out = _filter2d(out_x, kernel_y[..., None]) + + return out diff --git a/scripts_infer/anidoc_inference.py b/scripts_infer/anidoc_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..c2687676899a25b750ad17a4d8278f41c0625437 --- /dev/null +++ b/scripts_infer/anidoc_inference.py @@ -0,0 +1,371 @@ +import sys + +from pyparsing import col +sys.path.insert(0,".") + +import argparse +from packaging import version +import glob +import os +from LightGlue.lightglue import LightGlue, SuperPoint, DISK, SIFT, ALIKED, DoGHardNet +from LightGlue.lightglue.utils import load_image, rbd +from cotracker.predictor import CoTrackerPredictor, sample_trajectories, generate_gassian_heatmap, sample_trajectories_with_ref +import torch +from diffusers.utils.import_utils import is_xformers_available + +from models_diffusers.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel +from pipelines.AniDoc import AniDocPipeline +from models_diffusers.controlnet_svd import ControlNetSVDModel +from diffusers.utils import load_image, export_to_video, export_to_gif +import time +from lineart_extractor.annotator.lineart import LineartDetector +import numpy as np +from PIL import Image +from utils import load_images_from_folder,export_gif_with_ref,export_gif_side_by_side,extract_frames_from_video,safe_round,select_multiple_points,generate_point_map,generate_point_map_frames,export_gif_side_by_side_complete,export_gif_side_by_side_complete_ablation +import random +import torchvision.transforms as T +from LightGlue.lightglue import viz2d +import matplotlib.pyplot as plt +from cotracker.utils.visualizer import Visualizer, read_video_from_path +from torchvision.transforms import PILToTensor + + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--pretrained_model_name_or_path", type=str, default="pretrained_weights/stable-video-diffusion-img2vid-xt", help="Path to the input image.") + + parser.add_argument( + "--pretrained_unet", type=str, help="Path to the input image.", + + default="pretrained_weights/anidoc" + + ) + parser.add_argument( + "--controlnet_model_name_or_path", type=str, help="Path to the input image.", + default="pretrained_weights/anidoc/controlnet" + ) + parser.add_argument("--output_dir", type=str, default=None, help="Path to the output video.") + parser.add_argument("--seed", type=int, default=42, help="random seed.") + + parser.add_argument("--noise_aug", type=float, default=0.02) + + parser.add_argument("--num_frames", type=int, default=14) + parser.add_argument("--width", type=int, default=512) + parser.add_argument("--height", type=int, default=320) + parser.add_argument("--all_sketch",action="store_true",help="all_sketch") + parser.add_argument("--not_quant_sketch",action="store_true",help="not_quant_sketch") + parser.add_argument("--repeat_sketch",action="store_true",help="not_quant_sketch") + parser.add_argument("--matching",action="store_true",help="add keypoint matching") + parser.add_argument("--tracking",action="store_true",help="tracking keypoint") + parser.add_argument("--repeat_matching",action="store_true",help="not tracking, but just simply repeat") + parser.add_argument("--tracker_point_init", type=str, default='gaussion', choices=['dift', 'gaussion', 'both'], help="Regular grid size") + parser.add_argument( + "--tracker_shift_grid", + type=int, default=0, choices=[0, 1], + help="shift the grid for the tracker") + parser.add_argument("--tracker_grid_size", type=int, default=8, help="Regular grid size") + parser.add_argument( + "--tracker_grid_query_frame", + type=int, + default=0, + help="Compute dense and grid tracks starting from this frame", + ) + parser.add_argument( + "--tracker_backward_tracking", + action="store_true", + help="Compute tracks in both directions, not only forward", + ) + parser.add_argument("--control_image", type=str, default=None, help="Path to the output video.") + parser.add_argument("--ref_image", type=str, default=None, help="Path to the output video.") + parser.add_argument("--max_points", type=int, default=10) + + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + + args = get_args() + dtype = torch.float16 + + + unet = UNetSpatioTemporalConditionModel.from_pretrained( + + args.pretrained_unet, + subfolder="unet", + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + custom_resume=True, + ) + + unet.to("cuda",dtype) + + if args.controlnet_model_name_or_path: + + controlnet = ControlNetSVDModel.from_pretrained( + args.controlnet_model_name_or_path, + ) + else: + + controlnet = ControlNetSVDModel.from_unet( + unet, + conditioning_channels=8 + ) + controlnet.to("cuda",dtype) + if is_xformers_available(): + import xformers + xformers_version = version.parse(xformers.__version__) + unet.enable_xformers_memory_efficient_attention() + else: + raise ValueError( + "xformers is not available. Make sure it is installed correctly") + + pipe = AniDocPipeline.from_pretrained( + + args.pretrained_model_name_or_path, + unet=unet, + controlnet=controlnet, + low_cpu_mem_usage=False, + torch_dtype=torch.float16, variant="fp16" + ) + pipe.to("cuda") + device = "cuda" + detector = LineartDetector(device) + extractor = SuperPoint(max_num_keypoints=2000).eval().to(device) # load the extractor + matcher = LightGlue(features='superpoint').eval().to(device) # load the matcher + + tracker = CoTrackerPredictor( + checkpoint="pretrained_weights/cotracker2.pth", + shift_grid=args.tracker_shift_grid, + ) + tracker.requires_grad_(False) + tracker.to(device, dtype=torch.float32) + + + width, height = args.width, args.height + + # image = load_image('dalle3_cat.jpg') + if args.output_dir is None: + args.output_dir = "results" + os.makedirs(args.output_dir, exist_ok=True) + + image_folder_list=[ + 'data_test/sample1.mp4', + ] + + ref_image_list=[ + "data_test/sample1.png", + ] + if args.ref_image is not None and args.control_image is not None: + ref_image_list=[args.ref_image] + image_folder_list=[args.control_image] + + + + for val_id ,each_sample in enumerate(image_folder_list): + if os.path.isdir(each_sample): + + control_images=load_images_from_folder(each_sample) + elif each_sample.endswith(".mp4"): + control_images = extract_frames_from_video(each_sample) + ref_image=load_image(ref_image_list[val_id]).resize((width, height)) + + + #resize: + for j, each in enumerate(control_images): + control_images[j]=control_images[j].resize((width, height)) + + # load image from folder + if args.all_sketch: + controlnet_image=[] + for k in range(len(control_images)): + sketch=control_images[k] + sketch = np.array(sketch) + sketch=detector(sketch,coarse=False) + sketch=np.repeat(sketch[:, :, np.newaxis], 3, axis=2) + if args.not_quant_sketch: + pass + else: + sketch= (sketch > 200).astype(np.uint8)*255 + sketch = Image.fromarray(sketch).resize((width, height)) + + controlnet_image.append(sketch) + + controlnet_sketch_condition = [T.ToTensor()(img).unsqueeze(0) for img in controlnet_image] + controlnet_sketch_condition = torch.cat(controlnet_sketch_condition, dim=0).unsqueeze(0).to(device, dtype=torch.float16) + controlnet_sketch_condition = (controlnet_sketch_condition - 0.5) / 0.5 #(1,14,3,h,w) + # matching condition + with torch.no_grad(): + ref_img_value = T.ToTensor()(ref_image).to(device, dtype=torch.float16) #(0,1) + + ref_img_value = ref_img_value.to(torch.float32) + current_img= T.ToTensor()(controlnet_image[0]).to(device, dtype=torch.float16) #(0,1) + current_img = current_img.to(torch.float32) + feats0 = extractor.extract(ref_img_value) + feats1 = extractor.extract(current_img) + matches01 = matcher({'image0': feats0, 'image1': feats1}) + feats0, feats1, matches01 = [rbd(x) for x in [feats0, feats1, matches01]] + matches = matches01['matches'] + points0 = feats0['keypoints'][matches[..., 0]] + points1 = feats1['keypoints'][matches[..., 1]] + points0 = points0.cpu().numpy() + # points0_org=points0.copy() + points1 = points1.cpu().numpy() + + points0 = safe_round(points0, current_img.shape) + points1 = safe_round(points1, current_img.shape) + + num_points = min(50, points0.shape[0]) + points0,points1 = select_multiple_points(points0, points1, num_points) + mask1, mask2 = generate_point_map(size=current_img.shape, coords0=points0, coords1=points1) + # import ipdb;ipdb.set_trace() + point_map1=torch.from_numpy(mask1) + point_map2=torch.from_numpy(mask2) + point_map1 = point_map1.unsqueeze(0).unsqueeze(0).unsqueeze(0).to(device, dtype=torch.float16) + point_map2 = point_map2.unsqueeze(0).unsqueeze(0).unsqueeze(0).to(device, dtype=torch.float16) + point_map=torch.cat([point_map1,point_map2],dim=2) + conditional_pixel_values=ref_img_value.unsqueeze(0).unsqueeze(0) + conditional_pixel_values = (conditional_pixel_values - 0.5) / 0.5 + + point_map_with_ref= torch.cat([point_map,conditional_pixel_values],dim=2) + original_shape = list(point_map_with_ref.shape) + new_shape = original_shape.copy() + new_shape[1] = args.num_frames-1 + + if args.repeat_matching: + matching_controlnet_image=point_map_with_ref.repeat(1,args.num_frames,1,1,1) + controlnet_condition=torch.cat([controlnet_sketch_condition, matching_controlnet_image], dim=2) + elif args.tracking: + with torch.no_grad(): + video_for_tracker = (controlnet_sketch_condition * 0.5 + 0.5) * 255. + queries = np.insert(points1,0,0,axis=1) + queries =torch.from_numpy(queries).to(device,torch.float).unsqueeze(0) + + if queries.shape[1]==0: + pred_tracks_sampled=None + points0_sampled = None + else: + pred_tracks, pred_visibility = tracker( + video_for_tracker.to(dtype=torch.float32), + queries=queries, + grid_size=args.tracker_grid_size, # 8 + grid_query_frame=args.tracker_grid_query_frame, # 0 + backward_tracking=args.tracker_backward_tracking, # False + # segm_mask=segm_mask, + ) + pred_tracks_sampled, pred_visibility_sampled,points0_sampled = sample_trajectories_with_ref( + pred_tracks.cpu(), pred_visibility.cpu(), torch.from_numpy(points0).unsqueeze(0).cpu(), + max_points=args.max_points, + motion_threshold=1, + vis_threshold=3, + ) + if pred_tracks_sampled is None: + mask1 = np.zeros((args.height, args.width), dtype=np.uint8) + mask2 = np.zeros((args.num_frames,args.height, args.width), dtype=np.uint8) + else: + pred_tracks_sampled = pred_tracks_sampled.squeeze(0).cpu().numpy() + pred_visibility_sampled =pred_visibility_sampled.squeeze(0).cpu().numpy() + points0_sampled =points0_sampled.squeeze(0).cpu().numpy() + for frame_id in range(args.num_frames): + pred_tracks_sampled[frame_id] = safe_round(pred_tracks_sampled[frame_id],current_img.shape) + points0_sampled = safe_round(points0_sampled,current_img.shape) + + mask1, mask2 = generate_point_map_frames(size=current_img.shape, coords0=points0_sampled,coords1=pred_tracks_sampled,visibility=pred_visibility_sampled) + + point_map1=torch.from_numpy(mask1) + point_map2=torch.from_numpy(mask2) + point_map1 = point_map1.unsqueeze(0).unsqueeze(0).repeat(1,args.num_frames,1,1,1).to(device, dtype=torch.float16) + point_map2 = point_map2.unsqueeze(0).unsqueeze(2).to(device, dtype=torch.float16) + point_map=torch.cat([point_map1,point_map2],dim=2) + + conditional_pixel_values_repeat=conditional_pixel_values.repeat(1,14,1,1,1) + + point_map_with_ref= torch.cat([point_map,conditional_pixel_values_repeat],dim=2) + controlnet_condition= torch.cat([controlnet_sketch_condition, point_map_with_ref], dim=2) + else: + zero_tensor = torch.zeros(new_shape).to(device, dtype=torch.float16) + matching_controlnet_image=torch.cat((point_map_with_ref,zero_tensor),dim=1) + controlnet_condition = torch.cat([controlnet_sketch_condition, matching_controlnet_image], dim=2) + + + ref_base_name=os.path.splitext(os.path.basename(ref_image_list[val_id]))[0] + sketch_base_name=os.path.splitext(os.path.basename(each_sample))[0] + supp_dir=os.path.join(args.output_dir,ref_base_name+"_"+sketch_base_name) + os.makedirs(supp_dir, exist_ok=True) + + elif args.repeat_sketch: + controlnet_image=[] + for i_2 in range(int(len(control_images)/2)): + sketch=control_images[0] + sketch = np.array(sketch) + sketch=detector(sketch,coarse=False) + sketch=np.repeat(sketch[:, :, np.newaxis], 3, axis=2) + + if args.not_quant_sketch: + pass + else: + sketch= (sketch > 200).astype(np.uint8)*255 + sketch = Image.fromarray(sketch) + controlnet_image.append(sketch) + for i_3 in range(int(len(control_images)/2)): + sketch=control_images[-1] + + + + sketch = np.array(sketch) + sketch=detector(sketch,coarse=False) + sketch=np.repeat(sketch[:, :, np.newaxis], 3, axis=2) + + if args.not_quant_sketch: + pass + else: + sketch= (sketch > 200).astype(np.uint8)*255 + sketch = Image.fromarray(sketch) + + controlnet_image.append(sketch) + + + + generator = torch.manual_seed(args.seed) + + + with torch.inference_mode(): + video_frames = pipe( + ref_image, + controlnet_condition, + height=args.height, + width=args.width, + num_frames=14, + decode_chunk_size=8, + motion_bucket_id=127, + fps=7, + noise_aug_strength=0.02, + generator=generator, + ).frames[0] + + + + + out_file = supp_dir+'.mp4' + + + if args.all_sketch: + + + export_gif_side_by_side_complete_ablation(ref_image,controlnet_image,video_frames,out_file.replace('.mp4','.gif'),supp_dir,6) + + elif args.repeat_sketch: + export_gif_with_ref(control_images[0],video_frames,controlnet_image[-1],controlnet_image[0],out_file.replace('.mp4','.gif'),6) + + + + + + + + + + + diff --git a/scripts_infer/anidoc_inference.sh b/scripts_infer/anidoc_inference.sh new file mode 100644 index 0000000000000000000000000000000000000000..028ee34ee8bcb8ce20496da54e20d6678f9897bd --- /dev/null +++ b/scripts_infer/anidoc_inference.sh @@ -0,0 +1 @@ +CUDA_VISIBLE_DEVICES=2 python scripts_infer/anidoc_inference.py --all_sketch --matching --tracking --control_image 'data_test/sample4_2.mp4' --ref_image 'data_test/sample4.png' --output_dir 'results' --max_point 10 \ No newline at end of file diff --git a/utils.py b/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b58e1aa0f3f15d356c701c1d943a2fde8f0d5578 --- /dev/null +++ b/utils.py @@ -0,0 +1,1130 @@ +import argparse +import math +import os +import cv2 +import subprocess +from datetime import timedelta +from urllib.parse import urlparse +import re +import numpy as np +import PIL +from PIL import Image, ImageDraw +import datetime +import torch +import torchvision +import torch.distributed as dist +from torch.utils.data.distributed import DistributedSampler +from torch.nn.parallel import DistributedDataParallel as DDP +import torchvision.transforms as transforms +import torch.nn.functional as F +import torch.utils.checkpoint +from einops import rearrange +import random +from skimage.metrics import structural_similarity as compare_ssim + +from diffusers.utils import load_image + + + + +def export_to_video(video_frames, output_video_path, fps): + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + h, w, _ = video_frames[0].shape + video_writer = cv2.VideoWriter( + output_video_path, fourcc, fps=fps, frameSize=(w, h)) + for i in range(len(video_frames)): + img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR) + video_writer.write(img) + + +def export_to_gif(frames, output_gif_path, fps): + """ + Export a list of frames to a GIF. + + Args: + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - output_gif_path (str): Path to save the output GIF. + - duration_ms (int): Duration of each frame in milliseconds. + + """ + # Convert numpy arrays to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance( + frame, np.ndarray) else frame for frame in frames] + + pil_frames[0].save(output_gif_path.replace('.mp4', '.gif'), + format='GIF', + append_images=pil_frames[1:], + save_all=True, + duration=100, + loop=0) + +from PIL import Image +import numpy as np + +def export_gif_with_ref(start_image, frames, end_image, reference_image, output_gif_path, fps): + """ + Export a list of frames into a GIF with columns and an additional version with only frames. + + Args: + - start_image (PIL.Image): The starting image. + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - end_image (PIL.Image): The ending image. + - reference_image (PIL.Image): The reference image. + - output_gif_path (str): Path to save the output GIF. + - fps (int): Frames per second for the GIF. + """ + + # Convert numpy frames to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] + + # Get dimensions of images + width, height = start_image.size + + # Resize the reference image and frames to match the height of start and end images if needed + reference_image = reference_image.resize((reference_image.width, height)) + resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames] + + # Create a new image for each frame with the three columns + column_frames = [] + for frame in resized_frames: + # Create an empty image with the total width for all three columns + new_width = start_image.width + reference_image.width + end_image.width+frame.width + combined_frame = Image.new('RGB', (new_width, height)) + + # Paste the start image, reference image, and frame into the new image + combined_frame.paste(start_image, (0, 0)) + combined_frame.paste(reference_image, (start_image.width, 0)) + combined_frame.paste(end_image, (start_image.width + reference_image.width, 0)) + combined_frame.paste(frame, (start_image.width + reference_image.width+end_image.width, 0)) + + column_frames.append(combined_frame) + + # Calculate frame duration in milliseconds based on fps + frame_duration = 150 + + # Save the GIF with columns + column_frames[0].save(output_gif_path, + format='GIF', + append_images=column_frames[1:], + save_all=True, + duration=frame_duration, + loop=0) + + + + + +def tensor_to_vae_latent(t, vae): + video_length = t.shape[1] + + t = rearrange(t, "b f c h w -> (b f) c h w") + latents = vae.encode(t).latent_dist.sample() + latents = rearrange(latents, "(b f) c h w -> b f c h w", f=video_length) + latents = latents * vae.config.scaling_factor + + return latents + + +def download_image(url): + original_image = ( + lambda image_url_or_path: load_image(image_url_or_path) + if urlparse(image_url_or_path).scheme + else PIL.Image.open(image_url_or_path).convert("RGB") + )(url) + return original_image + + +def map_ssim_distance(dis): + if dis > 0.95: + return 1 + elif dis > 0.9: + return 2 + elif dis > 0.85: + return 3 + elif dis > 0.80: + return 4 + elif dis > 0.75: + return 5 + elif dis > 0.70: + return 6 + elif dis > 0.65: + return 7 + elif dis > 0.60: + return 8 + elif dis > 0.55: + return 9 + else: + return 10 + + +def calculate_ssim(frame1, frame2): + # convert the frames to grayscale images since the compare_ssim function accepts grayscale images + gray_frame1 = cv2.cvtColor(frame1, cv2.COLOR_RGB2GRAY) + gray_frame2 = cv2.cvtColor(frame2, cv2.COLOR_RGB2GRAY) + + # compute SSIM + ssim = compare_ssim(gray_frame1, gray_frame2) + + return ssim + + +def mse(image1, image2): + err = np.sum((image1.astype("float") - image2.astype("float")) ** 2) + err /= float(image1.shape[0] * image1.shape[1]) + return err + + +def calculate_video_motion_distance(frames_data): + # obtain the number of frames in the video + frame_count, _, _, _ = frames_data.shape + + # init + similarities = [] + + # calculate the similarity between each two frames + for frame_index in range(1, frame_count): + prev_frame = frames_data[frame_index - 1, :, :, :] + current_frame = frames_data[frame_index, :, :, :] + + # calculate the similarity, you can choose to use SSIM or MSE, etc. + similarity = calculate_ssim(prev_frame, current_frame) + similarities.append(similarity) + + # calculate the mean similarity as the motion distance of the video + motion_distance = np.mean(similarities) + + return similarities, motion_distance + + + +def load_images_from_folder_to_pil(folder, target_size=(512, 512)): + images = [] + valid_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"} # Add or remove extensions as needed + + def frame_number(filename): + # Try the pattern 'frame_x_7fps' + new_pattern_match = re.search(r'frame_(\d+)_7fps', filename) + if new_pattern_match: + return int(new_pattern_match.group(1)) + + # If the new pattern is not found, use the original digit extraction method + matches = re.findall(r'\d+', filename) + if matches: + if matches[-1] == '0000' and len(matches) > 1: + return int(matches[-2]) # Return the second-to-last sequence if the last is '0000' + return int(matches[-1]) # Otherwise, return the last sequence + return float('inf') # Return 'inf' + + # Sorting files based on frame number + # sorted_files = sorted(os.listdir(folder), key=frame_number) + sorted_files = sorted(os.listdir(folder)) + + # Load, resize, and convert images + for filename in sorted_files: + ext = os.path.splitext(filename)[1].lower() + if ext in valid_extensions: + img_path = os.path.join(folder, filename) + img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # Read image with original channels + if img is not None: + # Resize image + img = cv2.resize(img, target_size, interpolation=cv2.INTER_AREA) + + # Convert to uint8 if necessary + if img.dtype == np.uint16: + img = (img / 256).astype(np.uint8) + + # Ensure all images are in RGB format + if len(img.shape) == 2: # Grayscale image + img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) + elif len(img.shape) == 3 and img.shape[2] == 3: # Color image in BGR format + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + # Convert the numpy array to a PIL image + pil_img = Image.fromarray(img) + images.append(pil_img) + + return images + +def extract_frames_from_video(video_path): + + video_capture = cv2.VideoCapture(video_path) + + frames = [] + + + if not video_capture.isOpened(): + + return frames + + + while True: + ret, frame = video_capture.read() + if not ret: + break + + + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + + pil_image = Image.fromarray(frame_rgb) + + frames.append(pil_image) + + video_capture.release() + + return frames + + +def export_gif_side_by_side(ref_frame,sketches, frames, output_gif_path, fps): + """ + Export a list of frames into a GIF with columns and an additional version with only frames. + + Args: + - start_image (PIL.Image): The starting image. + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - end_image (PIL.Image): The ending image. + - reference_image (PIL.Image): The reference image. + - output_gif_path (str): Path to save the output GIF. + - fps (int): Frames per second for the GIF. + """ + + # Convert numpy frames to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] + + # Get dimensions of images + width, height = pil_frames[0].size + + + resized_frames = [frame.resize((width, height)) for frame in pil_frames] + resized_sketches = [sketch.resize((width, height)) for sketch in sketches] + ref_frame=ref_frame.resize((width, height)) + # Create a new image for each frame with the three columns + column_frames = [] + for i, frame in enumerate(resized_frames): + # Create an empty image with the total width for all three columns + new_width = resized_sketches[0].width + frame.width+frame.width + combined_frame = Image.new('RGB', (new_width, height)) + + # Paste the start image, reference image, and frame into the new image + + combined_frame.paste(ref_frame, (0, 0)) + combined_frame.paste(resized_sketches[i], (resized_sketches[0].width, 0)) + + combined_frame.paste(frame, (resized_sketches[0].width+resized_sketches[0].width, 0)) + + column_frames.append(combined_frame) + + # Calculate frame duration in milliseconds based on fps + frame_duration = 150 + + # Save the GIF with columns + column_frames[0].save(output_gif_path, + format='GIF', + append_images=column_frames[1:], + save_all=True, + duration=frame_duration, + loop=0) + + +#shuffle operation + +def safe_round(coords, size): + height, width = size[1], size[2] + rounded_coords = np.round(coords).astype(int) + rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1) + rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1) + return rounded_coords +def random_number(num_points,size,coords0,coords1): + shuffle_indices = np.random.permutation(np.arange(coords0.shape[0])) + + + shuffled_coords0 = coords0[shuffle_indices] + shuffled_coords1 = coords1[shuffle_indices] + indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False) + + # selected_coords0 = coords0[indices] + # selected_coords1 = coords1[indices] + selected_coords0 = shuffled_coords0[indices] + selected_coords1 = shuffled_coords1[indices] + h, w = size[1], size[2] + mask0 = np.zeros((h, w), dtype=np.uint8) + mask1 = np.zeros((h, w), dtype=np.uint8) + for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)): + x0, y0 = coord0 + x1, y1 = coord1 + # import ipdb;ipdb.set_trace() + mask0[y0, x0] = i + 1 + mask1[y1, x1] = i + 1 + return mask0,mask1 + + +def split_and_shuffle(image, coordinates): + + assert image.shape[1] % 2 == 0 and image.shape[2] % 2 == 0, "Height and width must be even." + + + H, W = image.shape[1], image.shape[2] + + + patches_img = [ + image[:, :H//2, :W//2], + image[:, :H//2, W//2:], + image[:, H//2:, :W//2], + image[:, H//2:, W//2:] + ] + + patch_coords = [ + (0, H//2, 0, W//2), + (0, H//2, W//2, W), + (H//2, H, 0, W//2), + (H//2, H, W//2, W) + ] + + + indices = list(range(4)) + random.shuffle(indices) + + + new_patch_coords = [ + (0, 0), + (0, W//2), + (H//2, 0), + (H//2, W//2) + ] + + + new_coordinates = np.zeros_like(coordinates) + for i, (r, c) in enumerate(coordinates): + for idx, (r1, r2, c1, c2) in enumerate(patch_coords): + if r1 <= r < r2 and c1 <= c < c2: + new_r = r - r1 + new_patch_coords[indices.index(idx)][0] + new_c = c - c1 + new_patch_coords[indices.index(idx)][1] + new_coordinates[i] = [new_r, new_c] + break + + + shuffled_img = torch.cat([ + torch.cat([patches_img[indices[0]], patches_img[indices[1]]], dim=2), + torch.cat([patches_img[indices[2]], patches_img[indices[3]]], dim=2) + ], dim=1) + + return shuffled_img, new_coordinates + + +import os +import cv2 + +def extract_frames_from_videos(video_folder): + + for filename in os.listdir(video_folder): + if filename.endswith('.mp4'): + video_path = os.path.join(video_folder, filename) + + frames_folder = os.path.join("processed_video", os.path.splitext(filename)[0]) + os.makedirs(frames_folder, exist_ok=True) + + + cap = cv2.VideoCapture(video_path) + frame_count = 0 + + while True: + ret, frame = cap.read() + if not ret: + break + + frame_filename = os.path.join(frames_folder, f'frame_{frame_count:04d}.jpg') + + cv2.imwrite(frame_filename, frame) + frame_count += 1 + + cap.release() + print(f'Extracted {frame_count} frames from {filename} and saved to {frames_folder}') + + +def create_videos_from_frames(base_folder, output_folder, frame_rate=30): + + for root, dirs, files in os.walk(base_folder): + frames = [] + for file in sorted(files): + if file.endswith(('.jpg', '.png')): + frame_path = os.path.join(root, file) + frames.append(frame_path) + + if len(frames) == 14: + video_name = os.path.basename(root) + '.mp4' + video_path = os.path.join(output_folder, video_name) + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + first_frame = cv2.imread(frames[0]) + height, width, layers = first_frame.shape + video_writer = cv2.VideoWriter(video_path, fourcc, frame_rate, (width, height)) + + for frame in frames: + img = cv2.imread(frame) + video_writer.write(img) + + video_writer.release() + print(f'Created video: {video_path}') + +def random_rotate(image, angle_range=(-60, 60)): + angle = random.uniform(*angle_range) + return image.rotate(angle, fillcolor=(255, 255, 255)) + +def random_crop(image,ratio=0.9): + width, height = image.size + ratio = random.uniform(0.6, 1.0) + # print('ratio',ratio) + top = random.randint(0, height - int(height*ratio)) + left = random.randint(0, width - int(width*ratio)) + image=image.crop((left, top, left + int( width*ratio), top + int(height*ratio))) + image=image.resize((width,height)) + return image + +def random_flip(image): + if random.random() < 0.5: + image = image.transpose(Image.FLIP_LEFT_RIGHT) + if random.random() < 0.5: + image = image.transpose(Image.FLIP_TOP_BOTTOM) + return image + + +def patch_shuffle(image, num_patches): + + + C, H, W = image.shape + + assert H % num_patches == 0 and W % num_patches == 0, "Image dimensions must be divisible by num_patches" + + patch_size_h = H // num_patches + patch_size_w = W // num_patches + + + patches = image.unfold(1, patch_size_h, patch_size_h).unfold(2, patch_size_w, patch_size_w) + patches = patches.contiguous().view(C, num_patches * num_patches, patch_size_h, patch_size_w) + + + shuffle_idx = torch.randperm(num_patches * num_patches) + shuffled_patches = patches[:, shuffle_idx, :, :] + + + shuffled_patches = shuffled_patches.view(C, num_patches, num_patches, patch_size_h, patch_size_w) + shuffled_image = shuffled_patches.permute(0, 1, 3, 2, 4).contiguous() + shuffled_image = shuffled_image.view(C, H, W) + + return shuffled_image +def augment_image(image,k): + + image = random_rotate(image) + image = random_crop(image) + image = random_flip(image) + # torch_image = torchvision.transforms.ToTensor()(image) + # patch_shuffled_image = patch_shuffle(torch_image, k) + # to_pil = transforms.ToPILImage() + # image = to_pil(patch_shuffled_image) + + return image + + +def load_images_from_folder(folder): + image_list = [] + for filename in os.listdir(folder): + if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".jpeg"): + img_path = os.path.join(folder, filename) + try: + img = Image.open(img_path) + image_list.append(img) + except Exception as e: + print(f"Error loading image {filename}: {e}") + return image_list + + +def get_mask(model, input_img, s=640): + input_img = (input_img / 255).astype(np.float32) + h, w = h0, w0 = input_img.shape[:-1] + h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) + ph, pw = s - h, s - w + img_input = np.zeros([s, s, 3], dtype=np.float32) + img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h)) + img_input = np.transpose(img_input, (2, 0, 1)) + img_input = img_input[np.newaxis, :] + tmpImg = torch.from_numpy(img_input).type(torch.FloatTensor).to(model.device) + with torch.no_grad(): + + pred = model(tmpImg) + pred = pred.cpu().numpy()[0] + pred = np.transpose(pred, (1, 2, 0)) + pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] + pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis] + return pred + + +# code from + +def safe_round(coords, size): + height, width = size[1], size[2] + rounded_coords = np.round(coords).astype(int) + rounded_coords[:, 0] = np.clip(rounded_coords[:, 0], 0, width - 1) + rounded_coords[:, 1] = np.clip(rounded_coords[:, 1], 0, height - 1) + return rounded_coords +def random_number(num_points,size,coords0,coords1): + shuffle_indices = np.random.permutation(np.arange(coords0.shape[0])) + + + shuffled_coords0 = coords0[shuffle_indices] + shuffled_coords1 = coords1[shuffle_indices] + indices = np.random.choice(np.arange(shuffled_coords0.shape[0]), size=num_points, replace=False) + + # selected_coords0 = coords0[indices] + # selected_coords1 = coords1[indices] + selected_coords0 = shuffled_coords0[indices] + selected_coords1 = shuffled_coords1[indices] + h, w = size[1], size[2] + mask0 = np.zeros((h, w), dtype=np.uint8) + mask1 = np.zeros((h, w), dtype=np.uint8) + for i, (coord0, coord1) in enumerate(zip(selected_coords0, selected_coords1)): + x0, y0 = coord0 + x1, y1 = coord1 + # import ipdb;ipdb.set_trace() + mask0[y0, x0] = i + 1 + mask1[y1, x1] = i + 1 + return mask0,mask1 + + +import torch + +def split_and_shuffle(image, keypoints, num_rows, num_cols): + """ + Split the image into tiles, shuffle them, and update the keypoints accordingly. + + Parameters: + - image: Tensor of shape (3, H, W) + - keypoints: Tensor of shape (num_k, 2) + - num_rows: int, number of rows to split + - num_cols: int, number of columns to split + + Returns: + - shuffled_image: Tensor of shape (3, H, W) + - new_keypoints: Tensor of shape (num_k, 2) + """ + C, H, W = image.shape + + # Calculate padding to make H and W divisible by num_rows and num_cols + pad_h = (num_rows - H % num_rows) % num_rows + pad_w = (num_cols - W % num_cols) % num_cols + + # Pad the image + H_padded = H + pad_h + W_padded = W + pad_w + padded_image = torch.zeros((C, H_padded, W_padded), dtype=image.dtype).to(image.device) + padded_image[:, :H, :W] = image + + # Compute tile size + tile_height = H_padded // num_rows + tile_width = W_padded // num_cols + + # Reshape and permute to get tiles + tiles = padded_image.reshape(C, + num_rows, + tile_height, + num_cols, + tile_width) + tiles = tiles.permute(1, 3, 0, 2, 4).contiguous() + num_tiles = num_rows * num_cols + tiles = tiles.view(num_tiles, C, tile_height, tile_width) + + # Shuffle the tiles + idx_shuffle = torch.randperm(num_tiles).to(image.device) + tiles_shuffled = tiles[idx_shuffle] + + # Reshape back to image + tiles_shuffled = tiles_shuffled.view(num_rows, num_cols, C, tile_height, tile_width) + shuffled_image = tiles_shuffled.permute(2, 0, 3, 1, 4).contiguous() + shuffled_image = shuffled_image.view(C, H_padded, W_padded) + shuffled_image = shuffled_image[:, :H, :W] # Crop back to original size + + # Update keypoints + x = keypoints[:, 0] + y = keypoints[:, 1] + + # Compute the tile indices where the keypoints are located + tile_rows = (y / tile_height).long() + tile_cols = (x / tile_width).long() + tile_indices = tile_rows * num_cols + tile_cols # Shape: (num_k,) + + # Create inverse mapping from old tile indices to new tile positions + idx_unshuffle = torch.argsort(idx_shuffle) # idx_unshuffle[old_index] = new_index + + # Get new tile indices for each keypoint + new_tile_indices = idx_unshuffle[tile_indices] + new_tile_rows = new_tile_indices // num_cols + new_tile_cols = new_tile_indices % num_cols + + # Compute offsets within the tile + offset_x = x % tile_width + offset_y = y % tile_height + + # Compute new keypoints coordinates + new_x = new_tile_cols * tile_width + offset_x + new_y = new_tile_rows * tile_height + offset_y + + # Ensure keypoints are within image boundaries + new_x = new_x.clamp(0, W - 1) + new_y = new_y.clamp(0, H - 1) + + new_keypoints = torch.stack([new_x, new_y], dim=1) + + return shuffled_image, new_keypoints + +def generate_point_map(size, coords0, coords1): + + h, w = size[1], size[2] + mask0 = np.zeros((h, w), dtype=np.uint8) + mask1 = np.zeros((h, w), dtype=np.uint8) + for i, (coord0, coord1) in enumerate(zip(coords0, coords1)): + x0, y0 = coord0 + x1, y1 = coord1 + + x0, y0 = int(round(x0)), int(round(y0)) + x1, y1 = int(round(x1)), int(round(y1)) + + if 0 <= x0 < w and 0 <= y0 < h: + mask0[y0, x0] = i + 1 + if 0 <= x1 < w and 0 <= y1 < h: + mask1[y1, x1] = i + 1 + return mask0, mask1 + + +def select_multiple_points(points0, points1, num_points): + + N = len(points0) + num_points = min(num_points, N) + indices = np.random.choice(N, size=num_points, replace=False) + selected_points0 = points0[indices] + selected_points1 = points1[indices] + return selected_points0, selected_points1 + +def generate_point_map_frames(size, coords0, coords1,visibility): + + h, w = size[1], size[2] + mask0 = np.zeros((h, w), dtype=np.uint8) + num_frames = coords1.shape[0] + mask1 = np.zeros((num_frames, h, w), dtype=np.uint8) + + for i, coord0 in enumerate(coords0): + x0, y0 = coord0 + x0, y0 = int(round(x0)), int(round(y0)) + if 0 <= x0 < w and 0 <= y0 < h: + mask0[y0, x0] = i + 1 + + for frame_idx in range(num_frames): + coords_frame = coords1[frame_idx] + for i, coord1 in enumerate(coords_frame): + x1, y1 = coord1 + x1, y1 = int(round(x1)), int(round(y1)) + if 0 <= x1 < w and 0 <= y1 < h and visibility[frame_idx,i]==True: + mask1[frame_idx, y1, x1] = i + 1 + + return mask0, mask1 + + + +import numpy as np + +def extract_patches(image, coords, patch_size): + + N = coords.shape[0] + channels, H, W = image.shape + patches = np.zeros((N, channels, patch_size, patch_size), dtype=image.dtype) + half_size = patch_size // 2 + + for i in range(N): + x0, y0 = coords[i] + x0 = int(round(x0)) + y0 = int(round(y0)) + + # Define the patch region in the image + x_start_img = x0 - half_size + x_end_img = x0 + half_size + 1 + y_start_img = y0 - half_size + y_end_img = y0 + half_size + 1 + + # Define the region in the patch to fill + x_start_patch = 0 + y_start_patch = 0 + x_end_patch = patch_size + y_end_patch = patch_size + + # Adjust for boundaries + if x_start_img < 0: + x_start_patch = -x_start_img + x_start_img = 0 + if y_start_img < 0: + y_start_patch = -y_start_img + y_start_img = 0 + if x_end_img > W: + x_end_patch -= (x_end_img - W) + x_end_img = W + if y_end_img > H: + y_end_patch -= (y_end_img - H) + y_end_img = H + + # Calculate the actual sizes + patch_height = y_end_patch - y_start_patch + patch_width = x_end_patch - x_start_patch + img_height = y_end_img - y_start_img + img_width = x_end_img - x_start_img + + # Ensure the sizes match + if patch_height != img_height or patch_width != img_width: + min_height = min(patch_height, img_height) + min_width = min(patch_width, img_width) + y_end_patch = y_start_patch + min_height + y_end_img = y_start_img + min_height + x_end_patch = x_start_patch + min_width + x_end_img = x_start_img + min_width + + # Assign the image patch to the patches array + patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch] = \ + image[:, y_start_img:y_end_img, x_start_img:x_end_img] + + return patches + +def generate_point_feature_map_frames_naive(image, size, coords0, coords1, visibility, patch_size): + + channels, H, W = size + num_frames = coords1.shape[0] + N = coords0.shape[0] + + # Extract patches from the reference image at coords0 + patches = extract_patches(image, coords0, patch_size) + half_size = patch_size // 2 + + # Initialize the feature maps + feature_maps = np.zeros((num_frames, channels, H, W), dtype=image.dtype) + + for frame_idx in range(num_frames): + feature_map = np.zeros((channels, H, W), dtype=image.dtype) + coords_frame = coords1[frame_idx] + + for i in range(N): + if visibility[frame_idx, i]: + x1, y1 = coords_frame[i] + x1 = int(round(x1)) + y1 = int(round(y1)) + + # Define the patch region in the feature map + x_start_map = x1 - half_size + x_end_map = x1 + half_size + 1 + y_start_map = y1 - half_size + y_end_map = y1 + half_size + 1 + + # Define the region in the patch to use + x_start_patch = 0 + y_start_patch = 0 + x_end_patch = patch_size + y_end_patch = patch_size + + # Adjust for boundaries + if x_start_map < 0: + x_start_patch = -x_start_map + x_start_map = 0 + if y_start_map < 0: + y_start_patch = -y_start_map + y_start_map = 0 + if x_end_map > W: + x_end_patch -= (x_end_map - W) + x_end_map = W + if y_end_map > H: + y_end_patch -= (y_end_map - H) + y_end_map = H + + # Calculate the actual sizes + patch_height = y_end_patch - y_start_patch + patch_width = x_end_patch - x_start_patch + map_height = y_end_map - y_start_map + map_width = x_end_map - x_start_map + + # Ensure the sizes match + if patch_height != map_height or patch_width != map_width: + min_height = min(patch_height, map_height) + min_width = min(patch_width, map_width) + y_end_patch = y_start_patch + min_height + y_end_map = y_start_map + min_height + x_end_patch = x_start_patch + min_width + x_end_map = x_start_map + min_width + + # Place the patch into the feature map + feature_map[:, y_start_map:y_end_map, x_start_map:x_end_map] = \ + patches[i, :, y_start_patch:y_end_patch, x_start_patch:x_end_patch] + + feature_maps[frame_idx] = feature_map + + return feature_maps + + +import os +from PIL import Image +import numpy as np +from moviepy.editor import ImageSequenceClip + +def export_gif_side_by_side_complete(ref_frame, sketches, frames, output_gif_path, supp_dir,fps): + """ + Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches. + + Args: + - ref_frame (PIL.Image or np.ndarray): The reference image. + - sketches (list): List of sketch images (as numpy arrays or PIL Image objects). + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - output_gif_path (str): Path to save the output GIF. + - fps (int): Frames per second for the GIF and MP4. + """ + # Ensure the output directory exists + output_dir = os.path.dirname(output_gif_path) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Get the base name of the output file (without extension) + base_name = os.path.splitext(os.path.basename(output_gif_path))[0] + + # Create subdirectories for sketches and frames + sketch_dir = os.path.join(supp_dir,"sketches") + frame_dir = os.path.join(supp_dir,"frames") + os.makedirs(sketch_dir, exist_ok=True) + os.makedirs(frame_dir, exist_ok=True) + + # Convert numpy arrays to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] + pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches] + ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame + + # Get dimensions of images + width, height = pil_frames[0].size + + # Resize images + resized_frames = [frame.resize((width, height)) for frame in pil_frames] + resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches] + ref_frame = ref_frame.resize((width, height)) + + # Save each sketch frame + for i, sketch in enumerate(resized_sketches): + sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png") + sketch.save(sketch_filename) + + # Save each frame + for i, frame in enumerate(resized_frames): + frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") + frame.save(frame_filename) + + # Save reference frame + ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png") + ref_frame.save(ref_filename) + + # Create a new image for each frame with the three columns + column_frames = [] + for i, frame in enumerate(resized_frames): + # Create an empty image with the total width for all three columns + new_width = ref_frame.width + resized_sketches[i].width + frame.width + combined_frame = Image.new('RGB', (new_width, height)) + + # Paste the reference image, sketch, and frame into the new image + combined_frame.paste(ref_frame, (0, 0)) + combined_frame.paste(resized_sketches[i], (ref_frame.width, 0)) + combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0)) + + column_frames.append(combined_frame) + + # Calculate frame duration in milliseconds based on fps + frame_duration = int(1000 / fps) + + # Save the GIF with columns + column_frames[0].save(output_gif_path, + format='GIF', + append_images=column_frames[1:], + save_all=True, + duration=frame_duration, + loop=0) + + # Save the MP4 video with the same content + output_mp4_path = os.path.join(supp_dir , 'result.mp4') + # Convert PIL Images to numpy arrays for moviepy + video_frames = [np.array(frame) for frame in column_frames] + clip = ImageSequenceClip(video_frames, fps=fps) + clip.write_videofile(output_mp4_path, codec='libx264') + + + +def export_gif_with_ref_complete(start_image, frames, end_image, reference_image, output_gif_path, supp_dir, fps): + """ + Export a list of frames into a GIF with columns, save individual images and frames, + and create an MP4 video, following the storage method of 'export_gif_side_by_side_complete'. + + Args: + - start_image (PIL.Image or np.ndarray): The starting image. + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - end_image (PIL.Image or np.ndarray): The ending image. + - reference_image (PIL.Image or np.ndarray): The reference image. + - output_gif_path (str): Path to save the output GIF. + - supp_dir (str): Directory to save supplementary files. + - fps (int): Frames per second for the GIF and MP4. + """ + # Ensure the output directory exists + output_dir = os.path.dirname(output_gif_path) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Get the base name of the output file (without extension) + base_name = os.path.splitext(os.path.basename(output_gif_path))[0] + + # Create subdirectories for images and frames + start_end_dir = os.path.join(supp_dir, "start_end_images") + frame_dir = os.path.join(supp_dir, "frames") + reference_dir = os.path.join(supp_dir, "reference") + os.makedirs(start_end_dir, exist_ok=True) + os.makedirs(frame_dir, exist_ok=True) + os.makedirs(reference_dir, exist_ok=True) + + # Convert numpy arrays to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] + start_image = Image.fromarray(start_image) if isinstance(start_image, np.ndarray) else start_image + end_image = Image.fromarray(end_image) if isinstance(end_image, np.ndarray) else end_image + reference_image = Image.fromarray(reference_image) if isinstance(reference_image, np.ndarray) else reference_image + + # Get dimensions of images + width, height = start_image.size + + # Resize images to match the height + reference_image = reference_image.resize((reference_image.width, height)) + resized_frames = [frame.resize((frame.width, height)) for frame in pil_frames] + + # Save start_image, end_image, and reference_image + start_image_filename = os.path.join(start_end_dir, f"{base_name}_start.png") + start_image.save(start_image_filename) + end_image_filename = os.path.join(start_end_dir, f"{base_name}_end.png") + end_image.save(end_image_filename) + reference_image_filename = os.path.join(reference_dir, f"{base_name}_reference.png") + reference_image.save(reference_image_filename) + + # Save each frame + for i, frame in enumerate(resized_frames): + frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") + frame.save(frame_filename) + + # Create a new image for each frame with the columns + column_frames = [] + for i, frame in enumerate(resized_frames): + # Calculate the total width for all columns + new_width = start_image.width + reference_image.width + end_image.width + frame.width + combined_frame = Image.new('RGB', (new_width, height)) + + # Paste the images into the combined frame + combined_frame.paste(start_image, (0, 0)) + combined_frame.paste(reference_image, (start_image.width, 0)) + combined_frame.paste(end_image, (start_image.width + reference_image.width, 0)) + combined_frame.paste(frame, (start_image.width + reference_image.width + end_image.width, 0)) + + column_frames.append(combined_frame) + + # Calculate frame duration in milliseconds based on fps + frame_duration = int(1000 / fps) + + # Save the GIF with columns + column_frames[0].save(output_gif_path, + format='GIF', + append_images=column_frames[1:], + save_all=True, + duration=frame_duration, + loop=0) + + # Save the MP4 video with the same content + output_mp4_path = os.path.join(supp_dir, 'result.mp4') + # Convert PIL Images to numpy arrays for moviepy + video_frames = [np.array(frame) for frame in column_frames] + clip = ImageSequenceClip(video_frames, fps=fps) + clip.write_videofile(output_mp4_path, codec='libx264') + + +def export_gif_side_by_side_complete_ablation(ref_frame, sketches, frames, output_gif_path, supp_dir,fps): + """ + Export frames into a GIF and an MP4 video with columns, and save individual frames and sketches. + + Args: + - ref_frame (PIL.Image or np.ndarray): The reference image. + - sketches (list): List of sketch images (as numpy arrays or PIL Image objects). + - frames (list): List of frames (as numpy arrays or PIL Image objects). + - output_gif_path (str): Path to save the output GIF. + - fps (int): Frames per second for the GIF and MP4. + """ + # Ensure the output directory exists + output_dir = os.path.dirname(output_gif_path) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + # Get the base name of the output file (without extension) + base_name = os.path.splitext(os.path.basename(output_gif_path))[0] + + # Create subdirectories for sketches and frames + sketch_dir = os.path.join(supp_dir,"sketches") + frame_dir = os.path.join(supp_dir,"frames") + os.makedirs(sketch_dir, exist_ok=True) + os.makedirs(frame_dir, exist_ok=True) + + # Convert numpy arrays to PIL Images if needed + pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames] + pil_sketches = [Image.fromarray(sketch) if isinstance(sketch, np.ndarray) else sketch for sketch in sketches] + ref_frame = Image.fromarray(ref_frame) if isinstance(ref_frame, np.ndarray) else ref_frame + + # Get dimensions of images + width, height = pil_frames[0].size + + # Resize images + resized_frames = [frame.resize((width, height)) for frame in pil_frames] + resized_sketches = [sketch.resize((width, height)) for sketch in pil_sketches] + ref_frame = ref_frame.resize((width, height)) + + # Save each sketch frame + for i, sketch in enumerate(resized_sketches): + sketch_filename = os.path.join(sketch_dir, f"{base_name}_sketch_{i:04d}.png") + sketch.save(sketch_filename) + + # Save each frame + for i, frame in enumerate(resized_frames): + frame_filename = os.path.join(frame_dir, f"{base_name}_frame_{i:04d}.png") + frame.save(frame_filename) + + # Save reference frame + ref_filename = os.path.join(supp_dir, f"{base_name}_reference.png") + ref_frame.save(ref_filename) + + # Create a new image for each frame with the three columns + column_frames = [] + rgb_frames = [] + for i, frame in enumerate(resized_frames): + # Create an empty image with the total width for all three columns + new_width = ref_frame.width + resized_sketches[i].width + frame.width + combined_frame = Image.new('RGB', (new_width, height)) + + # Paste the reference image, sketch, and frame into the new image + combined_frame.paste(ref_frame, (0, 0)) + combined_frame.paste(resized_sketches[i], (ref_frame.width, 0)) + combined_frame.paste(frame, (ref_frame.width + resized_sketches[i].width, 0)) + + column_frames.append(combined_frame) + rgb_frames.append(frame) + + # Calculate frame duration in milliseconds based on fps + frame_duration = int(1000 / fps) + + # Save the GIF with columns + column_frames[0].save(output_gif_path, + format='GIF', + append_images=column_frames[1:], + save_all=True, + duration=frame_duration, + loop=0) + + # Save the MP4 video with the same content + output_mp4_path = supp_dir+'.mp4' + # Convert PIL Images to numpy arrays for moviepy + video_frames = [np.array(frame) for frame in column_frames] + rgb_frames = [np.array(frame) for frame in rgb_frames] + clip = ImageSequenceClip(rgb_frames, fps=fps) + clip.write_videofile(output_mp4_path, codec='libx264') \ No newline at end of file