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Configuration error
Configuration error
SupermanxKiaski
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Commit
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8e729e5
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Parent(s):
4919318
Upload video_dataset.py
Browse files- video_dataset.py +360 -0
video_dataset.py
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@@ -0,0 +1,360 @@
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1 |
+
import random
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2 |
+
|
3 |
+
import torch
|
4 |
+
import torch.nn.functional as F
|
5 |
+
from torch.utils.data import Dataset
|
6 |
+
from torchvision import transforms
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7 |
+
from torchvision.transforms.functional import crop
|
8 |
+
|
9 |
+
from models.video_model import VideoModel
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10 |
+
from util.atlas_utils import (
|
11 |
+
load_neural_atlases_models,
|
12 |
+
get_frames_data,
|
13 |
+
get_high_res_atlas,
|
14 |
+
get_atlas_crops,
|
15 |
+
reconstruct_video_layer,
|
16 |
+
create_uv_mask,
|
17 |
+
get_masks_boundaries,
|
18 |
+
get_random_crop_params,
|
19 |
+
get_atlas_bounding_box,
|
20 |
+
)
|
21 |
+
from util.util import load_video
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22 |
+
|
23 |
+
|
24 |
+
class AtlasDataset(Dataset):
|
25 |
+
def __init__(self, config):
|
26 |
+
self.config = config
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27 |
+
self.device = config["device"]
|
28 |
+
|
29 |
+
self.min_size = min(self.config["resx"], self.config["resy"])
|
30 |
+
self.max_size = max(self.config["resx"], self.config["resy"])
|
31 |
+
data_folder = f"data/videos/{self.config['checkpoint_path'].split('/')[2]}"
|
32 |
+
self.original_video = load_video(
|
33 |
+
data_folder,
|
34 |
+
resize=(self.config["resy"], self.config["resx"]),
|
35 |
+
num_frames=self.config["maximum_number_of_frames"],
|
36 |
+
).to(self.device)
|
37 |
+
|
38 |
+
(
|
39 |
+
foreground_mapping,
|
40 |
+
background_mapping,
|
41 |
+
foreground_atlas_model,
|
42 |
+
background_atlas_model,
|
43 |
+
alpha_model,
|
44 |
+
) = load_neural_atlases_models(config)
|
45 |
+
(
|
46 |
+
original_background_all_uvs,
|
47 |
+
original_foreground_all_uvs,
|
48 |
+
self.all_alpha,
|
49 |
+
foreground_atlas_alpha,
|
50 |
+
) = get_frames_data(
|
51 |
+
config,
|
52 |
+
foreground_mapping,
|
53 |
+
background_mapping,
|
54 |
+
alpha_model,
|
55 |
+
)
|
56 |
+
|
57 |
+
self.background_reconstruction = reconstruct_video_layer(original_background_all_uvs, background_atlas_model)
|
58 |
+
# using original video for the foreground layer
|
59 |
+
self.foreground_reconstruction = self.original_video * self.all_alpha
|
60 |
+
|
61 |
+
(
|
62 |
+
self.background_all_uvs,
|
63 |
+
self.scaled_background_uvs,
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64 |
+
self.background_min_u,
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65 |
+
self.background_min_v,
|
66 |
+
self.background_max_u,
|
67 |
+
self.background_max_v,
|
68 |
+
) = self.preprocess_uv_values(
|
69 |
+
original_background_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="background"
|
70 |
+
)
|
71 |
+
(
|
72 |
+
self.foreground_all_uvs,
|
73 |
+
self.scaled_foreground_uvs,
|
74 |
+
self.foreground_min_u,
|
75 |
+
self.foreground_min_v,
|
76 |
+
self.foreground_max_u,
|
77 |
+
self.foreground_max_v,
|
78 |
+
) = self.preprocess_uv_values(
|
79 |
+
original_foreground_all_uvs, config["grid_atlas_resolution"], device=self.device, layer="foreground"
|
80 |
+
)
|
81 |
+
|
82 |
+
self.background_uv_mask = create_uv_mask(
|
83 |
+
config,
|
84 |
+
background_mapping,
|
85 |
+
self.background_min_u,
|
86 |
+
self.background_min_v,
|
87 |
+
self.background_max_u,
|
88 |
+
self.background_max_v,
|
89 |
+
uv_shift=-0.5,
|
90 |
+
resolution_shift=1,
|
91 |
+
)
|
92 |
+
self.foreground_uv_mask = create_uv_mask(
|
93 |
+
config,
|
94 |
+
foreground_mapping,
|
95 |
+
self.foreground_min_u,
|
96 |
+
self.foreground_min_v,
|
97 |
+
self.foreground_max_u,
|
98 |
+
self.foreground_max_v,
|
99 |
+
uv_shift=0.5,
|
100 |
+
resolution_shift=0,
|
101 |
+
)
|
102 |
+
self.background_grid_atlas = get_high_res_atlas(
|
103 |
+
background_atlas_model,
|
104 |
+
self.background_min_v,
|
105 |
+
self.background_min_u,
|
106 |
+
self.background_max_v,
|
107 |
+
self.background_max_u,
|
108 |
+
config["grid_atlas_resolution"],
|
109 |
+
device=config["device"],
|
110 |
+
layer="background",
|
111 |
+
)
|
112 |
+
self.foreground_grid_atlas = get_high_res_atlas(
|
113 |
+
foreground_atlas_model,
|
114 |
+
self.foreground_min_v,
|
115 |
+
self.foreground_min_u,
|
116 |
+
self.foreground_max_v,
|
117 |
+
self.foreground_max_u,
|
118 |
+
config["grid_atlas_resolution"],
|
119 |
+
device=config["device"],
|
120 |
+
layer="foreground",
|
121 |
+
)
|
122 |
+
if config["return_atlas_alpha"]:
|
123 |
+
self.foreground_atlas_alpha = foreground_atlas_alpha # used for visualizations
|
124 |
+
self.cnn_min_crop_size = 2 ** self.config["num_scales"] + 1
|
125 |
+
if self.config["finetune_foreground"]:
|
126 |
+
self.mask_boundaries = get_masks_boundaries(
|
127 |
+
alpha_video=self.all_alpha.cpu(),
|
128 |
+
border=self.config["masks_border_expansion"],
|
129 |
+
threshold=self.config["mask_alpha_threshold"],
|
130 |
+
min_crop_size=self.cnn_min_crop_size,
|
131 |
+
)
|
132 |
+
self.cropped_foreground_atlas, self.foreground_atlas_bbox = get_atlas_bounding_box(
|
133 |
+
self.mask_boundaries, self.foreground_grid_atlas, self.foreground_all_uvs
|
134 |
+
)
|
135 |
+
|
136 |
+
self.step = -1
|
137 |
+
|
138 |
+
crop_transforms = transforms.Compose(
|
139 |
+
[
|
140 |
+
transforms.RandomApply(
|
141 |
+
[transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)],
|
142 |
+
p=0.1,
|
143 |
+
),
|
144 |
+
]
|
145 |
+
)
|
146 |
+
self.crop_aug = crop_transforms
|
147 |
+
self.dist = self.config["center_frame_distance"]
|
148 |
+
|
149 |
+
@staticmethod
|
150 |
+
def preprocess_uv_values(layer_uv_values, resolution, device="cuda", layer="background"):
|
151 |
+
if layer == "background":
|
152 |
+
shift = 1
|
153 |
+
else:
|
154 |
+
shift = 0
|
155 |
+
uv_values = (layer_uv_values + shift) * resolution
|
156 |
+
min_u, min_v = uv_values.reshape(-1, 2).min(dim=0).values.long()
|
157 |
+
uv_values -= torch.tensor([min_u, min_v], device=device)
|
158 |
+
max_u, max_v = uv_values.reshape(-1, 2).max(dim=0).values.ceil().long()
|
159 |
+
|
160 |
+
edge_size = torch.tensor([max_u, max_v], device=device)
|
161 |
+
scaled_uv_values = ((uv_values.reshape(-1, 2) / edge_size) * 2 - 1).unsqueeze(1).unsqueeze(0)
|
162 |
+
|
163 |
+
return uv_values, scaled_uv_values, min_u, min_v, max_u, max_v
|
164 |
+
|
165 |
+
def get_random_crop_data(self, crop_size):
|
166 |
+
t = random.randint(0, self.config["maximum_number_of_frames"] - 1)
|
167 |
+
y_start, x_start, h_crop, w_crop = get_random_crop_params((self.config["resx"], self.config["resy"]), crop_size)
|
168 |
+
return y_start, x_start, h_crop, w_crop, t
|
169 |
+
|
170 |
+
def get_global_crops_multi(self):
|
171 |
+
foreground_atlas_crops = []
|
172 |
+
background_atlas_crops = []
|
173 |
+
foreground_uvs = []
|
174 |
+
background_uvs = []
|
175 |
+
background_alpha_crops = []
|
176 |
+
foreground_alpha_crops = []
|
177 |
+
original_background_crops = []
|
178 |
+
original_foreground_crops = []
|
179 |
+
output_dict = {}
|
180 |
+
|
181 |
+
t = random.randint(self.dist, self.config["maximum_number_of_frames"] - 1 - self.dist)
|
182 |
+
flip = torch.rand(1) < self.config["flip_p"]
|
183 |
+
if self.config["finetune_foreground"]:
|
184 |
+
for cur_frame in [t - self.dist, t, t + self.dist]:
|
185 |
+
y_start, x_start, frame_h, frame_w = self.mask_boundaries[cur_frame].tolist()
|
186 |
+
crop_size = (
|
187 |
+
max(
|
188 |
+
random.randint(round(self.config["crops_min_cover"] * frame_h), frame_h),
|
189 |
+
self.cnn_min_crop_size,
|
190 |
+
),
|
191 |
+
max(
|
192 |
+
random.randint(round(self.config["crops_min_cover"] * frame_w), frame_w),
|
193 |
+
self.cnn_min_crop_size,
|
194 |
+
),
|
195 |
+
)
|
196 |
+
y_crop, x_crop, h_crop, w_crop = get_random_crop_params((frame_w, frame_h), crop_size)
|
197 |
+
|
198 |
+
foreground_uv = self.foreground_all_uvs[
|
199 |
+
cur_frame,
|
200 |
+
y_start + y_crop : y_start + y_crop + h_crop,
|
201 |
+
x_start + x_crop : x_start + x_crop + w_crop,
|
202 |
+
]
|
203 |
+
alpha = self.all_alpha[
|
204 |
+
[cur_frame],
|
205 |
+
:,
|
206 |
+
y_start + y_crop : y_start + y_crop + h_crop,
|
207 |
+
x_start + x_crop : x_start + x_crop + w_crop,
|
208 |
+
]
|
209 |
+
|
210 |
+
original_foreground_crop = self.foreground_reconstruction[
|
211 |
+
[cur_frame],
|
212 |
+
:,
|
213 |
+
y_start + y_crop : y_start + y_crop + h_crop,
|
214 |
+
x_start + x_crop : x_start + x_crop + w_crop,
|
215 |
+
]
|
216 |
+
|
217 |
+
original_foreground_crop = self.crop_aug(original_foreground_crop)
|
218 |
+
foreground_alpha_crops.append(alpha.flip(-1) if flip else alpha)
|
219 |
+
foreground_uvs.append(foreground_uv) # not scaled
|
220 |
+
original_foreground_crops.append(
|
221 |
+
original_foreground_crop.flip(-1) if flip else original_foreground_crop
|
222 |
+
)
|
223 |
+
|
224 |
+
foreground_min_vals = torch.tensor(
|
225 |
+
[self.config["grid_atlas_resolution"]] * 2, device=self.device, dtype=torch.long
|
226 |
+
)
|
227 |
+
foreground_max_vals = torch.tensor([0] * 2, device=self.device, dtype=torch.long)
|
228 |
+
for uv_values in foreground_uvs:
|
229 |
+
min_uv = uv_values.amin(dim=[0, 1]).long()
|
230 |
+
max_uv = uv_values.amax(dim=[0, 1]).ceil().long()
|
231 |
+
foreground_min_vals = torch.minimum(foreground_min_vals, min_uv)
|
232 |
+
foreground_max_vals = torch.maximum(foreground_max_vals, max_uv)
|
233 |
+
|
234 |
+
h_v = foreground_max_vals[1] - foreground_min_vals[1]
|
235 |
+
w_u = foreground_max_vals[0] - foreground_min_vals[0]
|
236 |
+
foreground_atlas_crop = crop(
|
237 |
+
self.foreground_grid_atlas,
|
238 |
+
foreground_min_vals[1],
|
239 |
+
foreground_min_vals[0],
|
240 |
+
h_v,
|
241 |
+
w_u,
|
242 |
+
)
|
243 |
+
foreground_atlas_crop = self.crop_aug(foreground_atlas_crop)
|
244 |
+
|
245 |
+
for i, uv_values in enumerate(foreground_uvs):
|
246 |
+
foreground_uvs[i] = (
|
247 |
+
2 * (uv_values - foreground_min_vals) / (foreground_max_vals - foreground_min_vals) - 1
|
248 |
+
).unsqueeze(0)
|
249 |
+
if flip:
|
250 |
+
foreground_uvs[i][:, :, :, 0] = -foreground_uvs[i][:, :, :, 0]
|
251 |
+
foreground_uvs[i] = foreground_uvs[i].flip(-2)
|
252 |
+
foreground_atlas_crops.append(foreground_atlas_crop.flip(-1) if flip else foreground_atlas_crop)
|
253 |
+
|
254 |
+
elif self.config["finetune_background"]:
|
255 |
+
crop_size = (
|
256 |
+
random.randint(round(self.config["crops_min_cover"] * self.min_size), self.min_size),
|
257 |
+
random.randint(round(self.config["crops_min_cover"] * self.max_size), self.max_size),
|
258 |
+
)
|
259 |
+
crop_data = self.get_random_crop_data(crop_size)
|
260 |
+
y, x, h, w, _ = crop_data
|
261 |
+
background_uv = self.background_all_uvs[[t - self.dist, t, t + self.dist], y : y + h, x : x + w]
|
262 |
+
original_background_crop = self.background_reconstruction[
|
263 |
+
[t - self.dist, t, t + self.dist], :, y : y + h, x : x + w
|
264 |
+
]
|
265 |
+
alpha = self.all_alpha[[t - self.dist, t, t + self.dist], :, y : y + h, x : x + w]
|
266 |
+
|
267 |
+
original_background_crop = self.crop_aug(original_background_crop)
|
268 |
+
|
269 |
+
original_background_crops = [
|
270 |
+
el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in original_background_crop
|
271 |
+
]
|
272 |
+
background_alpha_crops = [el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in alpha]
|
273 |
+
|
274 |
+
background_atlas_crop, background_min_vals, background_max_vals = get_atlas_crops(
|
275 |
+
background_uv,
|
276 |
+
self.background_grid_atlas,
|
277 |
+
self.crop_aug,
|
278 |
+
)
|
279 |
+
background_uv = 2 * (background_uv - background_min_vals) / (background_max_vals - background_min_vals) - 1
|
280 |
+
if flip:
|
281 |
+
background_uv[:, :, :, 0] = -background_uv[:, :, :, 0]
|
282 |
+
background_uv = background_uv.flip(-2)
|
283 |
+
background_atlas_crops = [
|
284 |
+
el.unsqueeze(0).flip(-1) if flip else el.unsqueeze(0) for el in background_atlas_crop
|
285 |
+
]
|
286 |
+
background_uvs = [el.unsqueeze(0) for el in background_uv]
|
287 |
+
|
288 |
+
if self.config["finetune_foreground"]:
|
289 |
+
output_dict["foreground_alpha"] = foreground_alpha_crops
|
290 |
+
output_dict["foreground_uvs"] = foreground_uvs
|
291 |
+
output_dict["original_foreground_crops"] = original_foreground_crops
|
292 |
+
output_dict["foreground_atlas_crops"] = foreground_atlas_crops
|
293 |
+
elif self.config["finetune_background"]:
|
294 |
+
output_dict["background_alpha"] = background_alpha_crops
|
295 |
+
output_dict["background_uvs"] = background_uvs
|
296 |
+
output_dict["original_background_crops"] = original_background_crops
|
297 |
+
output_dict["background_atlas_crops"] = background_atlas_crops
|
298 |
+
|
299 |
+
return output_dict
|
300 |
+
|
301 |
+
@torch.no_grad()
|
302 |
+
def render_video_from_atlas(self, model, layer="background", foreground_padding_mode="replicate"):
|
303 |
+
if layer == "background":
|
304 |
+
grid_atlas = self.background_grid_atlas
|
305 |
+
all_uvs = self.scaled_background_uvs
|
306 |
+
uv_mask = self.background_uv_mask
|
307 |
+
else:
|
308 |
+
grid_atlas = self.cropped_foreground_atlas
|
309 |
+
full_grid_atlas = self.foreground_grid_atlas
|
310 |
+
all_uvs = self.scaled_foreground_uvs
|
311 |
+
uv_mask = crop(self.foreground_uv_mask, *self.foreground_atlas_bbox)
|
312 |
+
atlas_edit_only = model.netG(grid_atlas)
|
313 |
+
edited_atlas_dict = model.render(atlas_edit_only, bg_image=grid_atlas)
|
314 |
+
|
315 |
+
if layer == "foreground":
|
316 |
+
atlas_edit_only = torch.nn.functional.pad(
|
317 |
+
atlas_edit_only,
|
318 |
+
pad=(
|
319 |
+
self.foreground_atlas_bbox[1],
|
320 |
+
full_grid_atlas.shape[-1] - (self.foreground_atlas_bbox[1] + self.foreground_atlas_bbox[3]),
|
321 |
+
self.foreground_atlas_bbox[0],
|
322 |
+
full_grid_atlas.shape[-2] - (self.foreground_atlas_bbox[0] + self.foreground_atlas_bbox[2]),
|
323 |
+
),
|
324 |
+
mode=foreground_padding_mode,
|
325 |
+
)
|
326 |
+
|
327 |
+
edit = F.grid_sample(
|
328 |
+
atlas_edit_only, all_uvs, mode="bilinear", align_corners=self.config["align_corners"]
|
329 |
+
).clamp(min=0.0, max=1.0)
|
330 |
+
edit = edit.squeeze().t() # shape (batch, 3)
|
331 |
+
edit = (
|
332 |
+
edit.reshape(self.config["maximum_number_of_frames"], self.config["resy"], self.config["resx"], 4)
|
333 |
+
.permute(0, 3, 1, 2)
|
334 |
+
.clamp(min=0.0, max=1.0)
|
335 |
+
)
|
336 |
+
edit_dict = model.render(edit, bg_image=self.original_video)
|
337 |
+
|
338 |
+
return edited_atlas_dict, edit_dict, uv_mask
|
339 |
+
|
340 |
+
def get_whole_atlas(self):
|
341 |
+
if self.config["finetune_foreground"]:
|
342 |
+
atlas = self.cropped_foreground_atlas
|
343 |
+
else:
|
344 |
+
atlas = self.background_grid_atlas
|
345 |
+
atlas = VideoModel.resize_crops(atlas, 3)
|
346 |
+
|
347 |
+
return atlas
|
348 |
+
|
349 |
+
def __getitem__(self, index):
|
350 |
+
self.step += 1
|
351 |
+
sample = {"step": self.step}
|
352 |
+
sample["global_crops"] = self.get_global_crops_multi()
|
353 |
+
|
354 |
+
if self.config["input_entire_atlas"] and ((self.step + 1) % self.config["entire_atlas_every"] == 0):
|
355 |
+
sample["input_image"] = self.get_whole_atlas()
|
356 |
+
|
357 |
+
return sample
|
358 |
+
|
359 |
+
def __len__(self):
|
360 |
+
return 1
|