-
Notifications
You must be signed in to change notification settings - Fork 82
/
video_generation.py
390 lines (344 loc) · 13.8 KB
/
video_generation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.
###############################################################################
# Copyright (C) 2022 Habana Labs, Ltd. an Intel Company
###############################################################################
# Changes:
# - added support for HPU device
import os
import glob
import sys
import argparse
import cv2
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms as pth_transforms
import numpy as np
from PIL import Image
import utils
import vision_transformer as vits
import habana_compat
FOURCC = {
"mp4": cv2.VideoWriter_fourcc(*"MP4V"),
"avi": cv2.VideoWriter_fourcc(*"XVID"),
}
class VideoGenerator:
def __init__(self, args):
self.args = args
# self.model = None
# Don't need to load model if you only want a video
if not self.args.video_only:
self.model = self.__load_model()
def run(self):
if self.args.input_path is None:
print(f"Provided input path {self.args.input_path} is non valid.")
sys.exit(1)
else:
if self.args.video_only:
self._generate_video_from_images(
self.args.input_path, self.args.output_path
)
else:
# If input path exists
if os.path.exists(self.args.input_path):
# If input is a video file
if os.path.isfile(self.args.input_path):
frames_folder = os.path.join(self.args.output_path, "frames")
attention_folder = os.path.join(
self.args.output_path, "attention"
)
os.makedirs(frames_folder, exist_ok=True)
os.makedirs(attention_folder, exist_ok=True)
self._extract_frames_from_video(
self.args.input_path, frames_folder
)
self._inference(
frames_folder,
attention_folder,
)
self._generate_video_from_images(
attention_folder, self.args.output_path
)
# If input is a folder of already extracted frames
if os.path.isdir(self.args.input_path):
attention_folder = os.path.join(
self.args.output_path, "attention"
)
os.makedirs(attention_folder, exist_ok=True)
self._inference(self.args.input_path, attention_folder)
self._generate_video_from_images(
attention_folder, self.args.output_path
)
# If input path doesn't exists
else:
print(f"Provided input path {self.args.input_path} doesn't exists.")
sys.exit(1)
def _extract_frames_from_video(self, inp: str, out: str):
vidcap = cv2.VideoCapture(inp)
self.args.fps = vidcap.get(cv2.CAP_PROP_FPS)
print(f"Video: {inp} ({self.args.fps} fps)")
print(f"Extracting frames to {out}")
success, image = vidcap.read()
count = 0
while success:
cv2.imwrite(
os.path.join(out, f"frame-{count:04}.jpg"),
image,
)
success, image = vidcap.read()
count += 1
def _generate_video_from_images(self, inp: str, out: str):
img_array = []
attention_images_list = sorted(glob.glob(os.path.join(inp, "attn-*.jpg")))
# Get size of the first image
with open(attention_images_list[0], "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
size = (img.width, img.height)
img_array.append(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))
print(f"Generating video {size} to {out}")
for filename in tqdm(attention_images_list[1:]):
with open(filename, "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
img_array.append(cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR))
out = cv2.VideoWriter(
os.path.join(out, "video." + self.args.video_format),
FOURCC[self.args.video_format],
self.args.fps,
size,
)
for i in range(len(img_array)):
out.write(img_array[i])
out.release()
print("Done")
def _inference(self, inp: str, out: str):
print(f"Generating attention images to {out}")
for img_path in tqdm(sorted(glob.glob(os.path.join(inp, "*.jpg")))):
with open(img_path, "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
if self.args.resize is not None:
transform = pth_transforms.Compose(
[
pth_transforms.ToTensor(),
pth_transforms.Resize(self.args.resize),
pth_transforms.Normalize(
(0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
),
]
)
else:
transform = pth_transforms.Compose(
[
pth_transforms.ToTensor(),
pth_transforms.Normalize(
(0.485, 0.456, 0.406), (0.229, 0.224, 0.225)
),
]
)
img = transform(img)
# make the image divisible by the patch size
w, h = (
img.shape[1] - img.shape[1] % self.args.patch_size,
img.shape[2] - img.shape[2] % self.args.patch_size,
)
img = img[:, :w, :h].unsqueeze(0)
w_featmap = img.shape[-2] // self.args.patch_size
h_featmap = img.shape[-1] // self.args.patch_size
attentions = self.model.get_last_selfattention(img.to(self.args.device))
habana_compat.mark_step()
nh = attentions.shape[1] # number of head
# we keep only the output patch attention
attentions = attentions[0, :, 0, 1:].reshape(nh, -1)
# we keep only a certain percentage of the mass
val, idx = torch.sort(attentions)
val /= torch.sum(val, dim=1, keepdim=True)
cumval = torch.cumsum(val, dim=1)
th_attn = cumval > (1 - self.args.threshold)
idx2 = torch.argsort(idx)
for head in range(nh):
th_attn[head] = th_attn[head][idx2[head]]
th_attn = th_attn.reshape(nh, w_featmap, h_featmap).float()
# interpolate
th_attn = (
nn.functional.interpolate(
th_attn.unsqueeze(0),
scale_factor=self.args.patch_size,
mode="nearest",
)[0]
.cpu()
.numpy()
)
habana_compat.mark_step()
attentions = attentions.reshape(nh, w_featmap, h_featmap)
attentions = (
nn.functional.interpolate(
attentions.unsqueeze(0),
scale_factor=self.args.patch_size,
mode="nearest",
)[0]
.cpu()
.numpy()
)
# save attentions heatmaps
fname = os.path.join(out, "attn-" + os.path.basename(img_path))
plt.imsave(
fname=fname,
arr=sum(
attentions[i] * 1 / attentions.shape[0]
for i in range(attentions.shape[0])
),
cmap="inferno",
format="jpg",
)
def __load_model(self):
# build model
model = vits.__dict__[self.args.arch](
patch_size=self.args.patch_size, num_classes=0
)
for p in model.parameters():
p.requires_grad = False
model.eval()
model.to(self.args.device)
if os.path.isfile(self.args.pretrained_weights):
state_dict = torch.load(self.args.pretrained_weights, map_location="cpu")
if (
self.args.checkpoint_key is not None
and self.args.checkpoint_key in state_dict
):
print(
f"Take key {self.args.checkpoint_key} in provided checkpoint dict"
)
state_dict = state_dict[self.args.checkpoint_key]
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
# remove `backbone.` prefix induced by multicrop wrapper
state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
msg = model.load_state_dict(state_dict, strict=False)
print(
"Pretrained weights found at {} and loaded with msg: {}".format(
self.args.pretrained_weights, msg
)
)
else:
print(
"Please use the `--pretrained_weights` argument to indicate the path of the checkpoint to evaluate."
)
url = None
if self.args.arch == "vit_small" and self.args.patch_size == 16:
url = "dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth"
elif self.args.arch == "vit_small" and self.args.patch_size == 8:
url = "dino_deitsmall8_300ep_pretrain/dino_deitsmall8_300ep_pretrain.pth" # model used for visualizations in our paper
elif self.args.arch == "vit_base" and self.args.patch_size == 16:
url = "dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth"
elif self.args.arch == "vit_base" and self.args.patch_size == 8:
url = "dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth"
if url is not None:
print(
"Since no pretrained weights have been provided, we load the reference pretrained DINO weights."
)
state_dict = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/dino/" + url
)
model.load_state_dict(state_dict, strict=True)
else:
print(
"There is no reference weights available for this model => We use random weights."
)
return model
def parse_args():
parser = argparse.ArgumentParser("Generation self-attention video")
# HPU
parser.add_argument('--device', choices=['hpu', 'cuda', 'cpu'], default='hpu',
help='Device to be used for computation')
parser.add_argument(
"--arch",
default="vit_small",
type=str,
choices=["vit_tiny", "vit_small", "vit_base"],
help="Architecture (support only ViT atm).",
)
parser.add_argument(
"--patch_size", default=8, type=int, help="Patch resolution of the self.model."
)
parser.add_argument(
"--pretrained_weights",
default="",
type=str,
help="Path to pretrained weights to load.",
)
parser.add_argument(
"--checkpoint_key",
default="teacher",
type=str,
help='Key to use in the checkpoint (example: "teacher")',
)
parser.add_argument(
"--input_path",
required=True,
type=str,
help="""Path to a video file if you want to extract frames
or to a folder of images already extracted by yourself.
or to a folder of attention images.""",
)
parser.add_argument(
"--output_path",
default="./",
type=str,
help="""Path to store a folder of frames and / or a folder of attention images.
and / or a final video. Default to current directory.""",
)
parser.add_argument(
"--threshold",
type=float,
default=0.6,
help="""We visualize masks
obtained by thresholding the self-attention maps to keep xx percent of the mass.""",
)
parser.add_argument(
"--resize",
default=None,
type=int,
nargs="+",
help="""Apply a resize transformation to input image(s). Use if OOM error.
Usage (single or W H): --resize 512, --resize 720 1280""",
)
parser.add_argument(
"--video_only",
action="store_true",
help="""Use this flag if you only want to generate a video and not all attention images.
If used, --input_path must be set to the folder of attention images. Ex: ./attention/""",
)
parser.add_argument(
"--fps",
default=30.0,
type=float,
help="FPS of input / output video. Automatically set if you extract frames from a video.",
)
parser.add_argument(
"--video_format",
default="mp4",
type=str,
choices=["mp4", "avi"],
help="Format of generated video (mp4 or avi).",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
habana_compat.setup_hpu(args)
vg = VideoGenerator(args)
vg.run()