-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathdataset.py
More file actions
366 lines (330 loc) · 18.6 KB
/
Copy pathdataset.py
File metadata and controls
366 lines (330 loc) · 18.6 KB
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
from torchvision import get_image_backend
from datasets.videodataset import VideoDataset
from datasets.videodataset_multiclips import (VideoDatasetMultiClips,
collate_fn)
from datasets.breakfast_segments import SegmentsDataset
from datasets.loader import VideoLoader, VideoLoaderDecord
import os
from pathlib import Path
def usual_image_name_formatter(x):
return f'image_{x:05d}.jpg'
def mnist_image_name_formatter(x):
return f'{x:d}.jpg'
def something_image_name_formatter(x):
return f'{x:05d}.jpg'
def avi_video_formatter(x):
return f'{x:d}.avi'
def mp4_video_formatter(x):
return f'{x:d}.mp4'
def get_training_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
n_segments=None,
spatial_transform=None,
temporal_transform=None,
target_transform=None,
use_image_features=False,
timm_model="xception41",
video_path_formatter = (lambda root_path, label,
video_id: root_path / label / f'{video_id}.mp4')):
assert dataset_name in [
'kinetics', 'mini_kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit',
'breakfast', 'mini_breakfast', 'movingmnist', 'movingmnist_blackframes',
'movingmnist_longterm', 'movingmnist_motiondiff', 'movingmnist_motionsame',
'movingmnist_frequencies', 'movingmnist_frequencies_complex', 'something',
'movingmnist_static', 'charades', 'multiTHUMOS', 'breakfast_subactions',
'breakfast_segments', 'multiTHUMOS_extended', 'breakfast_MNIST', 'MultiTHUMOS_MNIST',
'breakfast_MNIST_subactions'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5', 'None']
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if 'movingmnist' in dataset_name:
image_name_formatter = mnist_image_name_formatter
elif 'something' in dataset_name:
image_name_formatter = something_image_name_formatter
elif '_MNIST' in dataset_name:
#image_name_formatter = avi_video_formatter
image_name_formatter = mp4_video_formatter # To use with the noisy dataset
else: image_name_formatter = usual_image_name_formatter
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(image_name_formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(image_name_formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
if 'movingmnist' in dataset_name or 'something' in dataset_name:
video_path_formatter = (
lambda root_path, label, video_id: root_path / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderDecord()
if dataset_name in ['kinetics', 'mini_kinetics']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
'train_256' / label / f'{video_id}')
elif dataset_name in ['breakfast', 'breakfast_segments']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.mp4')
elif dataset_name in ['breakfast_subactions']:
#video_path_formatter = (lambda root_path, label, video_id: root_path /
# video_id.split("_")[2] /
# f'{"_".join(video_id.split("_")[:-1])}.mp4')
video_path_formatter = (lambda root_path, label, video_id: root_path /
video_id.split("_")[2] /
f'{"_".join(video_id.split("_")[:3])}.mp4')
elif dataset_name in ['charades']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{video_id}.mp4')
elif dataset_name in ['multiTHUMOS']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_"))}.mp4')
elif dataset_name in ['multiTHUMOS_extended']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_")[:-1])}.mp4')
elif dataset_name in ['hmdb51', 'ucf101']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.avi')
elif "_MNIST" in dataset_name and "subactions" not in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path / f'{video_id}.mp4')#.avi') # changed for dataset with noise
elif "_MNIST" in dataset_name and "subactions" in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join([video_id.split("_")[1]]+[video_id.split("_")[0].replace("stereoch0", "stereo00").replace("stereoch1", "stereo01")]+video_id.split("_")[1:3])}.mp4')
#print("video_path_formatter", video_path_formatter)
print("Building VideoDataset for", dataset_name)
if dataset_name == 'breakfast_segments':
training_data = SegmentsDataset(video_path,
annotation_path,
'training',
n_segments,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
else:
training_data = VideoDataset(video_path,
annotation_path,
'training',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
return training_data
def get_validation_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
n_segments=None,
spatial_transform=None,
temporal_transform=None,
target_transform=None,
use_image_features=False,
timm_model="xception41",
collate_fn=collate_fn,
video_path_formatter = (lambda root_path, label,
video_id: root_path / label / f'{video_id}.mp4')):
assert dataset_name in [
'kinetics', 'mini_kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit',
'breakfast', 'mini_breakfast', 'movingmnist', 'movingmnist_blackframes',
'movingmnist_longterm', 'movingmnist_motiondiff', 'movingmnist_motionsame',
'movingmnist_frequencies', 'movingmnist_frequencies_complex', 'something',
'movingmnist_static', 'charades', 'multiTHUMOS', 'breakfast_subactions',
'breakfast_segments', 'multiTHUMOS_extended', 'breakfast_MNIST', 'MultiTHUMOS_MNIST',
'breakfast_MNIST_subactions'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5', 'None']
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if 'movingmnist' in dataset_name:
image_name_formatter = mnist_image_name_formatter
elif 'something' in dataset_name:
image_name_formatter = something_image_name_formatter
elif '_MNIST' in dataset_name:
#image_name_formatter = avi_video_formatter
image_name_formatter = mp4_video_formatter # To use with the noisy dataset
else: image_name_formatter = usual_image_name_formatter
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(image_name_formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(image_name_formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
if 'movingmnist' in dataset_name or 'something' in dataset_name:
video_path_formatter = (
lambda root_path, label, video_id: root_path / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderDecord()
if dataset_name in ['kinetics', 'mini_kinetics']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
'val_256' / label / f'{video_id}')
elif dataset_name in ['breakfast', 'breakfast_segments']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.mp4')
elif dataset_name in ['breakfast_subactions']:
#video_path_formatter = (lambda root_path, label, video_id: root_path /
# video_id.split("_")[2] /
# f'{"_".join(video_id.split("_")[:-1])}.mp4')
video_path_formatter = (lambda root_path, label, video_id: root_path /
video_id.split("_")[2] /
f'{"_".join(video_id.split("_")[:3])}.mp4')
elif dataset_name in ['charades']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{video_id}.mp4')
elif dataset_name in ['multiTHUMOS']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_"))}.mp4')
elif dataset_name in ['multiTHUMOS_extended']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_")[:-1])}.mp4')
elif dataset_name in ['hmdb51', 'ucf101']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.avi')
elif "_MNIST" in dataset_name and "subactions" not in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path / f'{video_id}.mp4') #.avi') # changed for dataset with noise
elif "_MNIST" in dataset_name and "subactions" in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join([video_id.split("_")[1]]+[video_id.split("_")[0].replace("stereoch", "stereo0").replace("stereoch1", "stereo01")]+video_id.split("_")[1:3])}.mp4')
if dataset_name == 'breakfast_segments':
validation_data = SegmentsDataset(video_path,
annotation_path,
'validation',
n_segments,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
collate_fn = None
else:
validation_data = VideoDatasetMultiClips(
video_path,
annotation_path,
'validation',
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
return validation_data, collate_fn
def get_inference_data(video_path,
annotation_path,
dataset_name,
input_type,
file_type,
inference_subset,
n_segments=None,
spatial_transform=None,
temporal_transform=None,
target_transform=None,
use_image_features=False,
timm_model="xception41",
collate_fn=collate_fn,
video_path_formatter = (lambda root_path, label,
video_id: root_path / label / f'{video_id}.mp4')):
assert dataset_name in [
'kinetics', 'mini_kinetics', 'activitynet', 'ucf101', 'hmdb51', 'mit',
'breakfast', 'mini_breakfast', 'movingmnist', 'movingmnist_blackframes',
'movingmnist_longterm', 'movingmnist_motiondiff', 'movingmnist_motionsame',
'movingmnist_frequencies', 'movingmnist_frequencies_complex', 'something',
'movingmnist_static', 'charades', 'multiTHUMOS', 'breakfast_subactions',
'breakfast_segments', 'multiTHUMOS_extended', 'breakfast_MNIST', 'MultiTHUMOS_MNIST',
'breakfast_MNIST_subactions'
]
assert input_type in ['rgb', 'flow']
assert file_type in ['jpg', 'hdf5', 'None']
assert inference_subset in ['train', 'val', 'test']
if file_type == 'jpg':
assert input_type == 'rgb', 'flow input is supported only when input type is hdf5.'
if 'movingmnist' in dataset_name:
image_name_formatter = mnist_image_name_formatter
elif 'something' in dataset_name:
image_name_formatter = something_image_name_formatter
elif '_MNIST' in dataset_name:
#image_name_formatter = avi_video_formatter
image_name_formatter = mp4_video_formatter # To use with the noisy dataset
else: image_name_formatter = usual_image_name_formatter
if get_image_backend() == 'accimage':
from datasets.loader import ImageLoaderAccImage
loader = VideoLoader(image_name_formatter, ImageLoaderAccImage())
else:
loader = VideoLoader(image_name_formatter)
video_path_formatter = (
lambda root_path, label, video_id: root_path / label / video_id)
if dataset_name in ['movingmnist', 'movingmnist_blackframes',
'movingmnist_longterm', 'something']:
video_path_formatter = (
lambda root_path, label, video_id: root_path / video_id)
else:
if input_type == 'rgb':
loader = VideoLoaderDecord()
if dataset_name in ['kinetics', 'mini_kinetics']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}')
elif dataset_name in ['breakfast', 'breakfast_segments']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.mp4')
elif dataset_name in ['breakfast_subactions']:
#video_path_formatter = (lambda root_path, label, video_id: root_path /
# video_id.split("_")[2] /
# f'{"_".join(video_id.split("_")[:-1])}.mp4')
video_path_formatter = (lambda root_path, label, video_id: root_path /
video_id.split("_")[2] /
f'{"_".join(video_id.split("_")[:3])}.mp4')
elif dataset_name in ['charades']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{video_id}.mp4')
elif dataset_name in ['multiTHUMOS']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_"))}.mp4')
elif dataset_name in ['multiTHUMOS_extended']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join(video_id.split("_")[:-1])}.mp4')
elif dataset_name in ['hmdb51', 'ucf101']:
video_path_formatter = (lambda root_path, label, video_id: root_path /
label / f'{video_id}.avi')
elif "_MNIST" in dataset_name and "subactions" not in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path / f'{video_id}.mp4') #.avi') # changed for dataset with noise
elif "_MNIST" in dataset_name and "subactions" in dataset_name:
video_path_formatter = (lambda root_path, label, video_id: root_path /
f'{"_".join([video_id.split("_")[1]]+[video_id.split("_")[0].replace("stereoch", "stereo0").replace("stereoch1", "stereo01")]+video_id.split("_")[1:3])}.mp4')
if inference_subset == 'train':
subset = 'training'
elif inference_subset == 'val':
subset = 'validation'
elif inference_subset == 'test':
if dataset_name in ['breakfast_MNIST']:
subset = 'test'
else:
subset = 'testing'
if dataset_name == 'breakfast_segments':
inference_data = SegmentsDataset(video_path,
annotation_path,
'testing',
n_segments,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter)
collate_fn = None
else:
inference_data = VideoDatasetMultiClips(
video_path,
annotation_path,
subset,
spatial_transform=spatial_transform,
temporal_transform=temporal_transform,
target_transform=target_transform,
video_loader=loader,
video_path_formatter=video_path_formatter,
target_type=['label', 'video_id', 'segment'])
return inference_data, collate_fn