This repository has been archived by the owner on Oct 31, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 748
/
run.py
862 lines (726 loc) · 40.9 KB
/
run.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
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
# Copyright (c) 2018-present, Facebook, Inc.
# 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 common.arguments import parse_args
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import os
import sys
import errno
from common.camera import *
from common.model import *
from common.loss import *
from common.generators import ChunkedGenerator, UnchunkedGenerator
from time import time
from common.utils import deterministic_random
args = parse_args()
print(args)
try:
# Create checkpoint directory if it does not exist
os.makedirs(args.checkpoint)
except OSError as e:
if e.errno != errno.EEXIST:
raise RuntimeError('Unable to create checkpoint directory:', args.checkpoint)
print('Loading dataset...')
dataset_path = 'data/data_3d_' + args.dataset + '.npz'
if args.dataset == 'h36m':
from common.h36m_dataset import Human36mDataset
dataset = Human36mDataset(dataset_path)
elif args.dataset.startswith('humaneva'):
from common.humaneva_dataset import HumanEvaDataset
dataset = HumanEvaDataset(dataset_path)
elif args.dataset.startswith('custom'):
from common.custom_dataset import CustomDataset
dataset = CustomDataset('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz')
else:
raise KeyError('Invalid dataset')
print('Preparing data...')
for subject in dataset.subjects():
for action in dataset[subject].keys():
anim = dataset[subject][action]
if 'positions' in anim:
positions_3d = []
for cam in anim['cameras']:
pos_3d = world_to_camera(anim['positions'], R=cam['orientation'], t=cam['translation'])
pos_3d[:, 1:] -= pos_3d[:, :1] # Remove global offset, but keep trajectory in first position
positions_3d.append(pos_3d)
anim['positions_3d'] = positions_3d
print('Loading 2D detections...')
keypoints = np.load('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz', allow_pickle=True)
keypoints_metadata = keypoints['metadata'].item()
keypoints_symmetry = keypoints_metadata['keypoints_symmetry']
kps_left, kps_right = list(keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(dataset.skeleton().joints_left()), list(dataset.skeleton().joints_right())
keypoints = keypoints['positions_2d'].item()
for subject in dataset.subjects():
assert subject in keypoints, 'Subject {} is missing from the 2D detections dataset'.format(subject)
for action in dataset[subject].keys():
assert action in keypoints[subject], 'Action {} of subject {} is missing from the 2D detections dataset'.format(action, subject)
if 'positions_3d' not in dataset[subject][action]:
continue
for cam_idx in range(len(keypoints[subject][action])):
# We check for >= instead of == because some videos in H3.6M contain extra frames
mocap_length = dataset[subject][action]['positions_3d'][cam_idx].shape[0]
assert keypoints[subject][action][cam_idx].shape[0] >= mocap_length
if keypoints[subject][action][cam_idx].shape[0] > mocap_length:
# Shorten sequence
keypoints[subject][action][cam_idx] = keypoints[subject][action][cam_idx][:mocap_length]
assert len(keypoints[subject][action]) == len(dataset[subject][action]['positions_3d'])
for subject in keypoints.keys():
for action in keypoints[subject]:
for cam_idx, kps in enumerate(keypoints[subject][action]):
# Normalize camera frame
cam = dataset.cameras()[subject][cam_idx]
kps[..., :2] = normalize_screen_coordinates(kps[..., :2], w=cam['res_w'], h=cam['res_h'])
keypoints[subject][action][cam_idx] = kps
subjects_train = args.subjects_train.split(',')
subjects_semi = [] if not args.subjects_unlabeled else args.subjects_unlabeled.split(',')
if not args.render:
subjects_test = args.subjects_test.split(',')
else:
subjects_test = [args.viz_subject]
semi_supervised = len(subjects_semi) > 0
if semi_supervised and not dataset.supports_semi_supervised():
raise RuntimeError('Semi-supervised training is not implemented for this dataset')
def fetch(subjects, action_filter=None, subset=1, parse_3d_poses=True):
out_poses_3d = []
out_poses_2d = []
out_camera_params = []
for subject in subjects:
for action in keypoints[subject].keys():
if action_filter is not None:
found = False
for a in action_filter:
if action.startswith(a):
found = True
break
if not found:
continue
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
if subject in dataset.cameras():
cams = dataset.cameras()[subject]
assert len(cams) == len(poses_2d), 'Camera count mismatch'
for cam in cams:
if 'intrinsic' in cam:
out_camera_params.append(cam['intrinsic'])
if parse_3d_poses and 'positions_3d' in dataset[subject][action]:
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
if len(out_camera_params) == 0:
out_camera_params = None
if len(out_poses_3d) == 0:
out_poses_3d = None
stride = args.downsample
if subset < 1:
for i in range(len(out_poses_2d)):
n_frames = int(round(len(out_poses_2d[i])//stride * subset)*stride)
start = deterministic_random(0, len(out_poses_2d[i]) - n_frames + 1, str(len(out_poses_2d[i])))
out_poses_2d[i] = out_poses_2d[i][start:start+n_frames:stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][start:start+n_frames:stride]
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
action_filter = None if args.actions == '*' else args.actions.split(',')
if action_filter is not None:
print('Selected actions:', action_filter)
cameras_valid, poses_valid, poses_valid_2d = fetch(subjects_test, action_filter)
filter_widths = [int(x) for x in args.architecture.split(',')]
if not args.disable_optimizations and not args.dense and args.stride == 1:
# Use optimized model for single-frame predictions
model_pos_train = TemporalModelOptimized1f(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], dataset.skeleton().num_joints(),
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels)
else:
# When incompatible settings are detected (stride > 1, dense filters, or disabled optimization) fall back to normal model
model_pos_train = TemporalModel(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], dataset.skeleton().num_joints(),
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
model_pos = TemporalModel(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], dataset.skeleton().num_joints(),
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
receptive_field = model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
if args.resume or args.evaluate:
chk_filename = os.path.join(args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'])
model_pos.load_state_dict(checkpoint['model_pos'])
if args.evaluate and 'model_traj' in checkpoint:
# Load trajectory model if it contained in the checkpoint (e.g. for inference in the wild)
model_traj = TemporalModel(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], 1,
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj.load_state_dict(checkpoint['model_traj'])
else:
model_traj = None
test_generator = UnchunkedGenerator(cameras_valid, poses_valid, poses_valid_2d,
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
print('INFO: Testing on {} frames'.format(test_generator.num_frames()))
if not args.evaluate:
cameras_train, poses_train, poses_train_2d = fetch(subjects_train, action_filter, subset=args.subset)
lr = args.learning_rate
if semi_supervised:
cameras_semi, _, poses_semi_2d = fetch(subjects_semi, action_filter, parse_3d_poses=False)
if not args.disable_optimizations and not args.dense and args.stride == 1:
# Use optimized model for single-frame predictions
model_traj_train = TemporalModelOptimized1f(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], 1,
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels)
else:
# When incompatible settings are detected (stride > 1, dense filters, or disabled optimization) fall back to normal model
model_traj_train = TemporalModel(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], 1,
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
model_traj = TemporalModel(poses_valid_2d[0].shape[-2], poses_valid_2d[0].shape[-1], 1,
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj_train = model_traj_train.cuda()
optimizer = optim.Adam(list(model_pos_train.parameters()) + list(model_traj_train.parameters()),
lr=lr, amsgrad=True)
losses_2d_train_unlabeled = []
losses_2d_train_labeled_eval = []
losses_2d_train_unlabeled_eval = []
losses_2d_valid = []
losses_traj_train = []
losses_traj_train_eval = []
losses_traj_valid = []
else:
optimizer = optim.Adam(model_pos_train.parameters(), lr=lr, amsgrad=True)
lr_decay = args.lr_decay
losses_3d_train = []
losses_3d_train_eval = []
losses_3d_valid = []
epoch = 0
initial_momentum = 0.1
final_momentum = 0.001
train_generator = ChunkedGenerator(args.batch_size//args.stride, cameras_train, poses_train, poses_train_2d, args.stride,
pad=pad, causal_shift=causal_shift, shuffle=True, augment=args.data_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
train_generator_eval = UnchunkedGenerator(cameras_train, poses_train, poses_train_2d,
pad=pad, causal_shift=causal_shift, augment=False)
print('INFO: Training on {} frames'.format(train_generator_eval.num_frames()))
if semi_supervised:
semi_generator = ChunkedGenerator(args.batch_size//args.stride, cameras_semi, None, poses_semi_2d, args.stride,
pad=pad, causal_shift=causal_shift, shuffle=True,
random_seed=4321, augment=args.data_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right,
endless=True)
semi_generator_eval = UnchunkedGenerator(cameras_semi, None, poses_semi_2d,
pad=pad, causal_shift=causal_shift, augment=False)
print('INFO: Semi-supervision on {} frames'.format(semi_generator_eval.num_frames()))
if args.resume:
epoch = checkpoint['epoch']
if 'optimizer' in checkpoint and checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
train_generator.set_random_state(checkpoint['random_state'])
else:
print('WARNING: this checkpoint does not contain an optimizer state. The optimizer will be reinitialized.')
lr = checkpoint['lr']
if semi_supervised:
model_traj_train.load_state_dict(checkpoint['model_traj'])
model_traj.load_state_dict(checkpoint['model_traj'])
semi_generator.set_random_state(checkpoint['random_state_semi'])
print('** Note: reported losses are averaged over all frames and test-time augmentation is not used here.')
print('** The final evaluation will be carried out after the last training epoch.')
# Pos model only
while epoch < args.epochs:
start_time = time()
epoch_loss_3d_train = 0
epoch_loss_traj_train = 0
epoch_loss_2d_train_unlabeled = 0
N = 0
N_semi = 0
model_pos_train.train()
if semi_supervised:
# Semi-supervised scenario
model_traj_train.train()
for (_, batch_3d, batch_2d), (cam_semi, _, batch_2d_semi) in \
zip(train_generator.next_epoch(), semi_generator.next_epoch()):
# Fall back to supervised training for the first epoch (to avoid instability)
skip = epoch < args.warmup
cam_semi = torch.from_numpy(cam_semi.astype('float32'))
inputs_3d = torch.from_numpy(batch_3d.astype('float32'))
if torch.cuda.is_available():
cam_semi = cam_semi.cuda()
inputs_3d = inputs_3d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Split point between labeled and unlabeled samples in the batch
split_idx = inputs_3d.shape[0]
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
inputs_2d_semi = torch.from_numpy(batch_2d_semi.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
inputs_2d_semi = inputs_2d_semi.cuda()
inputs_2d_cat = torch.cat((inputs_2d, inputs_2d_semi), dim=0) if not skip else inputs_2d
optimizer.zero_grad()
# Compute 3D poses
predicted_3d_pos_cat = model_pos_train(inputs_2d_cat)
loss_3d_pos = mpjpe(predicted_3d_pos_cat[:split_idx], inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
# Compute global trajectory
predicted_traj_cat = model_traj_train(inputs_2d_cat)
w = 1 / inputs_traj[:, :, :, 2] # Weight inversely proportional to depth
loss_traj = weighted_mpjpe(predicted_traj_cat[:split_idx], inputs_traj, w)
epoch_loss_traj_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_traj.item()
assert inputs_traj.shape[0]*inputs_traj.shape[1] == inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total += loss_traj
if not skip:
# Semi-supervised loss for unlabeled samples
predicted_semi = predicted_3d_pos_cat[split_idx:]
if pad > 0:
target_semi = inputs_2d_semi[:, pad:-pad, :, :2].contiguous()
else:
target_semi = inputs_2d_semi[:, :, :, :2].contiguous()
projection_func = project_to_2d_linear if args.linear_projection else project_to_2d
reconstruction_semi = projection_func(predicted_semi + predicted_traj_cat[split_idx:], cam_semi)
loss_reconstruction = mpjpe(reconstruction_semi, target_semi) # On 2D poses
epoch_loss_2d_train_unlabeled += predicted_semi.shape[0]*predicted_semi.shape[1] * loss_reconstruction.item()
if not args.no_proj:
loss_total += loss_reconstruction
# Bone length term to enforce kinematic constraints
if args.bone_length_term:
dists = predicted_3d_pos_cat[:, :, 1:] - predicted_3d_pos_cat[:, :, dataset.skeleton().parents()[1:]]
bone_lengths = torch.mean(torch.norm(dists, dim=3), dim=1)
penalty = torch.mean(torch.abs(torch.mean(bone_lengths[:split_idx], dim=0) \
- torch.mean(bone_lengths[split_idx:], dim=0)))
loss_total += penalty
N_semi += predicted_semi.shape[0]*predicted_semi.shape[1]
else:
N_semi += 1 # To avoid division by zero
loss_total.backward()
optimizer.step()
losses_traj_train.append(epoch_loss_traj_train / N)
losses_2d_train_unlabeled.append(epoch_loss_2d_train_unlabeled / N_semi)
else:
# Regular supervised scenario
for _, batch_3d, batch_2d in train_generator.next_epoch():
inputs_3d = torch.from_numpy(batch_3d.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_3d[:, :, 0] = 0
optimizer.zero_grad()
# Predict 3D poses
predicted_3d_pos = model_pos_train(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
loss_total = loss_3d_pos
loss_total.backward()
optimizer.step()
losses_3d_train.append(epoch_loss_3d_train / N)
# End-of-epoch evaluation
with torch.no_grad():
model_pos.load_state_dict(model_pos_train.state_dict())
model_pos.eval()
if semi_supervised:
model_traj.load_state_dict(model_traj_train.state_dict())
model_traj.eval()
epoch_loss_3d_valid = 0
epoch_loss_traj_valid = 0
epoch_loss_2d_valid = 0
N = 0
if not args.no_eval:
# Evaluate on test set
for cam, batch, batch_2d in test_generator.next_epoch():
inputs_3d = torch.from_numpy(batch.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Predict 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_valid += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
if semi_supervised:
cam = torch.from_numpy(cam.astype('float32'))
if torch.cuda.is_available():
cam = cam.cuda()
predicted_traj = model_traj(inputs_2d)
loss_traj = mpjpe(predicted_traj, inputs_traj)
epoch_loss_traj_valid += inputs_traj.shape[0]*inputs_traj.shape[1] * loss_traj.item()
assert inputs_traj.shape[0]*inputs_traj.shape[1] == inputs_3d.shape[0]*inputs_3d.shape[1]
if pad > 0:
target = inputs_2d[:, pad:-pad, :, :2].contiguous()
else:
target = inputs_2d[:, :, :, :2].contiguous()
reconstruction = project_to_2d(predicted_3d_pos + predicted_traj, cam)
loss_reconstruction = mpjpe(reconstruction, target) # On 2D poses
epoch_loss_2d_valid += reconstruction.shape[0]*reconstruction.shape[1] * loss_reconstruction.item()
assert reconstruction.shape[0]*reconstruction.shape[1] == inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_valid.append(epoch_loss_3d_valid / N)
if semi_supervised:
losses_traj_valid.append(epoch_loss_traj_valid / N)
losses_2d_valid.append(epoch_loss_2d_valid / N)
# Evaluate on training set, this time in evaluation mode
epoch_loss_3d_train_eval = 0
epoch_loss_traj_train_eval = 0
epoch_loss_2d_train_labeled_eval = 0
N = 0
for cam, batch, batch_2d in train_generator_eval.next_epoch():
if batch_2d.shape[1] == 0:
# This can only happen when downsampling the dataset
continue
inputs_3d = torch.from_numpy(batch.astype('float32'))
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_2d = inputs_2d.cuda()
inputs_traj = inputs_3d[:, :, :1].clone()
inputs_3d[:, :, 0] = 0
# Compute 3D poses
predicted_3d_pos = model_pos(inputs_2d)
loss_3d_pos = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_train_eval += inputs_3d.shape[0]*inputs_3d.shape[1] * loss_3d_pos.item()
N += inputs_3d.shape[0]*inputs_3d.shape[1]
if semi_supervised:
cam = torch.from_numpy(cam.astype('float32'))
if torch.cuda.is_available():
cam = cam.cuda()
predicted_traj = model_traj(inputs_2d)
loss_traj = mpjpe(predicted_traj, inputs_traj)
epoch_loss_traj_train_eval += inputs_traj.shape[0]*inputs_traj.shape[1] * loss_traj.item()
assert inputs_traj.shape[0]*inputs_traj.shape[1] == inputs_3d.shape[0]*inputs_3d.shape[1]
if pad > 0:
target = inputs_2d[:, pad:-pad, :, :2].contiguous()
else:
target = inputs_2d[:, :, :, :2].contiguous()
reconstruction = project_to_2d(predicted_3d_pos + predicted_traj, cam)
loss_reconstruction = mpjpe(reconstruction, target)
epoch_loss_2d_train_labeled_eval += reconstruction.shape[0]*reconstruction.shape[1] * loss_reconstruction.item()
assert reconstruction.shape[0]*reconstruction.shape[1] == inputs_3d.shape[0]*inputs_3d.shape[1]
losses_3d_train_eval.append(epoch_loss_3d_train_eval / N)
if semi_supervised:
losses_traj_train_eval.append(epoch_loss_traj_train_eval / N)
losses_2d_train_labeled_eval.append(epoch_loss_2d_train_labeled_eval / N)
# Evaluate 2D loss on unlabeled training set (in evaluation mode)
epoch_loss_2d_train_unlabeled_eval = 0
N_semi = 0
if semi_supervised:
for cam, _, batch_2d in semi_generator_eval.next_epoch():
cam = torch.from_numpy(cam.astype('float32'))
inputs_2d_semi = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
cam = cam.cuda()
inputs_2d_semi = inputs_2d_semi.cuda()
predicted_3d_pos_semi = model_pos(inputs_2d_semi)
predicted_traj_semi = model_traj(inputs_2d_semi)
if pad > 0:
target_semi = inputs_2d_semi[:, pad:-pad, :, :2].contiguous()
else:
target_semi = inputs_2d_semi[:, :, :, :2].contiguous()
reconstruction_semi = project_to_2d(predicted_3d_pos_semi + predicted_traj_semi, cam)
loss_reconstruction_semi = mpjpe(reconstruction_semi, target_semi)
epoch_loss_2d_train_unlabeled_eval += reconstruction_semi.shape[0]*reconstruction_semi.shape[1] \
* loss_reconstruction_semi.item()
N_semi += reconstruction_semi.shape[0]*reconstruction_semi.shape[1]
losses_2d_train_unlabeled_eval.append(epoch_loss_2d_train_unlabeled_eval / N_semi)
elapsed = (time() - start_time)/60
if args.no_eval:
print('[%d] time %.2f lr %f 3d_train %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000))
else:
if semi_supervised:
print('[%d] time %.2f lr %f 3d_train %f 3d_eval %f traj_eval %f 3d_valid %f '
'traj_valid %f 2d_train_sup %f 2d_train_unsup %f 2d_valid %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_train_eval[-1] * 1000,
losses_traj_train_eval[-1] * 1000,
losses_3d_valid[-1] * 1000,
losses_traj_valid[-1] * 1000,
losses_2d_train_labeled_eval[-1],
losses_2d_train_unlabeled_eval[-1],
losses_2d_valid[-1]))
else:
print('[%d] time %.2f lr %f 3d_train %f 3d_eval %f 3d_valid %f' % (
epoch + 1,
elapsed,
lr,
losses_3d_train[-1] * 1000,
losses_3d_train_eval[-1] * 1000,
losses_3d_valid[-1] *1000))
# Decay learning rate exponentially
lr *= lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
epoch += 1
# Decay BatchNorm momentum
momentum = initial_momentum * np.exp(-epoch/args.epochs * np.log(initial_momentum/final_momentum))
model_pos_train.set_bn_momentum(momentum)
if semi_supervised:
model_traj_train.set_bn_momentum(momentum)
# Save checkpoint if necessary
if epoch % args.checkpoint_frequency == 0:
chk_path = os.path.join(args.checkpoint, 'epoch_{}.bin'.format(epoch))
print('Saving checkpoint to', chk_path)
torch.save({
'epoch': epoch,
'lr': lr,
'random_state': train_generator.random_state(),
'optimizer': optimizer.state_dict(),
'model_pos': model_pos_train.state_dict(),
'model_traj': model_traj_train.state_dict() if semi_supervised else None,
'random_state_semi': semi_generator.random_state() if semi_supervised else None,
}, chk_path)
# Save training curves after every epoch, as .png images (if requested)
if args.export_training_curves and epoch > 3:
if 'matplotlib' not in sys.modules:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure()
epoch_x = np.arange(3, len(losses_3d_train)) + 1
plt.plot(epoch_x, losses_3d_train[3:], '--', color='C0')
plt.plot(epoch_x, losses_3d_train_eval[3:], color='C0')
plt.plot(epoch_x, losses_3d_valid[3:], color='C1')
plt.legend(['3d train', '3d train (eval)', '3d valid (eval)'])
plt.ylabel('MPJPE (m)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_3d.png'))
if semi_supervised:
plt.figure()
plt.plot(epoch_x, losses_traj_train[3:], '--', color='C0')
plt.plot(epoch_x, losses_traj_train_eval[3:], color='C0')
plt.plot(epoch_x, losses_traj_valid[3:], color='C1')
plt.legend(['traj. train', 'traj. train (eval)', 'traj. valid (eval)'])
plt.ylabel('Mean distance (m)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_traj.png'))
plt.figure()
plt.plot(epoch_x, losses_2d_train_labeled_eval[3:], color='C0')
plt.plot(epoch_x, losses_2d_train_unlabeled[3:], '--', color='C1')
plt.plot(epoch_x, losses_2d_train_unlabeled_eval[3:], color='C1')
plt.plot(epoch_x, losses_2d_valid[3:], color='C2')
plt.legend(['2d train labeled (eval)', '2d train unlabeled', '2d train unlabeled (eval)', '2d valid (eval)'])
plt.ylabel('MPJPE (2D)')
plt.xlabel('Epoch')
plt.xlim((3, epoch))
plt.savefig(os.path.join(args.checkpoint, 'loss_2d.png'))
plt.close('all')
# Evaluate
def evaluate(test_generator, action=None, return_predictions=False, use_trajectory_model=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
if not use_trajectory_model:
model_pos.eval()
else:
model_traj.eval()
N = 0
for _, batch, batch_2d in test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
if not use_trajectory_model:
predicted_3d_pos = model_pos(inputs_2d)
else:
predicted_3d_pos = model_traj(inputs_2d)
# Test-time augmentation (if enabled)
if test_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
if not use_trajectory_model:
predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if test_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----'+action+'----')
e1 = (epoch_loss_3d_pos / N)*1000
e2 = (epoch_loss_3d_pos_procrustes / N)*1000
e3 = (epoch_loss_3d_pos_scale / N)*1000
ev = (epoch_loss_3d_vel / N)*1000
print('Test time augmentation:', test_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
if args.render:
print('Rendering...')
input_keypoints = keypoints[args.viz_subject][args.viz_action][args.viz_camera].copy()
ground_truth = None
if args.viz_subject in dataset.subjects() and args.viz_action in dataset[args.viz_subject]:
if 'positions_3d' in dataset[args.viz_subject][args.viz_action]:
ground_truth = dataset[args.viz_subject][args.viz_action]['positions_3d'][args.viz_camera].copy()
if ground_truth is None:
print('INFO: this action is unlabeled. Ground truth will not be rendered.')
gen = UnchunkedGenerator(None, None, [input_keypoints],
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True)
if model_traj is not None and ground_truth is None:
prediction_traj = evaluate(gen, return_predictions=True, use_trajectory_model=True)
prediction += prediction_traj
if args.viz_export is not None:
print('Exporting joint positions to', args.viz_export)
# Predictions are in camera space
np.save(args.viz_export, prediction)
if args.viz_output is not None:
if ground_truth is not None:
# Reapply trajectory
trajectory = ground_truth[:, :1]
ground_truth[:, 1:] += trajectory
prediction += trajectory
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
if ground_truth is not None:
prediction = camera_to_world(prediction, R=cam['orientation'], t=cam['translation'])
ground_truth = camera_to_world(ground_truth, R=cam['orientation'], t=cam['translation'])
else:
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][args.viz_camera]:
rot = dataset.cameras()[subject][args.viz_camera]['orientation']
break
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
anim_output = {'Reconstruction': prediction}
if ground_truth is not None and not args.viz_no_ground_truth:
anim_output['Ground truth'] = ground_truth
input_keypoints = image_coordinates(input_keypoints[..., :2], w=cam['res_w'], h=cam['res_h'])
from common.visualization import render_animation
render_animation(input_keypoints, keypoints_metadata, anim_output,
dataset.skeleton(), dataset.fps(), args.viz_bitrate, cam['azimuth'], args.viz_output,
limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
input_video_path=args.viz_video, viewport=(cam['res_w'], cam['res_h']),
input_video_skip=args.viz_skip)
else:
print('Evaluating...')
all_actions = {}
all_actions_by_subject = {}
for subject in subjects_test:
if subject not in all_actions_by_subject:
all_actions_by_subject[subject] = {}
for action in dataset[subject].keys():
action_name = action.split(' ')[0]
if action_name not in all_actions:
all_actions[action_name] = []
if action_name not in all_actions_by_subject[subject]:
all_actions_by_subject[subject][action_name] = []
all_actions[action_name].append((subject, action))
all_actions_by_subject[subject][action_name].append((subject, action))
def fetch_actions(actions):
out_poses_3d = []
out_poses_2d = []
for subject, action in actions:
poses_2d = keypoints[subject][action]
for i in range(len(poses_2d)): # Iterate across cameras
out_poses_2d.append(poses_2d[i])
poses_3d = dataset[subject][action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
for i in range(len(poses_3d)): # Iterate across cameras
out_poses_3d.append(poses_3d[i])
stride = args.downsample
if stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_poses_3d, out_poses_2d
def run_evaluation(actions, action_filter=None):
errors_p1 = []
errors_p2 = []
errors_p3 = []
errors_vel = []
for action_key in actions.keys():
if action_filter is not None:
found = False
for a in action_filter:
if action_key.startswith(a):
found = True
break
if not found:
continue
poses_act, poses_2d_act = fetch_actions(actions[action_key])
gen = UnchunkedGenerator(None, poses_act, poses_2d_act,
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
e1, e2, e3, ev = evaluate(gen, action_key)
errors_p1.append(e1)
errors_p2.append(e2)
errors_p3.append(e3)
errors_vel.append(ev)
print('Protocol #1 (MPJPE) action-wise average:', round(np.mean(errors_p1), 1), 'mm')
print('Protocol #2 (P-MPJPE) action-wise average:', round(np.mean(errors_p2), 1), 'mm')
print('Protocol #3 (N-MPJPE) action-wise average:', round(np.mean(errors_p3), 1), 'mm')
print('Velocity (MPJVE) action-wise average:', round(np.mean(errors_vel), 2), 'mm')
if not args.by_subject:
run_evaluation(all_actions, action_filter)
else:
for subject in all_actions_by_subject.keys():
print('Evaluating on subject', subject)
run_evaluation(all_actions_by_subject[subject], action_filter)
print('')