-
Notifications
You must be signed in to change notification settings - Fork 4
/
utils.py
executable file
·1256 lines (1119 loc) · 53.1 KB
/
utils.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
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/usr/bin/env python
# -*- coding: utf-8 -*-
''' Utilities file
This file contains utility functions for bookkeeping, logging, and data loading.
Methods which directly affect training should either go in layers, the model,
or train_fns.py.
'''
from __future__ import print_function
import sys
import os
import numpy as np
import time
import datetime
import json
import pickle
from argparse import ArgumentParser
import animal_hash
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import datasets as dset
import pdb
def prepare_parser():
usage = 'Parser for all scripts.'
parser = ArgumentParser(description=usage)
### Dataset/Dataloader stuff ###
parser.add_argument(
'--dataset', type=str, default='I128_hdf5',
help='Which Dataset to train on, out of I128, I256, C10, C100;'
'Append "_hdf5" to use the hdf5 version for ISLVRC '
'(default: %(default)s)')
parser.add_argument(
'--augment', action='store_true', default=False,
help='Augment with random crops and flips (default: %(default)s)')
parser.add_argument(
'--num_workers', type=int, default=8,
help='Number of dataloader workers; consider using less for HDF5 '
'(default: %(default)s)')
parser.add_argument(
'--no_pin_memory', action='store_false', dest='pin_memory', default=True,
help='Pin data into memory through dataloader? (default: %(default)s)')
parser.add_argument(
'--shuffle', action='store_true', default=False,
help='Shuffle the data (strongly recommended)? (default: %(default)s)')
parser.add_argument(
'--load_in_mem', action='store_true', default=False,
help='Load all data into memory? (default: %(default)s)')
parser.add_argument(
'--use_multiepoch_sampler', action='store_true', default=False,
help='Use the multi-epoch sampler for dataloader? (default: %(default)s)')
### Model stuff ###
parser.add_argument(
'--model', type=str, default='BigGAN',
help='Name of the model module (default: %(default)s)')
parser.add_argument(
'--G_param', type=str, default='SN',
help='Parameterization style to use for G, spectral norm (SN) or SVD (SVD)'
' or None (default: %(default)s)')
parser.add_argument(
'--D_param', type=str, default='SN',
help='Parameterization style to use for D, spectral norm (SN) or SVD (SVD)'
' or None (default: %(default)s)')
parser.add_argument(
'--n_classes', type=int, default=0,
help='Number of target classes')
parser.add_argument(
'--n_pretrain_classes', type=int, default=0,
help='Number of pre-training classes')
parser.add_argument(
'--G_ch', type=int, default=64,
help='Channel multiplier for G (default: %(default)s)')
parser.add_argument(
'--D_ch', type=int, default=64,
help='Channel multiplier for D (default: %(default)s)')
parser.add_argument(
'--G_depth', type=int, default=1,
help='Number of resblocks per stage in G? (default: %(default)s)')
parser.add_argument(
'--D_depth', type=int, default=1,
help='Number of resblocks per stage in D? (default: %(default)s)')
parser.add_argument(
'--D_thin', action='store_false', dest='D_wide', default=True,
help='Use the SN-GAN channel pattern for D? (default: %(default)s)')
parser.add_argument(
'--G_shared', action='store_true', default=False,
help='Use shared embeddings in G? (default: %(default)s)')
parser.add_argument(
'--shared_dim', type=int, default=0,
help='G''s shared embedding dimensionality; if 0, will be equal to dim_z. '
'(default: %(default)s)')
parser.add_argument(
'--dim_z', type=int, default=128,
help='Noise dimensionality: %(default)s)')
parser.add_argument(
'--z_var', type=float, default=1.0,
help='Noise variance: %(default)s)')
parser.add_argument(
'--hier', action='store_true', default=False,
help='Use hierarchical z in G? (default: %(default)s)')
parser.add_argument(
'--cross_replica', action='store_true', default=False,
help='Cross_replica batchnorm in G?(default: %(default)s)')
parser.add_argument(
'--mybn', action='store_true', default=False,
help='Use my batchnorm (which supports standing stats?) %(default)s)')
parser.add_argument(
'--G_nl', type=str, default='relu',
help='Activation function for G (default: %(default)s)')
parser.add_argument(
'--D_nl', type=str, default='relu',
help='Activation function for D (default: %(default)s)')
parser.add_argument(
'--G_attn', type=str, default='64',
help='What resolutions to use attention on for G (underscore separated) '
'(default: %(default)s)')
parser.add_argument(
'--D_attn', type=str, default='64',
help='What resolutions to use attention on for D (underscore separated) '
'(default: %(default)s)')
parser.add_argument(
'--norm_style', type=str, default='bn',
help='Normalizer style for G, one of bn [batchnorm], in [instancenorm], '
'ln [layernorm], gn [groupnorm] (default: %(default)s)')
### Model init stuff ###
parser.add_argument(
'--seed', type=int, default=0,
help='Random seed to use; affects both initialization and '
' dataloading. (default: %(default)s)')
parser.add_argument(
'--G_init', type=str, default='ortho',
help='Init style to use for G (default: %(default)s)')
parser.add_argument(
'--D_init', type=str, default='ortho',
help='Init style to use for D(default: %(default)s)')
parser.add_argument(
'--skip_init', action='store_true', default=False,
help='Skip initialization, ideal for testing when ortho init was used '
'(default: %(default)s)')
### Optimizer stuff ###
parser.add_argument(
'--G_lr', type=float, default=5e-5,
help='Learning rate to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_lr', type=float, default=2e-4,
help='Learning rate to use for Discriminator (default: %(default)s)')
parser.add_argument(
'--G_B1', type=float, default=0.0,
help='Beta1 to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_B1', type=float, default=0.0,
help='Beta1 to use for Discriminator (default: %(default)s)')
parser.add_argument(
'--G_B2', type=float, default=0.999,
help='Beta2 to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_B2', type=float, default=0.999,
help='Beta2 to use for Discriminator (default: %(default)s)')
### Batch size, parallel, and precision stuff ###
parser.add_argument(
'--batch_size', type=int, default=64,
help='Default overall batchsize (default: %(default)s)')
parser.add_argument(
'--G_batch_size', type=int, default=0,
help='Batch size to use for G; if 0, same as D (default: %(default)s)')
parser.add_argument(
'--num_G_accumulations', type=int, default=1,
help='Number of passes to accumulate G''s gradients over '
'(default: %(default)s)')
parser.add_argument(
'--num_D_steps', type=int, default=2,
help='Number of D steps per G step (default: %(default)s)')
parser.add_argument(
'--num_D_accumulations', type=int, default=1,
help='Number of passes to accumulate D''s gradients over '
'(default: %(default)s)')
parser.add_argument(
'--split_D', action='store_true', default=False,
help='Run D twice rather than concatenating inputs? (default: %(default)s)')
parser.add_argument(
'--num_epochs', type=int, default=100,
help='Number of epochs to train for (default: %(default)s)')
parser.add_argument(
'--parallel', action='store_true', default=False,
help='Train with multiple GPUs (default: %(default)s)')
parser.add_argument(
'--G_fp16', action='store_true', default=False,
help='Train with half-precision in G? (default: %(default)s)')
parser.add_argument(
'--D_fp16', action='store_true', default=False,
help='Train with half-precision in D? (default: %(default)s)')
parser.add_argument(
'--D_mixed_precision', action='store_true', default=False,
help='Train with half-precision activations but fp32 params in D? '
'(default: %(default)s)')
parser.add_argument(
'--G_mixed_precision', action='store_true', default=False,
help='Train with half-precision activations but fp32 params in G? '
'(default: %(default)s)')
parser.add_argument(
'--accumulate_stats', action='store_true', default=False,
help='Accumulate "standing" batchnorm stats? (default: %(default)s)')
parser.add_argument(
'--num_standing_accumulations', type=int, default=16,
help='Number of forward passes to use in accumulating standing stats? '
'(default: %(default)s)')
parser.add_argument(
'--num_learning_steps', type=int, default=0,
help='learning steps for stage 1')
### Bookkeping stuff ###
parser.add_argument(
'--G_eval_mode', action='store_true', default=False,
help='Run G in eval mode (running/standing stats?) at sample/test time? '
'(default: %(default)s)')
parser.add_argument(
'--save_every', type=int, default=2000,
help='Save every X iterations (default: %(default)s)')
parser.add_argument(
'--num_save_copies', type=int, default=2,
help='How many copies to save (default: %(default)s)')
parser.add_argument(
'--num_best_copies', type=int, default=2,
help='How many previous best checkpoints to save (default: %(default)s)')
parser.add_argument(
'--which_best', type=str, default='FID',
help='Which metric to use to determine when to save new "best"'
'checkpoints, one of IS or FID (default: %(default)s)')
parser.add_argument(
'--no_fid', action='store_true', default=False,
help='Calculate IS only, not FID? (default: %(default)s)')
parser.add_argument(
'--test_every', type=int, default=5000,
help='Test every X iterations (default: %(default)s)')
parser.add_argument(
'--num_inception_images', type=int, default=50000,
help='Number of samples to compute inception metrics with '
'(default: %(default)s)')
parser.add_argument(
'--hashname', action='store_true', default=False,
help='Use a hash of the experiment name instead of the full config '
'(default: %(default)s)')
parser.add_argument(
'--base_root', type=str,
default='./',
help='Default location to store all weights, samples, data, and logs '
' (default: %(default)s),/DATA/data/Biggan_pytorch/single_cate/n01440764_hdf, /DATA/data/Biggan_pytorch/good_original_data')
parser.add_argument(
'--data_root', type=str, default='',
help='Default location where data is stored (default: %(default)s)')
parser.add_argument(
'--weights_root', type=str, default='',
help='Default location to store weights (default: %(default)s)')
parser.add_argument(
'--logs_root', type=str, default='',
help='Default location to store logs (default: %(default)s)')
parser.add_argument(
'--samples_root', type=str, default='',
help='Default location to store samples (default: %(default)s)')
parser.add_argument(
'--pbar', type=str, default='mine',
help='Type of progressbar to use; one of "mine" or "tqdm" '
'(default: %(default)s)')
parser.add_argument(
'--name_suffix', type=str, default='',
help='Suffix for experiment name for loading weights for sampling '
'(consider "best0") (default: %(default)s)')
parser.add_argument(
'--experiment_name', type=str, default='',
help='Optionally override the automatic experiment naming with this arg. '
'(default: %(default)s)')
parser.add_argument(
'--config_from_name', action='store_true', default=False,
help='Use a hash of the experiment name instead of the full config '
'(default: %(default)s)')
### EMA Stuff ###
parser.add_argument(
'--ema', action='store_true', default=False,
help='Keep an ema of G''s weights? (default: %(default)s)')
parser.add_argument(
'--ema_decay', type=float, default=0.9999,
help='EMA decay rate (default: %(default)s)')
parser.add_argument(
'--use_ema', action='store_true', default=False,
help='Use the EMA parameters of G for evaluation? (default: %(default)s)')
parser.add_argument(
'--ema_start', type=int, default=0,
help='When to start updating the EMA weights (default: %(default)s)')
### Numerical precision and SV stuff ###
parser.add_argument(
'--adam_eps', type=float, default=1e-8,
help='epsilon value to use for Adam (default: %(default)s)')
parser.add_argument(
'--BN_eps', type=float, default=1e-5,
help='epsilon value to use for BatchNorm (default: %(default)s)')
parser.add_argument(
'--SN_eps', type=float, default=1e-8,
help='epsilon value to use for Spectral Norm(default: %(default)s)')
parser.add_argument(
'--num_G_SVs', type=int, default=1,
help='Number of SVs to track in G (default: %(default)s)')
parser.add_argument(
'--num_D_SVs', type=int, default=1,
help='Number of SVs to track in D (default: %(default)s)')
parser.add_argument(
'--num_G_SV_itrs', type=int, default=1,
help='Number of SV itrs in G (default: %(default)s)')
parser.add_argument(
'--num_D_SV_itrs', type=int, default=1,
help='Number of SV itrs in D (default: %(default)s)')
### Ortho reg stuff ###
parser.add_argument(
'--G_ortho', type=float, default=0.0, # 1e-4 is default for BigGAN
help='Modified ortho reg coefficient in G(default: %(default)s)')
parser.add_argument(
'--D_ortho', type=float, default=0.0,
help='Modified ortho reg coefficient in D (default: %(default)s)')
parser.add_argument(
'--toggle_grads', action='store_true', default=True,
help='Toggle D and G''s "requires_grad" settings when not training them? '
' (default: %(default)s)')
### Which train function ###
parser.add_argument(
'--which_train_fn', type=str, default='GAN',
help='How2trainyourbois (default: %(default)s)')
parser.add_argument(
'--stage', type=str, default="BN",
help='Stage of the training ["BN", "FT"]. Use "BN" for learning the batch normalziation parameters and "FT" for fine-tuning')
parser.add_argument(
'--freezeD_layer', type=int, default=0,
help='layer to freeze in D')
### Resume training stuff
parser.add_argument(
'--load_weights', type=str, default='',
help='Suffix for which weights to load (e.g. best0, copy0) '
'(default: %(default)s)')
parser.add_argument(
'--resume', action='store_true', default=False,
help='Resume training? (default: %(default)s)')
### Log stuff ###
parser.add_argument(
'--logstyle', type=str, default='%3.3e',
help='What style to use when logging training metrics?'
'One of: %#.#f/ %#.#e (float/exp, text),'
'pickle (python pickle),'
'npz (numpy zip),'
'mat (MATLAB .mat file) (default: %(default)s)')
parser.add_argument(
'--log_G_spectra', action='store_true', default=False,
help='Log the top 3 singular values in each SN layer in G? '
'(default: %(default)s)')
parser.add_argument(
'--log_D_spectra', action='store_true', default=False,
help='Log the top 3 singular values in each SN layer in D? '
'(default: %(default)s)')
parser.add_argument(
'--sv_log_interval', type=int, default=10,
help='Iteration interval for logging singular values '
' (default: %(default)s)')
## Regularization stuff
parser.add_argument(
'--res_l2_scale', type=float, default=0.0,
help='res_embed regularization')
parser.add_argument(
'--comb_l1_scale', type=float, default=0.0,
help='combination weights regularization')
return parser
# Arguments for sample.py; not presently used in train.py
def add_sample_parser(parser):
parser.add_argument(
'--sample_npz', action='store_true', default=False,
help='Sample "sample_num_npz" images and save to npz? '
'(default: %(default)s)')
parser.add_argument(
'--sample_num_npz', type=int, default=16,
help='Number of images to sample when sampling NPZs '
'(default: %(default)s)')
parser.add_argument(
'--sample_sheets', action='store_true', default=False,
help='Produce class-conditional sample sheets and stick them in '
'the samples root? (default: %(default)s)')
parser.add_argument(
'--sample_interps', action='store_true', default=False,
help='Produce interpolation sheets and stick them in '
'the samples root? (default: %(default)s)')
parser.add_argument(
'--sample_sheet_folder_num', type=int, default=-1,
help='Number to use for the folder for these sample sheets '
'(default: %(default)s)')
parser.add_argument(
'--sample_random', action='store_true', default=False,
help='Produce a single random sheet? (default: %(default)s)')
parser.add_argument(
'--sample_trunc_curves', type=str, default='',
help='Get inception metrics with a range of variances?'
'To use this, specify a startpoint, step, and endpoint, e.g. '
'--sample_trunc_curves 0.2_0.1_1.0 for a startpoint of 0.2, '
'endpoint of 1.0, and stepsize of 1.0. Note that this is '
'not exactly identical to using tf.truncated_normal, but should '
'have approximately the same effect. (default: %(default)s)')
parser.add_argument(
'--sample_inception_metrics', action='store_true', default=False,
help='Calculate Inception metrics with sample.py? (default: %(default)s)')
return parser
# Convenience dicts
dset_dict = {'I32': dset.ImageFolder, 'I64': dset.ImageFolder,
'I128': dset.ImageFolder, 'I256': dset.ImageFolder,
'I32_hdf5': dset.ILSVRC_HDF5, 'I64_hdf5': dset.ILSVRC_HDF5,
'I128_hdf5': dset.ILSVRC_HDF5, 'I256_hdf5': dset.ILSVRC_HDF5,
'C10': dset.CIFAR10, 'C100': dset.CIFAR100, 'C10_ImageFolder': dset.ImageFolder, 'C100_ImageFolder': dset.ImageFolder}
imsize_dict = {'I32': 32, 'I32_hdf5': 32,
'I64': 64, 'I64_hdf5': 64,
'I128': 128, 'I128_hdf5': 128,
'I256': 256, 'I256_hdf5': 256,
'C10': 32, 'C100': 32, 'C100_ImageFolder': 32}
root_dict = {'I32': 'ImageNet', 'I32_hdf5': 'ILSVRC32.hdf5',
'I64': 'ImageNet', 'I64_hdf5': 'ILSVRC64.hdf5',
'I128': 'ImageNet', 'I128_hdf5': 'ILSVRC128.hdf5',
'I256': 'ImageNet', 'I256_hdf5': 'ILSVRC256.hdf5',
'C10': 'cifar', 'C100': 'cifar', 'C10_ImageFolder': 'cifar', 'C100_ImageFolder':'cifar'}
nclass_dict = {'I32': 1000, 'I32_hdf5': 1000,
'I64': 1000, 'I64_hdf5': 1000,
'I128': 1000, 'I128_hdf5': 1000,
'I256': 1000, 'I256_hdf5': 1000,
'C10': 10, 'C100': 100, 'C10_ImageFolder': 10, 'C100_ImageFolder': 100}
# Number of classes to put per sample sheet
classes_per_sheet_dict = {'I32': 50, 'I32_hdf5': 50,
'I64': 50, 'I64_hdf5': 50,
'I128': 20, 'I128_hdf5': 20,
'I256': 20, 'I256_hdf5': 20,
'C10': 10, 'C100': 100,
'C10_ImageFolder': 10, 'C100_ImageFolder': 100}
activation_dict = {'inplace_relu': nn.ReLU(inplace=True),
'relu': nn.ReLU(inplace=False),
'ir': nn.ReLU(inplace=True), }
class CenterCropLongEdge(object):
"""Crops the given PIL Image on the long edge.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
return transforms.functional.center_crop(img, min(img.size))
def __repr__(self):
return self.__class__.__name__
class RandomCropLongEdge(object):
"""Crops the given PIL Image on the long edge with a random start point.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
size = (min(img.size), min(img.size))
# Only step forward along this edge if it's the long edge
i = (0 if size[0] == img.size[0]
else np.random.randint(low=0, high=img.size[0] - size[0]))
j = (0 if size[1] == img.size[1]
else np.random.randint(low=0, high=img.size[1] - size[1]))
return transforms.functional.crop(img, i, j, size[0], size[1])
def __repr__(self):
return self.__class__.__name__
# multi-epoch Dataset sampler to avoid memory leakage and enable resumption of
# training from the same sample regardless of if we stop mid-epoch
class MultiEpochSampler(torch.utils.data.Sampler):
r"""Samples elements randomly over multiple epochs
Arguments:
data_source (Dataset): dataset to sample from
num_epochs (int) : Number of times to loop over the dataset
start_itr (int) : which iteration to begin from
"""
def __init__(self, data_source, num_epochs, start_itr=0, batch_size=128):
self.data_source = data_source
self.num_samples = len(self.data_source)
self.num_epochs = num_epochs
self.start_itr = start_itr
self.batch_size = batch_size
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError("num_samples should be a positive integeral "
"value, but got num_samples={}".format(self.num_samples))
def __iter__(self):
n = len(self.data_source)
# Determine number of epochs
num_epochs = int(np.ceil((n * self.num_epochs
- (self.start_itr * self.batch_size)) / float(n)))
# Sample all the indices, and then grab the last num_epochs index sets;
# This ensures if we're starting at epoch 4, we're still grabbing epoch 4's
# indices
out = [torch.randperm(n) for epoch in range(self.num_epochs)][-num_epochs:]
# Ignore the first start_itr % n indices of the first epoch
out[0] = out[0][(self.start_itr * self.batch_size % n):]
# if self.replacement:
# return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64).tolist())
# return iter(.tolist())
output = torch.cat(out).tolist()
print('Length dataset output is %d' % len(output))
return iter(output)
def __len__(self):
return len(self.data_source) * self.num_epochs - self.start_itr * self.batch_size
# Convenience function to centralize all data loaders
def get_data_loaders(dataset, data_root=None, augment=False, batch_size=64,
num_workers=8, shuffle=True, load_in_mem=False, hdf5=False,
pin_memory=True, drop_last=True, start_itr=0,
num_epochs=500, use_multiepoch_sampler=False,
**kwargs):
# Append /FILENAME.hdf5 to root if using hdf5
data_root += '/%s' % root_dict[dataset]
print('Using dataset root location %s' % data_root)
which_dataset = dset_dict[dataset]
norm_mean = [0.5, 0.5, 0.5]
norm_std = [0.5, 0.5, 0.5]
image_size = imsize_dict[dataset]
# For image folder datasets, name of the file where we store the precomputed
# image locations to avoid having to walk the dirs every time we load.
dataset_kwargs = {'index_filename': '%s_imgs.npz' % dataset}
# HDF5 datasets have their own inbuilt transform, no need to train_transform
if 'hdf5' in dataset:
train_transform = None
else:
if augment:
print('Data will be augmented...')
if dataset in ['C10', 'C100']:
train_transform = [transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()]
else:
train_transform = [RandomCropLongEdge(),
transforms.Resize(image_size),
transforms.RandomHorizontalFlip()]
else:
print('Data will not be augmented...')
if dataset in ['C10', 'C100']:
train_transform = []
else:
train_transform = [CenterCropLongEdge(), transforms.Resize(image_size)]
# train_transform = [transforms.Resize(image_size), transforms.CenterCrop]
train_transform = transforms.Compose(train_transform + [
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std)])
train_set = which_dataset(root=data_root, transform=train_transform,
load_in_mem=load_in_mem, **dataset_kwargs)
# Prepare loader; the loaders list is for forward compatibility with
# using validation / test splits.
loaders = []
start_itr = 0
if use_multiepoch_sampler:
print('Using multiepoch sampler from start_itr %d...' % start_itr)
loader_kwargs = {'num_workers': num_workers, 'pin_memory': pin_memory}
sampler = MultiEpochSampler(train_set, num_epochs, start_itr, batch_size)
train_loader = DataLoader(train_set, batch_size=batch_size,
sampler=sampler, **loader_kwargs)
else:
loader_kwargs = {'num_workers': num_workers, 'pin_memory': pin_memory,
'drop_last': drop_last} # Default, drop last incomplete batch
train_loader = DataLoader(train_set, batch_size=batch_size,
shuffle=shuffle, **loader_kwargs)
loaders.append(train_loader)
return loaders
# Utility file to seed rngs
def seed_rng(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
# Utility to peg all roots to a base root
# If a base root folder is provided, peg all other root folders to it.
def update_config_roots(config):
if config['base_root']:
print('Pegging all root folders to base root %s' % config['base_root'])
for key in ['data', 'weights', 'logs', 'samples']:
config['%s_root' % key] = '%s/%s' % (config['base_root'], key)
return config
# Utility to prepare root folders if they don't exist; parent folder must exist
def prepare_root(config):
for key in ['weights_root', 'logs_root', 'samples_root']:
if not os.path.exists(config[key]):
print('Making directory %s for %s...' % (config[key], key))
# os.mkdir(config[key])
os.makedirs(config[key])
# Simple wrapper that applies EMA to a model. COuld be better done in 1.0 using
# the parameters() and buffers() module functions, but for now this works
# with state_dicts using .copy_
class ema(object):
def __init__(self, source, target, decay=0.9999, start_itr=0):
self.source = source
self.target = target
self.decay = decay
# Optional parameter indicating what iteration to start the decay at
self.start_itr = start_itr
# Initialize target's params to be source's
self.source_dict = self.source.state_dict()
self.target_dict = self.target.state_dict()
print('Initializing EMA parameters to be source parameters...')
with torch.no_grad():
for key in self.source_dict:
self.target_dict[key].data.copy_(self.source_dict[key].data)
# target_dict[key].data = source_dict[key].data # Doesn't work!
def update(self, itr=None):
# If an iteration counter is provided and itr is less than the start itr,
# peg the ema weights to the underlying weights.
if itr and itr < self.start_itr:
decay = 0.0
else:
decay = self.decay
with torch.no_grad():
for key in self.source_dict:
self.target_dict[key].data.copy_(self.target_dict[key].data * decay
+ self.source_dict[key].data * (1 - decay))
# Apply modified ortho reg to a model
# This function is an optimized version that directly computes the gradient,
# instead of computing and then differentiating the loss.
def ortho(model, strength=1e-4, blacklist=[]):
with torch.no_grad():
for param in model.parameters():
# Only apply this to parameters with at least 2 axes, and not in the blacklist
if len(param.shape) < 2 or any([param is item for item in blacklist]):
continue
w = param.view(param.shape[0], -1)
grad = (2 * torch.mm(torch.mm(w, w.t())
* (1. - torch.eye(w.shape[0], device=w.device)), w))
param.grad.data += strength * grad.view(param.shape)
# Default ortho reg
# This function is an optimized version that directly computes the gradient,
# instead of computing and then differentiating the loss.
def default_ortho(model, strength=1e-4, blacklist=[]):
with torch.no_grad():
for param in model.parameters():
# Only apply this to parameters with at least 2 axes & not in blacklist
if len(param.shape) < 2 or param in blacklist:
continue
w = param.view(param.shape[0], -1)
grad = (2 * torch.mm(torch.mm(w, w.t())
- torch.eye(w.shape[0], device=w.device), w))
param.grad.data += strength * grad.view(param.shape)
# Convenience utility to switch on/off requires_grad
# Pass a list of names (full or partial) of parameters to include or exclude to toggle tho model partially
def toggle_grad(model, on_or_off, include=None, exclude=None): # Mohamad
named_params = model.named_parameters()
if include != None:
l = []
for name, p in named_params:
if any([(str in name) for str in include]):
l.append((name, p))
named_params = l
if exclude != None:
l = []
for name, p in named_params:
if all([(str not in name) for str in exclude]):
l.append((name, p))
named_params = l
for _, param in named_params:
param.requires_grad = on_or_off
# Function to join strings or ignore them
# Base string is the string to link "strings," while strings
# is a list of strings or Nones.
def join_strings(base_string, strings):
return base_string.join([item for item in strings if item])
# Save a model's weights, optimizer, and the state_dict
def save_weights(G, D, state_dict, weights_root, experiment_name,
name_suffix=None, G_ema=None):
root = '/'.join([weights_root, experiment_name])
if not os.path.exists(root):
os.mkdir(root)
if name_suffix:
print('Saving weights to %s/%s...' % (root, name_suffix))
else:
print('Saving weights to %s...' % root)
torch.save(G.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G', name_suffix])))
torch.save(G.optim.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G_optim', name_suffix])))
torch.save(D.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['D', name_suffix])))
torch.save(D.optim.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['D_optim', name_suffix])))
torch.save(state_dict,
'%s/%s.pth' % (root, join_strings('_', ['state_dict', name_suffix])))
if G_ema is not None:
torch.save(G_ema.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G_ema', name_suffix])))
# Load a model's weights, optimizer, and the state_dict
def load_weights(G, D, state_dict, which_weights, weights_root, experiment_name,
name_suffix=None, G_ema=None, strict=True, load_optim=True):
if which_weights == 'pretrained':
root = weights_root
elif which_weights == 'target':
root = '/'.join([weights_root, experiment_name])
else:
raise ValueError
if name_suffix:
print('Loading %s weights from %s...' % (name_suffix, root))
else:
print('Loading weights from %s...' % root)
if G is not None:
G.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G', name_suffix]))),
strict=strict)
if load_optim:
G.optim.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G_optim', name_suffix]))))
if D is not None:
D.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['D', name_suffix]))),
strict=strict)
if load_optim:
D.optim.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['D_optim', name_suffix]))))
# Load state dict
for item in state_dict:
state_dict[item] = torch.load('%s/%s.pth' % (root, join_strings('_', ['state_dict', name_suffix])))[item]
if G_ema is not None:
G_ema.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G_ema', name_suffix]))),
strict=strict)
''' MetricsLogger originally stolen from VoxNet source code.
Used for logging inception metrics'''
class MetricsLogger(object):
def __init__(self, fname, reinitialize=False):
self.fname = fname
self.reinitialize = reinitialize
if os.path.exists(self.fname):
if self.reinitialize:
print('{} exists, deleting...'.format(self.fname))
os.remove(self.fname)
def log(self, record=None, **kwargs):
"""
Assumption: no newlines in the input.
"""
if record is None:
record = {}
record.update(kwargs)
record['_stamp'] = time.time()
with open(self.fname, 'a') as f:
f.write(json.dumps(record, ensure_ascii=True) + '\n')
# Logstyle is either:
# '%#.#f' for floating point representation in text
# '%#.#e' for exponent representation in text
# 'npz' for output to npz # NOT YET SUPPORTED
# 'pickle' for output to a python pickle # NOT YET SUPPORTED
# 'mat' for output to a MATLAB .mat file # NOT YET SUPPORTED
class MyLogger(object):
def __init__(self, fname, reinitialize=False, logstyle='%3.3f'):
self.root = fname
if not os.path.exists(self.root):
os.mkdir(self.root)
self.reinitialize = reinitialize
self.metrics = []
self.logstyle = logstyle # One of '%3.3f' or like '%3.3e'
# Delete log if re-starting and log already exists
def reinit(self, item):
if os.path.exists('%s/%s.log' % (self.root, item)):
if self.reinitialize:
# Only print the removal mess
if 'sv' in item:
if not any('sv' in item for item in self.metrics):
print('Deleting singular value logs...')
else:
print('{} exists, deleting...'.format('%s_%s.log' % (self.root, item)))
os.remove('%s/%s.log' % (self.root, item))
# Log in plaintext; this is designed for being read in MATLAB(sorry not sorry)
def log(self, itr, **kwargs):
for arg in kwargs:
if arg not in self.metrics:
if self.reinitialize:
self.reinit(arg)
self.metrics += [arg]
if self.logstyle == 'pickle':
print('Pickle not currently supported...')
# with open('%s/%s.log' % (self.root, arg), 'a') as f:
# pickle.dump(kwargs[arg], f)
elif self.logstyle == 'mat':
print('.mat logstyle not currently supported...')
else:
with open('%s/%s.log' % (self.root, arg), 'a') as f:
f.write('%d: %s\n' % (itr, self.logstyle % kwargs[arg]))
# Write some metadata to the logs directory
def write_metadata(logs_root, experiment_name, config, state_dict):
with open(('%s/%s/metalog.txt' %
(logs_root, experiment_name)), 'w') as writefile:
writefile.write('datetime: %s\n' % str(datetime.datetime.now()))
writefile.write('config: %s\n' % str(config))
writefile.write('state: %s\n' % str(state_dict))
"""
Very basic progress indicator to wrap an iterable in.
Author: Jan Schlüter
Andy's adds: time elapsed in addition to ETA, makes it possible to add
estimated time to 1k iters instead of estimated time to completion.
"""
def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'):
"""
Returns a generator over `items`, printing the number and percentage of
items processed and the estimated remaining processing time before yielding
the next item. `total` gives the total number of items (required if `items`
has no length), and `min_delay` gives the minimum time in seconds between
subsequent prints. `desc` gives an optional prefix text (end with a space).
"""
total = total or len(items)
t_start = time.time()
t_last = 0
for n, item in enumerate(items):
t_now = time.time()
if t_now - t_last > min_delay:
print("\r%s%d/%d (%6.2f%%)" % (
desc, n + 1, total, n / float(total) * 100), end=" ")
if n > 0:
if displaytype == 's1k': # minutes/seconds for 1000 iters
next_1000 = n + (1000 - n % 1000)
t_done = t_now - t_start
t_1k = t_done / n * next_1000
outlist = list(divmod(t_done, 60)) + list(divmod(t_1k - t_done, 60))
print("(TE/ET1k: %d:%02d / %d:%02d)" % tuple(outlist), end=" ")
else: # displaytype == 'eta':
t_done = t_now - t_start
t_total = t_done / n * total
outlist = list(divmod(t_done, 60)) + list(divmod(t_total - t_done, 60))
print("(TE/ETA: %d:%02d / %d:%02d)" % tuple(outlist), end=" ")
sys.stdout.flush()
t_last = t_now
yield item
t_total = time.time() - t_start
print("\r%s%d/%d (100.00%%) (took %d:%02d)" % ((desc, total, total) +
divmod(t_total, 60)))
# Sample function for use with inception metrics
def sample(G, z_, y_, config):
with torch.no_grad():
z_.sample_()
y_.sample_()
if config['parallel']:
G_z = nn.parallel.data_parallel(G, (z_, G.new_shared(y_)))
else:
G_z = G(z_, G.new_shared(y_))
return G_z, y_
# Sample function for sample sheets
def sample_sheet(G, classes_per_sheet, num_classes, samples_per_class, parallel,
samples_root, experiment_name, folder_number, z_=None):
# Prepare sample directory
if not os.path.isdir('%s/%s' % (samples_root, experiment_name)):
os.mkdir('%s/%s' % (samples_root, experiment_name))
if not os.path.isdir('%s/%s/%d' % (samples_root, experiment_name, folder_number)):
os.mkdir('%s/%s/%d' % (samples_root, experiment_name, folder_number))
# loop over total number of sheets
for i in range(num_classes // classes_per_sheet):
ims = []
y = torch.arange(i * classes_per_sheet, (i + 1) * classes_per_sheet, device='cuda')
for j in range(samples_per_class):
if (z_ is not None) and hasattr(z_, 'sample_') and classes_per_sheet <= z_.size(0):
z_.sample_()
else:
z_ = torch.randn(classes_per_sheet, G.dim_z, device='cuda')
with torch.no_grad():
if parallel:
o = nn.parallel.data_parallel(G, (z_[:classes_per_sheet], G.new_shared(y)))
else:
o = G(z_[:classes_per_sheet], G.new_shared(y))
ims += [o.data.cpu()]
# This line should properly unroll the images
out_ims = torch.stack(ims, 1).view(-1, ims[0].shape[1], ims[0].shape[2],
ims[0].shape[3]).data.float().cpu()
# The path for the samples
image_filename = '%s/%s/%d/samples%d.jpg' % (samples_root, experiment_name,
folder_number, i)
torchvision.utils.save_image(out_ims, image_filename,
nrow=samples_per_class, normalize=True)
# Interp function; expects x0 and x1 to be of shape (shape0, 1, rest_of_shape..)
def interp(x0, x1, num_midpoints):
lerp = torch.linspace(0, 1.0, num_midpoints + 2, device='cuda').to(x0.dtype)
return ((x0 * (1 - lerp.view(1, -1, 1))) + (x1 * lerp.view(1, -1, 1)))
# interp sheet function
# Supports full, class-wise and intra-class interpolation
def interp_sheet(G, num_per_sheet, num_midpoints, num_classes, parallel,
samples_root, experiment_name, folder_number, sheet_number=0,
fix_z=False, fix_y=False, device='cuda'):
# Prepare zs and ys
if fix_z: # If fix Z, only sample 1 z per row
zs = torch.randn(num_per_sheet, 1, G.dim_z, device=device)
zs = zs.repeat(1, num_midpoints + 2, 1).view(-1, G.dim_z)
else:
zs = interp(torch.randn(num_per_sheet, 1, G.dim_z, device=device),
torch.randn(num_per_sheet, 1, G.dim_z, device=device),
num_midpoints).view(-1, G.dim_z)
if fix_y: # If fix y, only sample 1 z per row
ys = sample_1hot(num_per_sheet, num_classes)
ys = G.new_shared(ys).view(num_per_sheet, 1, -1)
ys = ys.repeat(1, num_midpoints + 2, 1).view(num_per_sheet * (num_midpoints + 2), -1)
else:
ys = interp(G.new_shared(sample_1hot(num_per_sheet, num_classes)).view(num_per_sheet, 1, -1),
G.new_shared(sample_1hot(num_per_sheet, num_classes)).view(num_per_sheet, 1, -1),
num_midpoints).view(num_per_sheet * (num_midpoints + 2), -1)
# Run the net--note that we've already passed y through G.shared.
if G.fp16:
zs = zs.half()
with torch.no_grad():
if parallel:
out_ims = nn.parallel.data_parallel(G, (zs, ys)).data.cpu()