-
Notifications
You must be signed in to change notification settings - Fork 5
/
train_BETA.py
729 lines (594 loc) · 26.1 KB
/
train_BETA.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
import os
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
from sklearn.mixture import GaussianMixture
from torchnet.meter import AUCMeter
from utils.util import AverageMeter, accuracy, TrackMeter
from utils.util import set_seed
from utils.config import Config, ConfigDict, DictAction
from losses import build_loss
from builder import build_optimizer
from models.build import build_model
from utils.util import format_time, interleave, de_interleave
from builder import build_logger
from datasets import build_divm_loader
from losses.TransLoss import AdversarialLoss
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('config', type=str, help='config file path')
parser.add_argument('--work-dir', help='the dir to save logs and models')
parser.add_argument('--resume', type=str, help='path to latest checkpoint (default: None)')
parser.add_argument('--load', type=str, help='Load init weights for fine-tune (default: None)')
parser.add_argument('--cfgname', help='specify log_file; for debug use')
parser.add_argument('--seed', default=0, type=int, help='random seed')
parser.add_argument('--cfg-options', nargs='+', action=DictAction,
help='override the config; e.g., --cfg-options port=10001 k1=a,b k2="[a,b]"'
'Note that the quotation marks are necessary and that no white space is allowed.')
args = parser.parse_args()
return args
def get_cfg(args):
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# work_dir
if args.work_dir is not None:
cfg.work_dir = args.work_dir
elif cfg.get('work_dir', None) is None:
dirname = os.path.dirname(args.config).replace('configs', 'checkpoints', 1)
filename = os.path.splitext(os.path.basename(args.config))[0]
cfg.work_dir = os.path.join(dirname, filename)
os.makedirs(cfg.work_dir, exist_ok=True)
# cfgname
if args.cfgname is not None:
cfg.cfgname = args.cfgname
else:
cfg.cfgname = os.path.splitext(os.path.basename(args.config))[0]
assert cfg.cfgname is not None
# seed
if args.seed != 0:
cfg.seed = args.seed
elif not hasattr(cfg, 'seed'):
cfg.seed = 42
set_seed(cfg.seed)
# resume or load init weights
if args.resume:
cfg.resume = args.resume
if args.load:
cfg.load = args.load
assert not (cfg.resume and cfg.load)
return cfg
def adjust_lr(optimizer, step, tot_steps, gamma=10, power=0.75):
decay = (1 + gamma * step / tot_steps) ** (-power)
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['init_lr'] * decay
def set_optimizer(model, cfg):
base_params = [v for k, v in model.named_parameters() if 'fc' not in k]
head_params = [v for k, v in model.named_parameters() if 'fc' in k]
param_groups = [{'params': base_params, 'lr': cfg.lr * 0.1},
{'params': head_params, 'lr': cfg.lr}]
optimizer = build_optimizer(cfg.optimizer, param_groups)
for param_group in optimizer.param_groups:
param_group['init_lr'] = param_group['lr']
return optimizer
def set_model(cfg):
model = build_model(cfg.tgt_model)
model.fc = build_model(cfg.tgt_head)
model = torch.nn.DataParallel(model).cuda()
return model
def update_batch_stats(model, flag):
for m in model.modules():
if isinstance(m, nn.BatchNorm2d):
m.update_batch_stats = flag
def test(test_loader, model, criterion, epoch, logger, writer, model2=None):
""" test target """
model.eval()
if model2 is not None:
model2.eval()
losses = AverageMeter()
top1 = AverageMeter()
all_pred = []
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(test_loader):
images = images.float().cuda()
labels = labels.cuda()
bsz = labels.shape[0]
# forward
logits = model(images)
if model2 is not None:
logits2 = model2(images)
logits = (logits + logits2) / 2
loss = criterion(logits, labels)
pred = F.softmax(logits, dim=1)
all_pred.append(pred.detach())
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(logits, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
all_pred = torch.cat(all_pred)
mean_ent = (-all_pred * torch.log(all_pred + 1e-5)).sum(dim=1).mean().item() / np.log(all_pred.size(0))
# writer
writer.add_scalar(f'Loss/divm_test', losses.avg, epoch)
writer.add_scalar(f'Entropy/divm_test', mean_ent, epoch)
writer.add_scalar(f'Acc/divm_test', top1.avg, epoch)
# logger
time2 = time.time()
test_time = format_time(time2 - time1)
logger.info(f'Test at epoch [{epoch}] - test_time: {test_time}, '
f'test_loss: {losses.avg:.3f}, '
f'test_entropy: {mean_ent:.3f}, '
f'test_Acc@1: {top1.avg:.2f}')
return top1.avg, mean_ent
def test_class_acc(test_loader, model, criterion, it, logger, writer, cfg, model2=None):
""" test target """
model.eval()
if model2 is not None:
model2.eval()
losses = AverageMeter()
top1 = AverageMeter()
all_pred, all_labels = [], []
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(test_loader):
images = images.float().cuda()
labels = labels.cuda()
all_labels.append(labels)
bsz = labels.shape[0]
# forward
logits = model(images)
if model2 is not None:
logits2 = model2(images)
logits = (logits + logits2) / 2
loss = criterion(logits, labels)
pred = F.softmax(logits, dim=1)
all_pred.append(pred.detach())
# update metric
losses.update(loss.item(), bsz)
acc1, acc5 = accuracy(logits, labels, topk=(1, 5))
top1.update(acc1[0], bsz)
all_labels = torch.cat(all_labels)
all_pred = torch.cat(all_pred)
mean_ent = (-all_pred * torch.log(all_pred + 1e-5)).sum(dim=1).mean().item() / np.log(all_pred.size(0))
pred_max = all_pred.max(dim=1).indices
# class-wise acc
class_accs = []
all_eq = pred_max == all_labels
for c in range(cfg.num_classes):
mask_c = all_labels == c
acc_c = all_eq[mask_c].float().mean().item()
class_accs.append(round(acc_c * 100, 2))
avg_acc = round(sum(class_accs) / len(class_accs), 2)
# writer
writer.add_scalar(f'Loss/ft_tgt_test', losses.avg, it)
writer.add_scalar(f'Entropy/ft_tgt_test', mean_ent, it)
writer.add_scalar(f'Acc/ft_tgt_test', top1.avg, it)
# logger
time2 = time.time()
test_time = format_time(time2 - time1)
logger.info(f'Test at iter [{it}] - test_time: {test_time}, '
f'test_loss: {losses.avg:.3f}, '
f'test_entropy: {mean_ent:.3f}, '
f'test_Acc@1: {top1.avg:.2f}')
logger.info(f'per class acc: {str(class_accs)}, avg_acc: {avg_acc}')
return top1.avg, mean_ent, pred_max
def pred_target(test_loader, model, epoch, logger, cfg, model2=None):
""" get predictions for target samples """
model.eval()
if model2 is not None:
model2.eval()
all_psl = []
all_labels = []
all_pred = []
time1 = time.time()
with torch.no_grad():
for idx, (images, labels) in enumerate(test_loader):
images = images.cuda()
labels = labels.cuda()
bsz = images.shape[0]
# forward
logits = model(images)
if model2 is not None:
output2 = model2(images)
logits = (logits + output2) / 2
psl = logits.max(dim=1).indices
pred = F.softmax(logits, dim=1)
if epoch == 0:
src_idx = torch.sort(pred, dim=1, descending=True).indices
for i in range(bsz):
pred[i, src_idx[i, cfg.topk:]] = \
(1.0 - pred[i, src_idx[i, :cfg.topk]].sum()) / (cfg.num_classes - cfg.topk)
all_psl.append(psl)
all_labels.append(labels)
all_pred.append(pred.detach())
all_psl = torch.cat(all_psl)
all_labels = torch.cat(all_labels)
all_pred = torch.cat(all_pred)
psl_acc = (all_psl == all_labels).float().mean()
# logger
time2 = time.time()
pred_time = format_time(time2 - time1)
logger.info(f'Predict target at epoch [{epoch}]: psl_acc: {psl_acc:.2f}, time: {pred_time}')
return all_psl, all_labels, all_pred
def warmup(warmup_loader, model, optimizer, epoch, logger, cfg):
batch_time = AverageMeter()
losses = AverageMeter()
criterion = nn.CrossEntropyLoss().cuda()
num_iters = len(warmup_loader)
model.train()
t1 = end = time.time()
for batch_idx, (inputs, labels) in enumerate(warmup_loader):
inputs = inputs.cuda()
labels = labels.cuda()
outputs = model(inputs)
loss = criterion(outputs, labels)
losses.update(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# logger
if batch_idx % cfg.log_interval == 0:
lr = optimizer.param_groups[0]['lr']
logger.info(f'Epoch [{epoch}][{batch_idx}/{num_iters}] - '
f'Batch time: {batch_time.avg:.2f}, '
f'lr: {lr:.6f}, '
f'loss: {losses.avg:.3f}')
t2 = time.time()
epoch_time = format_time(t2 - t1)
logger.info(f'Epoch [{epoch}] - train_time: {epoch_time}, '
f'train_loss: {losses.avg:.3f}\n')
def dist_train(warmup_loader, model, optimizer, epoch, logger, cfg, pred_mem):
batch_time = AverageMeter()
losses = AverageMeter()
losses_kl = AverageMeter()
losses_ent = AverageMeter()
num_iters = len(warmup_loader)
model.train()
t1 = end = time.time()
for batch_idx, (images, _, indices) in enumerate(warmup_loader):
images = images.cuda()
targets = pred_mem[indices, :]
bsz = images.shape[0]
# forward
logits = model(images)
pred_tgt = F.softmax(logits, dim=1)
loss_kl = nn.KLDivLoss(reduction='batchmean')(pred_tgt.log(), targets)
loss_entropy = (-pred_tgt * torch.log(pred_tgt + 1e-5)).sum(dim=1).mean()
pred_mean = pred_tgt.mean(dim=0)
loss_gentropy = torch.sum(-pred_mean * torch.log(pred_mean + 1e-5))
loss_entropy -= loss_gentropy
loss = loss_kl + loss_entropy
# update metric
losses.update(loss.item(), bsz)
losses_kl.update(loss_kl.item(), bsz)
losses_ent.update(loss_entropy.item(), bsz)
# backward1
optimizer.zero_grad()
loss.backward()
# backward2
if cfg.lam_mix > 0:
alpha = 0.3
lam = np.random.beta(alpha, alpha)
index = torch.randperm(bsz).cuda()
mixed_images = lam * images + (1 - lam) * images[index, :]
mixed_targets = (lam * pred_tgt + (1 - lam) * pred_tgt[index, :]).detach()
update_batch_stats(model, False)
mixed_logits = model(mixed_images)
update_batch_stats(model, True)
mixed_pred_tgt = F.softmax(mixed_logits, dim=1)
loss_mix_kl = cfg.lam_mix * nn.KLDivLoss(reduction='batchmean')(mixed_pred_tgt.log(), mixed_targets)
loss_mix_kl.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# logger
if batch_idx % cfg.log_interval == 0:
lr = optimizer.param_groups[0]['lr']
logger.info(
f'Epoch [{epoch}][{batch_idx}/{num_iters}] - '
f'Batch time: {batch_time.avg:.2f}, '
f'lr: {lr:.6f}, '
f'loss_kl: {losses_kl.avg:.3f}, '
f'loss_ent: {losses_ent.avg:.3f}, '
f'distill_loss: {losses.avg:.3f}'
)
t2 = time.time()
epoch_time = format_time(t2 - t1)
logger.info(
f'Epoch [{epoch}] - train_time: {epoch_time}, '
f'loss_kl: {losses_kl.avg:.3f}, '
f'loss_ent: {losses_ent.avg:.3f}, '
f'distill_loss: {losses.avg:.3f}'
)
def eval_train(eval_loader, model):
model.eval()
losses = []
criterion = nn.CrossEntropyLoss(reduction='none').cuda()
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(eval_loader): # shuffle=False
inputs = inputs.cuda()
targets = targets.cuda()
outputs = model(inputs)
loss = criterion(outputs, targets)
losses.append(loss)
losses = torch.cat(losses)
losses = (losses - losses.min()) / (losses.max() - losses.min())
losses = losses.cpu()
# fit a two-component GMM to the loss
input_loss = losses.reshape(-1, 1)
gmm = GaussianMixture(n_components=2, max_iter=10, tol=1e-2, reg_covar=5e-4)
gmm.fit(input_loss)
prob = gmm.predict_proba(input_loss)
prob = prob[:, gmm.means_.argmin()]
return prob, losses
def train(label_loader, unlabel_loader, model, model2, criterion, optimizer, epoch, logger, cfg):
batch_time = AverageMeter()
losses = AverageMeter()
losses_trans = AverageMeter()
losses_x = AverageMeter()
losses_u = AverageMeter()
t1 = end = time.time()
labeled_train_iter = iter(label_loader)
unlabeled_train_iter = iter(unlabel_loader)
adv = AdversarialLoss()
model.train()
model2.eval()
num_iters = len(label_loader)
for batch_idx in range(num_iters):
try:
(inputs_x1, inputs_x2), targets_x, w_x = next(labeled_train_iter)
except:
assert False
try:
(inputs_u1, inputs_u2) = next(unlabeled_train_iter)
except:
unlabeled_train_iter = iter(unlabel_loader)
(inputs_u1, inputs_u2) = next(unlabeled_train_iter)
batch_size = inputs_x1.size(0)
# to cuda
inputs_x1, inputs_x2 = inputs_x1.cuda(), inputs_x2.cuda()
inputs_u1, inputs_u2 = inputs_u1.cuda(), inputs_u2.cuda()
targets_x = torch.zeros(batch_size, cfg.num_classes).scatter_(1, targets_x.view(-1, 1), 1).cuda()
w_x = w_x.view(-1, 1).cuda()
# co-refinement and co-guessing
with torch.no_grad():
# label refinement of labeled samples
outputs_x1 = model(inputs_x1)
outputs_x2 = model(inputs_x2)
px = (torch.softmax(outputs_x1, dim=1) + torch.softmax(outputs_x2, dim=1)) / 2
px = w_x * targets_x + (1 - w_x) * px
ptx = px ** (1 / cfg.T_sharpen) # temparature sharpening
targets_x = ptx / ptx.sum(dim=1, keepdim=True) # normalize
targets_x = targets_x.detach()
# label co-guessing of unlabeled samples
outputs_u11 = model(inputs_u1)
outputs_u12 = model(inputs_u2)
outputs_u21 = model2(inputs_u1)
outputs_u22 = model2(inputs_u2)
pu = (torch.softmax(outputs_u11, dim=1) + torch.softmax(outputs_u12, dim=1) +
torch.softmax(outputs_u21, dim=1) + torch.softmax(outputs_u22, dim=1)) / 4
ptu = pu ** (1 / cfg.T_sharpen)
targets_u = ptu / ptu.sum(dim=1, keepdim=True) # normalize
targets_u = targets_u.detach()
# mixmatch forward
lam = np.random.beta(cfg.alpha, cfg.alpha)
lam = max(lam, 1 - lam)
all_inputs = torch.cat([inputs_x1, inputs_x2, inputs_u1, inputs_u2], dim=0)
all_targets = torch.cat([targets_x, targets_x, targets_u, targets_u], dim=0)
idx = torch.randperm(all_inputs.size(0))
input_a, input_b = all_inputs, all_inputs[idx]
target_a, target_b = all_targets, all_targets[idx]
lam_u = cfg.lam_u
if cfg.lam_u > 0:
mixed_input = lam * input_a + (1 - lam) * input_b
mixed_target = lam * target_a + (1 - lam) * target_b
mixed_inputs = interleave(mixed_input, batch_size)
logits = model(mixed_inputs)
logits = de_interleave(logits, batch_size)
# loss
Lx, Lu = criterion(
logits[:batch_size * 2], mixed_target[:batch_size * 2],
logits[batch_size * 2:], mixed_target[batch_size * 2:]
)
cur_epoch = epoch - 1 + batch_idx / num_iters
lam_u = cfg.lam_u * np.clip((cur_epoch - cfg.warmup_epochs) / cfg.rampup_epochs, 0., 1.)
loss = Lx + lam_u * Lu
losses_u.update(Lu.item())
else:
mixed_input = lam * input_a[:batch_size * 2] + (1 - lam) * input_b[:batch_size * 2]
mixed_target = lam * target_a[:batch_size * 2] + (1 - lam) * target_b[:batch_size * 2]
mixed_inputs = interleave(mixed_input, batch_size)
logits = model(mixed_inputs)
logits = de_interleave(logits, batch_size)
Lx = criterion(logits, mixed_target) # SmoothCE
loss = Lx
losses_x.update(Lx.item())
# penalty
if cfg.lam_p > 0:
prior = torch.ones(cfg.num_classes).cuda() / cfg.num_classes
pred_mean = torch.softmax(logits, dim=1).mean(0)
penalty = torch.sum(prior * torch.log(prior / pred_mean))
loss += cfg.lam_p * penalty
# transfer
if cfg.lam_t > 0:
_, feat_a = model(input_a, req_feat=True)
_, feat_b = model(input_b, req_feat=True)
transfer_loss = adv(feat_a, feat_b)
loss += cfg.lam_t * transfer_loss
losses_trans.update(transfer_loss.item())
# update losses
losses.update(loss.item())
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# logger
if batch_idx % cfg.log_interval == 0:
lr = optimizer.param_groups[0]['lr']
logger.info(f'Epoch [{epoch}][{batch_idx}/{num_iters}] - '
f'Batch time: {batch_time.avg:.2f}, '
f'lr: {lr:.6f}, '
f'loss: {losses.avg:.3f}, '
f'loss_trans: {losses_trans.avg:.3f}, '
f'loss_x: {losses_x.avg:.3f}, '
f'loss_u: {losses_u.avg:.3f}(lam_u={lam_u:.2f})')
t2 = time.time()
epoch_time = format_time(t2 - t1)
logger.info(
f'Epoch [{epoch}] - train_time: {epoch_time}, '
f'loss_trans: {losses_trans.avg:.3f}, '
f'loss_x: {losses_x.avg:.3f}, '
f'loss: {losses.avg:.3f}'
)
return losses.avg, losses_x.avg, losses_u.avg
def main():
# args & cfg
args = parse_args()
cfg = get_cfg(args)
cudnn.benchmark = True
# write cfg
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = os.path.join(cfg.work_dir, f'{timestamp}.cfg')
with open(log_file, 'a') as f:
f.write(cfg.pretty_text)
# logger
logger = build_logger(cfg.work_dir, cfgname=f'train')
writer = SummaryWriter(log_dir=os.path.join(cfg.work_dir, f'tensorboard'))
'''
# -----------------------------------------
# build model & optimizer
# -----------------------------------------
'''
# build source model & load weights
src_model = build_model(cfg.src_model)
src_model = torch.nn.DataParallel(src_model).cuda()
print(f'==> Loading checkpoint "{cfg.load}"')
ckpt = torch.load(cfg.load, map_location='cuda')
src_model.load_state_dict(ckpt['model_state'])
# build target model
model1 = set_model(cfg)
model2 = set_model(cfg)
optimizer1 = set_optimizer(model1, cfg)
optimizer2 = set_optimizer(model2, cfg)
train_criterion = build_loss(cfg.loss.train).cuda()
test_criterion = build_loss(cfg.loss.test).cuda()
print('==> Model built.')
'''
# -----------------------------------------
# build dataset/dataloader
# -----------------------------------------
'''
test_loader = build_divm_loader(cfg, mode='test')
'''
# -----------------------------------------
# Test source model before distill
# -----------------------------------------
'''
if cfg.get('test_class_acc', False):
test_class_acc(test_loader, src_model, test_criterion, 0, logger, writer, cfg)
else:
test(test_loader, src_model, test_criterion, 0, logger, writer)
'''
# -----------------------------------------
# Predict target
# -----------------------------------------
'''
tgt_psl, gt_labels, pred_mem = pred_target(test_loader, src_model, 0, logger, cfg)
warmup_loader = build_divm_loader(cfg, mode='warmup', psl=tgt_psl)
warmup_loader_idx = build_divm_loader(cfg, mode='warmup', return_idx=True)
eval_train_loader = build_divm_loader(cfg, mode='eval_train', psl=tgt_psl)
'''
# -----------------------------------------
# Start target training
# -----------------------------------------
'''
print("==> Start training...")
model1.train()
model2.train()
test_meter = TrackMeter()
start_epoch = 1
for epoch in range(start_epoch, cfg.epochs + 1):
adjust_lr(optimizer1, epoch, cfg.epochs, power=1.5)
adjust_lr(optimizer2, epoch, cfg.epochs, power=1.5)
# momentum update pred_mem
if epoch % cfg.pred_interval == 0:
_, _, pred_t = pred_target(test_loader, model1, epoch, logger, cfg, model2)
pred_mem = cfg.ema * pred_mem + (1 - cfg.ema) * pred_t
model1.train()
model2.train()
if epoch <= cfg.warmup_epochs:
warmup(warmup_loader, model1, optimizer1, epoch, logger, cfg)
warmup(warmup_loader, model2, optimizer2, epoch, logger, cfg)
else:
# distill loss
logger.info(f'Start distill training at epoch [{epoch}]...')
dist_train(warmup_loader_idx, model1, optimizer1, epoch, logger, cfg, pred_mem)
dist_train(warmup_loader_idx, model2, optimizer2, epoch, logger, cfg, pred_mem)
# calc GMM probs
logger.info(f'==> Start evaluation at epoch [{epoch}]...')
t1 = time.time()
prob1, losses1 = eval_train(eval_train_loader, model1)
prob2, losses2 = eval_train(eval_train_loader, model2)
mask1 = prob1 >= cfg.tau_p
mask2 = prob2 >= cfg.tau_p
t2 = time.time()
eval_time = format_time(t2 - t1)
logger.info(f'==> Evaluation finished ({eval_time}).')
# DivideMix
label_indices = mask1.nonzero()[0]
unlabel_indices = (~mask1).nonzero()[0]
masked_probs = prob2[mask1]
masked_psl = tgt_psl[mask1]
label_loader = build_divm_loader(cfg, mode='label', indices=label_indices, probs=masked_probs, psl=masked_psl)
unlabel_loader = build_divm_loader(cfg, mode='unlabel', indices=unlabel_indices)
if len(label_loader) > 0 and len(unlabel_loader) > 0:
train(label_loader, unlabel_loader, model1, model2, train_criterion, optimizer1,
epoch, logger, cfg)
else:
logger.info(f'Skip DivM for model_1 at epoch [{epoch}] - num_label: {len(label_indices)}, '
f'num_unlabel: {len(unlabel_indices)}.')
label_indices = mask2.nonzero()[0]
unlabel_indices = (~mask2).nonzero()[0]
masked_probs = prob1[mask2]
masked_psl = tgt_psl[mask2]
label_loader = build_divm_loader(cfg, mode='label', indices=label_indices, probs=masked_probs, psl=masked_psl)
unlabel_loader = build_divm_loader(cfg, mode='unlabel', indices=unlabel_indices)
if len(label_loader) > 0 and len(unlabel_loader) > 0:
train(label_loader, unlabel_loader, model2, model1, train_criterion, optimizer2,
epoch, logger, cfg)
else:
logger.info(f'Skip DivM for model_2 at epoch [{epoch}] - num_label: {len(label_indices)}, '
f'num_unlabel: {len(unlabel_indices)}.')
if epoch % cfg.test_interval == 0 or epoch == cfg.epochs:
if cfg.get('test_class_acc', False):
test_acc, mean_ent, pred_max = \
test_class_acc(test_loader, model1, test_criterion, epoch, logger, writer, cfg, model2)
else:
test_acc, mean_ent = test(test_loader, model1, test_criterion, epoch, logger, writer, model2)
test_meter.update(test_acc, idx=epoch)
# We print the best test_acc but use the last checkpoint for fine-tuning.
logger.info(f'Best test_Acc@1: {test_meter.max_val:.2f} (epoch={test_meter.max_idx}).')
# save last
model_path = os.path.join(cfg.work_dir, 'last.pth')
state_dict = {
'model1_state': model1.state_dict(),
'model2_state': model2.state_dict(),
'optimizer1_state': optimizer1.state_dict(),
'optimizer2_state': optimizer2.state_dict(),
'epochs': cfg.epochs
}
torch.save(state_dict, model_path)
if __name__ == '__main__':
main()