-
Notifications
You must be signed in to change notification settings - Fork 2
/
selftrainingMCN_5model_meannet.py
748 lines (657 loc) · 34.2 KB
/
selftrainingMCN_5model_meannet.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
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import print_function, absolute_import
import argparse
import time
import os.path as osp
import os
import sys
import numpy as np
import torch
import csv
import codecs
from torch import nn
from torch.nn import init
from torch.backends import cudnn
from torch.utils.data import DataLoader
from reid import datasets
from reid import models
from reid.loss import TripletLoss
from reid.trainers import CoTrainerAsy, CoTrainerAsy4, CoTrainerAsy5
from reid.trainers_meannet import CoTrainerAsyMean_5model
from reid.evaluators import Evaluator, extract_features
from reid.utils.data import transforms as T
import torch.nn.functional as F
from reid.utils.data.preprocessor import Preprocessor
from reid.utils.data.sampler import RandomIdentitySampler
from reid.utils.serialization import load_checkpoint, save_checkpoint
from sklearn.cluster import DBSCAN
from reid.rerank import re_ranking
class Logger(object):
def __init__(self, fileN="Default.log"):
self.terminal = sys.stdout
self.log = open(fileN, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
pass
sys.stdout = Logger("./LOG/MCN_5_mean/D2M/print.txt")
def data_write_csv(file_name, datas):#file_name为写入CSV文件的路径,datas为要写入数据列表
file_csv = codecs.open(file_name,'w+','utf-8')#追加
writer = csv.writer(file_csv, delimiter=' ', quotechar=' ', quoting=csv.QUOTE_MINIMAL)
for data in datas:
writer.writerow(data)
print("保存文件成功,处理结束")
def calScores(clusters, labels):
"""
compute pair-wise precision pair-wise recall
"""
from scipy.special import comb
if len(clusters) == 0:
return 0, 0
else:
curCluster = []
for curClus in clusters.values():
curCluster.append(labels[curClus])
TPandFP = sum([comb(len(val), 2) for val in curCluster])
TP = 0
for clusterVal in curCluster:
for setMember in set(clusterVal):
if sum(clusterVal == setMember) < 2: continue
TP += comb(sum(clusterVal == setMember), 2)
FP = TPandFP - TP
# FN and TN
TPandFN = sum([comb(labels.tolist().count(val), 2) for val in set(labels)])
FN = TPandFN - TP
# cal precision and recall
precision, recall = TP / (TP + FP), TP / (TP + FN)
fScore = 2 * precision * recall / (precision + recall)
return precision, recall, fScore
def get_data(name, data_dir, height, width, batch_size,
workers):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, num_val=0.1)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# use all training and validation images in target dataset
train_set = dataset.trainval
num_classes = dataset.num_trainval_ids
transformer = T.Compose([
T.Resize((height, width)),
T.ToTensor(),
normalizer,
])
extfeat_loader = DataLoader(
Preprocessor(train_set, root=dataset.images_dir,
transform=transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
test_loader = DataLoader(
Preprocessor(list(set(dataset.query) | set(dataset.gallery)),
root=dataset.images_dir, transform=transformer),
batch_size=batch_size // 2, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, num_classes, extfeat_loader, test_loader
def saveAll(nameList, rootDir, tarDir):
import os
import shutil
if os.path.exists(tarDir):
shutil.rmtree(tarDir)
os.makedirs(tarDir)
for name in nameList:
shutil.copyfile(os.path.join(rootDir, name), os.path.join(tarDir, name))
def get_source_data(name, data_dir, height, width, batch_size, workers):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root, num_val=0.1)
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# use all training images on source dataset
train_set = dataset.train
num_classes = dataset.num_train_ids
transformer = T.Compose([
T.Resize((height, width)),
T.ToTensor(),
normalizer,
])
extfeat_loader = DataLoader(
Preprocessor(train_set, root=dataset.images_dir,
transform=transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return dataset, extfeat_loader
def calDis(qFeature, gFeature): # 246s
x, y = F.normalize(qFeature), F.normalize(gFeature)
# x, y = qFeature, gFeature
m, n = x.shape[0], y.shape[0]
disMat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t()
disMat.addmm_(1, -2, x, y.t())
return disMat.clamp_(min=1e-5)
def labelUnknown(knownFeat, allLab, unknownFeat):
disMat = calDis(knownFeat, unknownFeat)
labLoc = disMat.argmin(dim=0)
return allLab[labLoc]
def labelNoise(feature, labels):
# features and labels with -1
noiseFeat, pureFeat = feature[labels == -1, :], feature[labels != -1, :]
labels = labels[labels != -1]
unLab = labelUnknown(pureFeat, labels, noiseFeat)
return unLab.numpy()
def main(args):
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.benchmark = True
# Create data loaders
assert args.num_instances > 1, "num_instances should be greater than 1"
assert args.batch_size % args.num_instances == 0, \
'num_instances should divide batch_size'
if args.height is None or args.width is None:
args.height, args.width = (144, 56) if args.arch == 'inception' else \
(256, 128)
# get source data
src_dataset, src_extfeat_loader = \
get_source_data(args.src_dataset, args.data_dir, args.height,
args.width, args.batch_size, args.workers)
# get target data
tgt_dataset, num_classes, tgt_extfeat_loader, test_loader = \
get_data(args.tgt_dataset, args.data_dir, args.height,
args.width, args.batch_size, args.workers)
# Create model
# Hacking here to let the classifier be the number of source ids
if args.src_dataset == 'dukemtmc':
model = models.create(args.arch, num_classes=632, pretrained=False)
coModel = models.create(args.arch, num_classes=632, pretrained=False)
co2Model = models.create(args.arch, num_classes=632, pretrained=False)
co3Model = models.create(args.arch, num_classes=632, pretrained=False)
co4Model = models.create(args.arch, num_classes=632, pretrained=False)
model_ema = models.create(args.arch, num_classes=632, pretrained=False)
coModel_ema = models.create(args.arch, num_classes=632, pretrained=False)
co2Model_ema = models.create(args.arch, num_classes=632, pretrained=False)
co3Model_ema = models.create(args.arch, num_classes=632, pretrained=False)
co4Model_ema = models.create(args.arch, num_classes=632, pretrained=False)
elif args.src_dataset == 'market1501':
model = models.create(args.arch, num_classes=676, pretrained=False)
coModel = models.create(args.arch, num_classes=676, pretrained=False)
co2Model = models.create(args.arch, num_classes=676, pretrained=False)
co3Model = models.create(args.arch, num_classes=676, pretrained=False)
co4Model = models.create(args.arch, num_classes=676, pretrained=False)
model_ema = models.create(args.arch, num_classes=676, pretrained=False)
coModel_ema = models.create(args.arch, num_classes=676, pretrained=False)
co2Model_ema = models.create(args.arch, num_classes=676, pretrained=False)
co3Model_ema = models.create(args.arch, num_classes=676, pretrained=False)
co4Model_ema = models.create(args.arch, num_classes=676, pretrained=False)
elif args.src_dataset == 'msmt17':
model = models.create(args.arch, num_classes=1041, pretrained=False)
coModel = models.create(args.arch, num_classes=1041, pretrained=False)
co2Model = models.create(args.arch, num_classes=1041, pretrained=False)
co3Model = models.create(args.arch, num_classes=1041, pretrained=False)
co4Model = models.create(args.arch, num_classes=1041, pretrained=False)
model_ema = models.create(args.arch, num_classes=1041, pretrained=False)
coModel_ema = models.create(args.arch, num_classes=1041, pretrained=False)
co2Model_ema = models.create(args.arch, num_classes=1041, pretrained=False)
co3Model_ema = models.create(args.arch, num_classes=1041, pretrained=False)
co4Model_ema = models.create(args.arch, num_classes=1041, pretrained=False)
elif args.src_dataset == 'cuhk03':
model = models.create(args.arch, num_classes=1230, pretrained=False)
coModel = models.create(args.arch, num_classes=1230, pretrained=False)
co2Model = models.create(args.arch, num_classes=1230, pretrained=False)
co3Model = models.create(args.arch, num_classes=1230, pretrained=False)
co4Model = models.create(args.arch, num_classes=1230, pretrained=False)
model_ema = models.create(args.arch, num_classes=1230, pretrained=False)
coModel_ema = models.create(args.arch, num_classes=1230, pretrained=False)
co2Model_ema = models.create(args.arch, num_classes=1230, pretrained=False)
co3Model_ema = models.create(args.arch, num_classes=1230, pretrained=False)
co4Model_ema = models.create(args.arch, num_classes=1230, pretrained=False)
else:
raise RuntimeError('Please specify the number of classes (ids) of the network.')
# Load from checkpoint
if args.resume:
print('Resuming checkpoints from finetuned model on another dataset...\n')
checkpoint = load_checkpoint(args.resume)
model.load_state_dict(checkpoint['state_dict'], strict=False)
coModel.load_state_dict(checkpoint['state_dict'], strict=False)
co2Model.load_state_dict(checkpoint['state_dict'], strict=False)
co3Model.load_state_dict(checkpoint['state_dict'], strict=False)
co4Model.load_state_dict(checkpoint['state_dict'], strict=False)
model_ema.load_state_dict(checkpoint['state_dict'], strict=False)
coModel_ema.load_state_dict(checkpoint['state_dict'], strict=False)
co2Model_ema.load_state_dict(checkpoint['state_dict'], strict=False)
co3Model_ema.load_state_dict(checkpoint['state_dict'], strict=False)
co4Model_ema.load_state_dict(checkpoint['state_dict'], strict=False)
else:
raise RuntimeWarning('Not using a pre-trained model.')
model = nn.DataParallel(model).cuda()
coModel = nn.DataParallel(coModel).cuda()
co2Model = nn.DataParallel(co2Model).cuda()
co3Model = nn.DataParallel(co3Model).cuda()
co4Model = nn.DataParallel(co4Model).cuda()
model_ema = nn.DataParallel(model_ema).cuda()
coModel_ema = nn.DataParallel(coModel_ema).cuda()
co2Model_ema = nn.DataParallel(co2Model_ema).cuda()
co3Model_ema = nn.DataParallel(co3Model_ema).cuda()
co4Model_ema = nn.DataParallel(co4Model_ema).cuda()
for param in model_ema.parameters():
param.detach_() #截断反向传播的梯度流
for param in coModel_ema.parameters():
param.detach_()
for param in co2Model_ema.parameters():
param.detach_()
for param in co3Model_ema.parameters():
param.detach_()
for param in co4Model_ema.parameters():
param.detach_()
evaluator = Evaluator(model_ema, print_freq=args.print_freq)
evaluator.evaluate(test_loader, tgt_dataset.query, tgt_dataset.gallery)
# if args.evaluate: return
# Criterion
criterion = [
TripletLoss(args.margin, args.num_instances, isAvg=False, use_semi=False).cuda(),
TripletLoss(args.margin, args.num_instances, isAvg=False, use_semi=False).cuda(),
]
# Optimizer
optimizer = torch.optim.Adam(
model.parameters(), lr=args.lr
)
coOptimizer = torch.optim.Adam(
coModel.parameters(), lr=args.lr
)
co2Optimizer = torch.optim.Adam(
co2Model.parameters(), lr = args.lr
)
co3Optimizer = torch.optim.Adam(
co3Model.parameters(), lr = args.lr
)
co4Optimizer = torch.optim.Adam(
co4Model.parameters(), lr = args.lr
)
optims = [optimizer, coOptimizer, co2Optimizer, co3Optimizer,co4Optimizer]
# training stage transformer on input images
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transformer = T.Compose([
T.Resize((args.height, args.width)),
T.RandomHorizontalFlip(),
T.ToTensor(), normalizer,
T.RandomErasing(probability=0.5, sh=0.2, r1=0.3)
])
data_transformer = T.Compose([
T.Resize((args.height,args.width)),
T.ToTensor(),
normalizer,
])
# # Start training
for iter_n in range(args.iteration):
if args.lambda_value == 0:
source_features = 0
else:
# get source datas' feature
source_features, _ = extract_features(model_ema, src_extfeat_loader, print_freq=args.print_freq)
# synchronization feature order with src_dataset.train
source_features = torch.cat([source_features[f].unsqueeze(0) for f, _, _ in src_dataset.train], 0)
# extract training images' features
print('Iteration {}: Extracting Target Dataset Features...'.format(iter_n + 1))
target_features, _ = extract_features(model_ema, tgt_extfeat_loader, print_freq=args.print_freq)
# synchronization feature order with dataset.train
target_features = torch.cat([target_features[f].unsqueeze(0) for f, _, _ in tgt_dataset.trainval], 0)
# calculate distance and rerank result
print('Calculating feature distances...')
target_features = target_features.numpy()
rerank_dist = re_ranking(source_features, target_features, lambda_value=args.lambda_value)
if iter_n == 0:
# DBSCAN cluster
tri_mat = np.triu(rerank_dist, 1) # tri_mat.dim=2
tri_mat = tri_mat[np.nonzero(tri_mat)] # tri_mat.dim=1
tri_mat = np.sort(tri_mat, axis=None)
top_num = np.round(args.rho * tri_mat.size).astype(int)
eps = tri_mat[:top_num].mean()
print('eps in cluster: {:.3f}'.format(eps))
cluster = DBSCAN(eps=eps, min_samples=4, metric='precomputed', n_jobs=8)
# select & cluster images as training set of this epochs
print('Clustering and labeling...')
labels = cluster.fit_predict(rerank_dist)
num_ids = len(set(labels)) - 1
print('Iteration {} have {} training ids'.format(iter_n + 1, num_ids))
# generate new dataset
new_dataset, unknown_dataset = [], []
# assign label for target ones
unknownLab = labelNoise(torch.from_numpy(target_features), torch.from_numpy(labels))
# unknownFeats = target_features[labels==-1,:]
unCounter, index = 0, 0
from collections import defaultdict
realIDs, fakeIDs = defaultdict(list), []
for (fname, realPID, cam), label in zip(tgt_dataset.trainval, labels):
if label == -1:
unknown_dataset.append((fname, int(unknownLab[unCounter]), cam)) # unknown data
fakeIDs.append(int(unknownLab[unCounter]))
realIDs[realPID].append(index)
unCounter += 1
index += 1
continue
# dont need to change codes in trainer.py _parsing_input function and sampler function after add 0
new_dataset.append((fname, label, cam))
fakeIDs.append(label)
realIDs[realPID].append(index)
index += 1
print('Iteration {} have {} training images'.format(iter_n + 1, len(new_dataset)))
print('Iteration {} have {} outliers training images'.format(iter_n+1, len(unknown_dataset)))
precision, recall, fscore = calScores(realIDs, np.asarray(fakeIDs)) # fakeIDs does not contain -1
print('precision:{}, recall:{}, fscore: {}'.format(100 * precision, 100 * recall, fscore))
train_loader = DataLoader(
Preprocessor(new_dataset, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(new_dataset, args.num_instances),
pin_memory=True, drop_last=True
)
# hard samples
# noiseImgs = [name[1] for name in unknown_dataset]
# saveAll(noiseImgs, tgt_dataset.images_dir, 'noiseImg')
# import ipdb; ipdb.set_trace()
unLoader = DataLoader(
Preprocessor(unknown_dataset, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(unknown_dataset, args.num_instances),
pin_memory=True, drop_last=True
)
#*********************************二次聚类**************************************
print('***************second cluster*************')
print('tgt_dataset.trainval type:{}'.format(type(tgt_dataset.trainval)))
print('new_dataset type:{}'.format(type(new_dataset)))
#tgt_dataset.trainval type:<class 'list'>
#new_dataset type:<class 'list'>
#data_write_csv('tgt_dataset.trainval.csv',tgt_dataset.trainval)
#data_write_csv('new_dataset.csv',new_dataset)
train_all_loader = DataLoader(
Preprocessor(new_dataset, root=tgt_dataset.images_dir,
transform=data_transformer),
batch_size=args.batch_size, num_workers=4,
shuffle=False, pin_memory=True)
target_features2, _ = extract_features(model_ema, train_all_loader, print_freq=args.print_freq)
# synchronization feature order with dataset.train
target_features2 = torch.cat([target_features2[f].unsqueeze(0) for f, _, _ in new_dataset], 0)
# calculate distance and rerank result
print('Calculating feature distances...')
target_features2 = target_features2.numpy()
rerank_dist2 = re_ranking(source_features, target_features2, lambda_value=args.lambda_value)
if iter_n == 0:
# DBSCAN cluster
tri_mat2 = np.triu(rerank_dist2, 1) # tri_mat2.dim=2
tri_mat2 = tri_mat2[np.nonzero(tri_mat2)] # tri_mat2.dim=1
tri_mat2 = np.sort(tri_mat2, axis=None)
top_num2 = np.round(args.rho * tri_mat2.size).astype(int)
eps2 = tri_mat2[:top_num2].mean()
print('eps2 in cluster: {:.3f}'.format(eps2))
cluster2 = DBSCAN(eps=eps2, min_samples=4, metric='precomputed', n_jobs=8)
# select & cluster images as training set of this epochs
print('Clustering and labeling...')
labels2 = cluster2.fit_predict(rerank_dist2)
num_ids2 = len(set(labels2)) - 1
print('Iteration {} have {} training ids'.format(iter_n + 1, num_ids2))
# generate new dataset
new_dataset2, unknown_dataset2 = [], []
# assign label for target ones
unknownLab2 = labelNoise(torch.from_numpy(target_features2), torch.from_numpy(labels2))
# unknownFeats = target_features[labels==-1,:]
unCounter, index = 0, 0
from collections import defaultdict
realIDs2, fakeIDs2 = defaultdict(list), []
for (fname, realPID, cam), label in zip(new_dataset, labels2):
if label == -1:
unknown_dataset2.append((fname, int(unknownLab2[unCounter]), cam)) # unknown data
fakeIDs2.append(int(unknownLab2[unCounter]))
realIDs2[realPID].append(index)
unCounter += 1
index += 1
continue
# dont need to change codes in trainer.py _parsing_input function and sampler function after add 0
new_dataset2.append((fname, label, cam))
fakeIDs2.append(label)
realIDs2[realPID].append(index)
index += 1
print('Iteration {} have {} inliers2 training images'.format(iter_n + 1, len(new_dataset2)))
print('Iteration {} have {} outliers2 training images'.format(iter_n+1, len(unknown_dataset2)))
precision2, recall2, fscore2 = calScores(realIDs2, np.asarray(fakeIDs2)) # fakeIDs2 does not contain -1
print('precision2:{}, recall2:{}, fscore2: {}'.format(100 * precision2, 100 * recall2, fscore2))
# train_inliers_loader = DataLoader(
# Preprocessor(new_dataset2, root=tgt_dataset.images_dir, transform=train_transformer),
# batch_size=args.batch_size, num_workers=4,
# sampler=RandomIdentitySampler(new_dataset2, args.num_instances),
# pin_memory=True, drop_last=True
# )
# hard samples
# noiseImgs = [name[1] for name in unknown_dataset]
# saveAll(noiseImgs, tgt_dataset.images_dir, 'noiseImg')
# import ipdb; ipdb.set_trace()
train_out2_loader = DataLoader(
Preprocessor(unknown_dataset2, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(unknown_dataset2, args.num_instances),
pin_memory=True, drop_last=True
)
#*********************************三次聚类**************************************
print('***************second cluster*************')
print('tgt_dataset.trainval type:{}'.format(type(tgt_dataset.trainval)))
print('new_dataset2 type:{}'.format(type(new_dataset2)))
#tgt_dataset.trainval type:<class 'list'>
#new_dataset type:<class 'list'>
#data_write_csv('tgt_dataset.trainval.csv',tgt_dataset.trainval)
#data_write_csv('new_dataset.csv',new_dataset)
train_in2_loader = DataLoader(
Preprocessor(new_dataset2, root=tgt_dataset.images_dir,
transform=data_transformer),
batch_size=args.batch_size, num_workers=4,
shuffle=False, pin_memory=True)
target_features3, _ = extract_features(model_ema, train_in2_loader, print_freq=args.print_freq)
# synchronization feature order with dataset.train
target_features3 = torch.cat([target_features3[f].unsqueeze(0) for f, _, _ in new_dataset2], 0)
# calculate distance and rerank result
print('Calculating feature distances...')
target_features3 = target_features3.numpy()
rerank_dist3 = re_ranking(source_features, target_features3, lambda_value=args.lambda_value)
if iter_n == 0:
# DBSCAN cluster
tri_mat3 = np.triu(rerank_dist3, 1) # tri_mat2.dim=2
tri_mat3 = tri_mat3[np.nonzero(tri_mat3)] # tri_mat2.dim=1
tri_mat3 = np.sort(tri_mat3, axis=None)
top_num3 = np.round(args.rho * tri_mat3.size).astype(int)
eps3 = tri_mat3[:top_num3].mean()
print('eps3 in cluster: {:.3f}'.format(eps3))
cluster3 = DBSCAN(eps=eps3, min_samples=4, metric='precomputed', n_jobs=8)
# select & cluster images as training set of this epochs
print('Clustering and labeling...')
labels3 = cluster3.fit_predict(rerank_dist3)
num_ids3 = len(set(labels3)) - 1
print('Iteration {} have {} training ids'.format(iter_n + 1, num_ids3))
# generate new dataset
new_dataset3, unknown_dataset3 = [], []
# assign label for target ones
unknownLab3 = labelNoise(torch.from_numpy(target_features3), torch.from_numpy(labels3))
# unknownFeats = target_features[labels==-1,:]
unCounter, index = 0, 0
from collections import defaultdict
realIDs3, fakeIDs3 = defaultdict(list), []
for (fname, realPID, cam), label in zip(new_dataset, labels3):
if label == -1:
unknown_dataset3.append((fname, int(unknownLab3[unCounter]), cam)) # unknown data
fakeIDs3.append(int(unknownLab3[unCounter]))
realIDs3[realPID].append(index)
unCounter += 1
index += 1
continue
# dont need to change codes in trainer.py _parsing_input function and sampler function after add 0
new_dataset3.append((fname, label, cam))
fakeIDs3.append(label)
realIDs3[realPID].append(index)
index += 1
print('Iteration {} have {} inliers3 training images'.format(iter_n + 1, len(new_dataset3)))
print('Iteration {} have {} outliers3 training images'.format(iter_n+1, len(unknown_dataset3)))
precision3, recall3, fscore3 = calScores(realIDs3, np.asarray(fakeIDs3)) # fakeIDs3 does not contain -1
print('precision3:{}, recall3:{}, fscore3: {}'.format(100 * precision3, 100 * recall3, fscore3))
# train_in3_loader = DataLoader(
# Preprocessor(new_dataset3, root=tgt_dataset.images_dir, transform=train_transformer),
# batch_size=args.batch_size, num_workers=4,
# sampler=RandomIdentitySampler(new_dataset3, args.num_instances),
# pin_memory=True, drop_last=True
# )
# hard samples
# noiseImgs = [name[1] for name in unknown_dataset]
# saveAll(noiseImgs, tgt_dataset.images_dir, 'noiseImg')
# import ipdb; ipdb.set_trace()
train_out3_loader = DataLoader(
Preprocessor(unknown_dataset3, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(unknown_dataset3, args.num_instances),
pin_memory=True, drop_last=True
)
#*********************************四次聚类**************************************
print('***************second cluster*************')
print('tgt_dataset.trainval type:{}'.format(type(tgt_dataset.trainval)))
print('new_dataset3 type:{}'.format(type(new_dataset3)))
#tgt_dataset.trainval type:<class 'list'>
#new_dataset type:<class 'list'>
#data_write_csv('tgt_dataset.trainval.csv',tgt_dataset.trainval)
#data_write_csv('new_dataset.csv',new_dataset)
train_in3_loader = DataLoader(
Preprocessor(new_dataset3, root=tgt_dataset.images_dir,
transform=data_transformer),
batch_size=args.batch_size, num_workers=4,
shuffle=False, pin_memory=True)
target_features4, _ = extract_features(model_ema, train_in3_loader, print_freq=args.print_freq)
# synchronization feature order with dataset.train
target_features4 = torch.cat([target_features4[f].unsqueeze(0) for f, _, _ in new_dataset3], 0)
# calculate distance and rerank result
print('Calculating feature distances...')
target_features4 = target_features4.numpy()
rerank_dist4 = re_ranking(source_features, target_features4, lambda_value=args.lambda_value)
if iter_n == 0:
# DBSCAN4cluster
tri_mat4 = np.triu(rerank_dist4, 1) # tri_mat4.dim=2
tri_mat4 = tri_mat4[np.nonzero(tri_mat4)] # tri_mat4.dim=1
tri_mat4 = np.sort(tri_mat4, axis=None)
top_num4 = np.round(args.rho * tri_mat4.size).astype(int)
eps4 = tri_mat4[:top_num4].mean()
print('eps4 in cluster: {:.3f}'.format(eps4))
cluster4 = DBSCAN(eps=eps4, min_samples=4, metric='precomputed', n_jobs=8)
# select & cluster images as training set of this epochs
print('Clustering and labeling...')
labels4 = cluster4.fit_predict(rerank_dist4)
num_ids4 = len(set(labels4)) - 1
print('Iteration {} have {} training ids'.format(iter_n + 1, num_ids4))
# generate new dataset
new_dataset4, unknown_dataset4 = [], []
# assign label for target ones
unknownLab4 = labelNoise(torch.from_numpy(target_features4), torch.from_numpy(labels4))
# unknownFeats = target_features[labels==-1,:]
unCounter, index = 0, 0
from collections import defaultdict
realIDs4, fakeIDs4 = defaultdict(list), []
for (fname, realPID, cam), label in zip(new_dataset, labels4):
if label == -1:
unknown_dataset4.append((fname, int(unknownLab4[unCounter]), cam)) # unknown data
fakeIDs4.append(int(unknownLab4[unCounter]))
realIDs4[realPID].append(index)
unCounter += 1
index += 1
continue
# dont need to change codes in trainer.py _parsing_input function and sampler function after add 0
new_dataset4.append((fname, label, cam))
fakeIDs4.append(label)
realIDs4[realPID].append(index)
index += 1
print('Iteration {} have {} inliers4 training images'.format(iter_n + 1, len(new_dataset4)))
print('Iteration {} have {} outliers4 training images'.format(iter_n+1, len(unknown_dataset4)))
precision4, recall4, fscore4 = calScores(realIDs4, np.asarray(fakeIDs4)) # fakeIDs4 does not contain -1
print('precision4:{}, recall4:{}, fscore4: {}'.format(100 * precision4, 100 * recall4, fscore4))
train_in4_loader = DataLoader(
Preprocessor(new_dataset4, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(new_dataset4, args.num_instances),
pin_memory=True, drop_last=True
)
# hard samples
# noiseImgs = [name[1] for name in unknown_dataset]
# saveAll(noiseImgs, tgt_dataset.images_dir, 'noiseImg')
# import ipdb; ipdb.set_trace()
train_out4_loader = DataLoader(
Preprocessor(unknown_dataset4, root=tgt_dataset.images_dir, transform=train_transformer),
batch_size=args.batch_size, num_workers=4,
sampler=RandomIdentitySampler(unknown_dataset4, args.num_instances),
pin_memory=True, drop_last=True
)
# train model with new generated dataset
trainer = CoTrainerAsyMean_5model(
model, coModel, co2Model, co3Model, co4Model, model_ema, coModel_ema, co2Model_ema, co3Model_ema, co4Model_ema, train_in4_loader, train_out4_loader, train_out3_loader, train_out2_loader, unLoader, criterion, optims
)
# trainer = CoTrainerAsy(
# model, coModel, train_loader, unLoader, criterion, optims
# )
# Start training
for epoch in range(args.epochs):
trainer.train(epoch, remRate=0.2 + (0.8 / args.iteration) * (1 + iter_n))
# test only
rank_score = evaluator.evaluate(test_loader, tgt_dataset.query, tgt_dataset.gallery)
# print('co-model:\n')
# rank_score = evaluatorB.evaluate(test_loader, tgt_dataset.query, tgt_dataset.gallery)
# Evaluate
rank_score = evaluator.evaluate(test_loader, tgt_dataset.query, tgt_dataset.gallery)
save_checkpoint({
'state_dict': model.module.state_dict(),
'epoch': epoch + 1, 'best_top1': rank_score.market1501[0],
}, True, fpath=osp.join(args.logs_dir, 'asyCo.pth'))
return rank_score.map, rank_score.market1501[0]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Triplet loss classification")
# data
parser.add_argument('--src_dataset', type=str, default='dukemtmc',
choices=datasets.names())
parser.add_argument('--tgt_dataset', type=str, default='market1501',
choices=datasets.names())
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--workers', type=int, default=4)
parser.add_argument('--split', type=int, default=0)
parser.add_argument('--noiseLam', type=float, default=0.5)
parser.add_argument('--height', type=int,
help="input height, default: 256 for resnet*, "
"144 for inception")
parser.add_argument('--width', type=int,
help="input width, default: 128 for resnet*, "
"56 for inception")
parser.add_argument('--combine-trainval', action='store_true',
help="train and val sets together for training, "
"val set alone for validation")
parser.add_argument('--num_instances', type=int, default=4,
help="each minibatch consist of "
"(batch_size // num_instances) identities, and "
"each identity has num_instances instances, "
"default: 4")
# model
parser.add_argument('--arch', type=str, default='resnet50',
choices=models.names())
# loss
parser.add_argument('--margin', type=float, default=0.5,
help="margin of the triplet loss, default: 0.5")
parser.add_argument('--lambda_value', type=float, default=0.1,
help="balancing parameter, default: 0.1")
parser.add_argument('--rho', type=float, default=1.6e-3,
help="rho percentage, default: 1.6e-3")
# optimizer
parser.add_argument('--lr', type=float, default=6e-5,
help="learning rate of all parameters")
# training configs
parser.add_argument('--resume', type=str, metavar='PATH',
default='')
parser.add_argument('--evaluate', type=int, default=0,
help="evaluation only")
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--print_freq', type=int, default=1)
parser.add_argument('--iteration', type=int, default=10)
parser.add_argument('--epochs', type=int, default=30)
# metric learning
parser.add_argument('--dist_metric', type=str, default='euclidean',
choices=['euclidean', 'kissme'])
# misc
parser.add_argument('--data_dir', type=str, metavar='PATH',
default='')
parser.add_argument('--logs_dir', type=str, metavar='PATH',
default='')
args = parser.parse_args()
mean_ap, rank1 = main(args)