-
Notifications
You must be signed in to change notification settings - Fork 20
/
main.py
286 lines (233 loc) · 11.7 KB
/
main.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
from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
import torch.nn.functional as F
import random
import numpy as np
import sys
import collections
import copy
import time
from datetime import timedelta
from reid import datasets
from reid import models
from reid.models.memory import MemoryClassifier
from reid.trainers import Trainer
from reid.evaluators import Evaluator, extract_features
from reid.utils.data import IterLoader
from reid.utils.data import transforms as T
from reid.utils.data.sampler import RandomMultipleGallerySampler
from reid.utils.data.preprocessor import Preprocessor
from reid.utils.logging import Logger
from reid.utils.serialization import load_checkpoint, save_checkpoint, copy_state_dict
from reid.loss.triplet import TripletLoss
from reid.solver import WarmupMultiStepLR
start_epoch = best_mAP = 0
def get_data(name, data_dir):
root = osp.join(data_dir, name)
dataset = datasets.create(name, root)
return dataset
def get_train_loader(args, dataset, height, width, batch_size, workers,
num_instances, iters, trainset=None):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.RandomHorizontalFlip(p=0.5),
T.Pad(10),
T.RandomCrop((height, width)),
T.ToTensor(),
normalizer])
train_set = sorted(dataset.train) if trainset is None else sorted(trainset)
rmgs_flag = num_instances > 0
if rmgs_flag:
sampler = RandomMultipleGallerySampler(train_set, num_instances)
else:
sampler = None
train_loader = IterLoader(
DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer),
batch_size=batch_size, num_workers=workers, sampler=sampler,
shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=None)
return train_loader
def get_test_loader(dataset, height, width, batch_size, workers, testset=None):
normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transformer = T.Compose([
T.Resize((height, width), interpolation=3),
T.ToTensor(),
normalizer
])
if testset is None:
testset = list(set(dataset.query) | set(dataset.gallery))
test_loader = DataLoader(
Preprocessor(testset, root=dataset.images_dir, transform=test_transformer),
batch_size=batch_size, num_workers=workers,
shuffle=False, pin_memory=True)
return test_loader
def create_model(args, num_classes):
model = models.create(args.arch, num_features=args.features, dropout=args.dropout, norm=True,num_classes=num_classes, BNNeck=args.BNNeck)
# use CUDA
model.cuda()
if args.resume:
checkpoint = load_checkpoint(args.resume)
model.copyWeight_eval(checkpoint['state_dict'])
model = nn.DataParallel(model)
return model
def main():
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
main_worker(args)
def main_worker(args):
global start_epoch, best_mAP
start_time = time.monotonic()
cudnn.benchmark = True
sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt'))
print("==========\nArgs:{}\n==========".format(args))
# Create datasets
iters = args.iters if (args.iters > 0) else None
print("==> Load datasets")
dataset_src1 = get_data(args.dataset_src1,args.data_dir)
dataset_src2 = get_data(args.dataset_src2,args.data_dir)
dataset_src3 = get_data(args.dataset_src3,args.data_dir)
dataset = get_data(args.dataset, args.data_dir)
datasets_src = [dataset_src1, dataset_src2, dataset_src3]
# camMar, camDuke = get_data('marCam', args.data_dir), get_data('dukeCam', args.data_dir)
train_loader_src1 = get_train_loader(args, dataset_src1, args.height, args.width,
args.batch_size, args.workers, args.num_instances, iters)
train_loader_src2 = get_train_loader(args, dataset_src2, args.height, args.width,
args.batch_size, args.workers, args.num_instances, iters)
train_loader_src3 = get_train_loader(args, dataset_src3, args.height, args.width,
args.batch_size, args.workers, args.num_instances, iters)
test_loader = get_test_loader(dataset, args.height, args.width, args.test_batch_size, args.workers)
train_loader = [train_loader_src1, train_loader_src2, train_loader_src3]
num_classes1 = dataset_src1.num_train_pids
num_classes2 = dataset_src2.num_train_pids
num_classes3 = dataset_src3.num_train_pids
num_classes = [num_classes1, num_classes2, num_classes3]
print(' number classes = ', num_classes)
# Create model
model = create_model(args, num_classes=[0,0,0])
# Evaluator
evaluator = Evaluator(model)
if args.evaluate:
evaluator.evaluate(test_loader, dataset.query, dataset.gallery, cmc_flag=True)
return
print("==> Initialize source-domain class centroids and memorys ")
source_centers_all = []
memories = []
for dataset_i in range(len(datasets_src)):
dataset_source = datasets_src[dataset_i]
sour_cluster_loader = get_test_loader(dataset_source, args.height, args.width,
args.test_batch_size, args.workers, testset=sorted(dataset_source.train))
source_features, _ = extract_features(model, sour_cluster_loader, print_freq=50)
sour_fea_dict = collections.defaultdict(list)
for f, pid, _, _ in sorted(dataset_source.train):
sour_fea_dict[pid].append(source_features[f].unsqueeze(0))
source_centers = [torch.cat(sour_fea_dict[pid], 0).mean(0) for pid in sorted(sour_fea_dict.keys())]
source_centers = torch.stack(source_centers, 0) ## pid,2048
source_centers = F.normalize(source_centers, dim=1).cuda()
source_centers_all.append(source_centers)
curMemo = MemoryClassifier(model.module.num_features, source_centers.shape[0],
temp=args.temp, momentum=args.momentum).cuda()
curMemo.features = source_centers
curMemo.labels = torch.arange(num_classes[dataset_i]).cuda()
curMemo = nn.DataParallel(curMemo)
memories.append(curMemo)
del source_centers, sour_cluster_loader, sour_fea_dict
# Optimizer
params = [{"params": [value]} for value in model.module.params() if value.requires_grad]
optimizer = torch.optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay)
lr_scheduler = WarmupMultiStepLR(optimizer, milestones=[30, 50], gamma=0.1, warmup_factor=0.01,
warmup_iters=10, warmup_method="linear")
criterion = TripletLoss(args.margin, args.num_instances, False).cuda()
trainer = Trainer(args, model, memories, criterion)
for epoch in range(args.epochs):
# Calculate distance
print('==> start training epoch {} \t ==> learning rate = {}'.format(epoch, optimizer.param_groups[0]['lr']))
torch.cuda.empty_cache()
trainer.train(epoch, train_loader, optimizer,
print_freq=args.print_freq, train_iters=args.iters)
if (epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1):
mAP = evaluator.evaluate(test_loader, dataset.query, dataset.gallery, cmc_flag=False)
is_best = (mAP > best_mAP)
best_mAP = max(mAP, best_mAP)
save_checkpoint({
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'best_mAP': best_mAP,
}, is_best, fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar'))
print('\n * Finished epoch {:3d} model mAP: {:5.1%} best: {:5.1%}{}\n'.
format(epoch, mAP, best_mAP, ' *' if is_best else ''))
lr_scheduler.step()
print('==> Test with the best model:')
checkpoint = load_checkpoint(osp.join(args.logs_dir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['state_dict'])
evaluator.evaluate(test_loader, dataset.query, dataset.gallery, cmc_flag=True)
end_time = time.monotonic()
print('Total running time: ', timedelta(seconds=end_time - start_time))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Self-paced contrastive learning on unsupervised re-ID")
# data
parser.add_argument('-d', '--dataset', type=str, default='market1501',
choices=datasets.names())
parser.add_argument('--dataset_src1', type=str, default='cuhknp',
choices=datasets.names())
parser.add_argument('--dataset_src2', type=str, default='dukemtmc',
choices=datasets.names())
parser.add_argument('--dataset_src3', type=str, default='msmt17v1',
choices=datasets.names())
parser.add_argument('-b', '--batch-size', type=int, default=64)
parser.add_argument('--test-batch-size', type=int, default=256)
parser.add_argument('-j', '--workers', type=int, default=4)
parser.add_argument('--height', type=int, default=256, help="input height")
parser.add_argument('--width', type=int, default=128, help="input width")
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('-a', '--arch', type=str, default='resMeta',
choices=models.names())
parser.add_argument('--features', type=int, default=0)
parser.add_argument('--dropout', type=float, default=0)
parser.add_argument('--BNNeck', action='store_true',
help="use triplet and BNNeck")
parser.add_argument('--momentum', type=float, default=0.2,
help="update momentum for the hybrid memory")
parser.add_argument('--BNtype', type=str, default='sample',
help=" MetaBN type ")
##loss
parser.add_argument('--margin', type=float, default=0.3,
help="margin of the triplet loss, default: 0.3")
# optimizer
parser.add_argument('--lr', type=float, default=0.00035,
help="learning rate")
parser.add_argument('--weight-decay', type=float, default=5e-4)
parser.add_argument('--epochs', type=int, default=60)
parser.add_argument('--iters', type=int, default=200)
parser.add_argument('--step-size', type=int, default=20)
# training configs
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--print-freq', type=int, default=5)
parser.add_argument('--eval-step', type=int, default=1)
parser.add_argument('--temp', type=float, default=0.05,
help="temperature for scaling contrastive loss")
# path
working_dir = osp.dirname(osp.abspath(__file__))
parser.add_argument('--data-dir', type=str, metavar='PATH',
default=osp.join(working_dir, 'data'))
parser.add_argument('--logs-dir', type=str, metavar='PATH',
default=osp.join(working_dir, 'logs'))
parser.add_argument('--resume', type=str, default='')
parser.add_argument('--evaluate', action='store_true',
help="evaluation only")
main()