-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_sle_hsl_cfl.py
482 lines (396 loc) · 19.9 KB
/
test_sle_hsl_cfl.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
from __future__ import print_function
import argparse
import time
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import torch.utils.data as data
import torchvision.transforms as transforms
from data_loader import SYSUData, RegDBData, TestData
from data_manager import *
from eval_metrics import eval_sysu, eval_regdb, eval_llcm
from model_sle_hsl_cfl import embed_net
from utils import *
parser = argparse.ArgumentParser(description='PyTorch Cross-Modality Training')
parser.add_argument('--dataset', default='sysu', help='dataset name: regdb or sysu]')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate, 0.00035 for adam')
parser.add_argument('--optim', default='sgd', type=str, help='optimizer')
parser.add_argument('--arch', default='resnet50', type=str,
help='network baseline: resnet50')
parser.add_argument('--resume', '-r', default='', type=str,
help='resume from checkpoint')
parser.add_argument('--test-only', action='store_true', help='test only')
parser.add_argument('--model_path', default='save_model/', type=str,
help='model save path')
parser.add_argument('--save_epoch', default=20, type=int,
metavar='s', help='save model every 10 epochs')
parser.add_argument('--log_path', default='log/', type=str,
help='log save path')
parser.add_argument('--vis_log_path', default='log/vis_log/', type=str,
help='log save path')
parser.add_argument('--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--img_w', default=144, type=int,
metavar='imgw', help='img width')
parser.add_argument('--img_h', default=288, type=int,
metavar='imgh', help='img height')
parser.add_argument('--batch-size', default=8, type=int,
metavar='B', help='training batch size')
parser.add_argument('--test-batch', default=64, type=int,
metavar='tb', help='testing batch size')
parser.add_argument('--method', default='awg', type=str,
metavar='m', help='method type: base or awg')
parser.add_argument('--margin', default=0.3, type=float,
metavar='margin', help='triplet loss margin')
parser.add_argument('--num_pos', default=4, type=int,
help='num of pos per identity in each modality')
parser.add_argument('--trial', default=1, type=int,
metavar='t', help='trial (only for RegDB dataset)')
parser.add_argument('--seed', default=0, type=int,
metavar='t', help='random seed')
parser.add_argument('--gpu', default='0', type=str,
help='gpu device ids for CUDA_VISIBLE_DEVICES')
parser.add_argument('--mode', default='all', type=str, help='all or indoor for sysu')
parser.add_argument('--tvsearch', action='store_true', help='whether thermal to visible search on RegDB')
parser.add_argument('--stage', default=23, type=int,
metavar='t', help='random seed')
parser.add_argument('--depth', default=2, type=int,
metavar='t', help='random seed')
parser.add_argument('--head', default=4, type=int,
metavar='t', help='random seed')
parser.add_argument('--reduce', default=0, type=int,
metavar='t', help='random seed')
parser.add_argument('--graphw', default=1.0, type=float,
metavar='t', help='random seed')
parser.add_argument('--gatw', default=1.0, type=float,
metavar='t', help='random seed')
parser.add_argument('--vt', default=0, type=int,
metavar='t', help='random seed')
parser.add_argument('--whiten', default=1, type=int,
metavar='whiten', help='random seed')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
dataset = args.dataset
if dataset == 'sysu':
data_path = '/SSD_dataset/CMReID/SYSU-MM01/ori_data/'
n_class = 395
test_mode = [1, 2]
elif dataset == 'regdb':
data_path = '/SSD_dataset/CMReID/RegDB/'
n_class = 206
test_mode = [2, 1]
elif dataset == 'llcm':
data_path = '/SSD_dataset/CMReID/LLCM/LLCM/'
log_path = args.log_path + 'llcm_log/'
n_class = 713
if args.vt == 1:
test_mode = [2, 1] #[2, 1]: VIS to IR; [1, 2]: IR to VIS
else:
test_mode = [1, 2]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0
pool_dim = 3584
print('==> Building model..')
if args.method == 'base':
net = embed_net(n_class, no_local='on', gm_pool='on', arch=args.arch, dataset=dataset, plearn=1,
stage=23, depth=args.depth, head=args.head, graphw=args.graphw, gatw=args.gatw, whiten=args.whiten)
else:
net = embed_net(n_class, no_local='on', gm_pool='on', arch=args.arch, dataset=dataset, plearn=1,
stage=23, depth=args.depth, head=args.head, graphw=args.graphw, gatw=args.gatw, whiten=args.whiten)
net.to(device)
cudnn.benchmark = True
checkpoint_path = args.model_path
if args.method == 'id':
criterion = nn.CrossEntropyLoss()
criterion.to(device)
print('==> Loading data..')
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform_test = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((args.img_h, args.img_w)),
transforms.ToTensor(),
normalize,
])
class ChannelRGB(object):
""" Adaptive selects a channel or two channels.
Args:
probability: The probability that the Random Erasing operation will be performed.
sl: Minimum proportion of erased area against input image.
sh: Maximum proportion of erased area against input image.
r1: Minimum aspect ratio of erased area.
mean: Erasing value.
"""
def __init__(self, idx=0):
self.idx = idx
def __call__(self, img):
if self.idx == 0:
# random select R Channel
img[1, :, :] = img[0, :, :]
img[2, :, :] = img[0, :, :]
elif self.idx == 1:
# random select G Channel
img[0, :, :] = img[1, :, :]
img[2, :, :] = img[1, :, :]
elif self.idx == 2:
# random select B Channel
img[0, :, :] = img[2, :, :]
img[1, :, :] = img[2, :, :]
else:
img = img
return img
transform_visible_list = [
transforms.ToPILImage(),
transforms.Resize((args.img_h, args.img_w)),
transforms.ToTensor(),
normalize]
# transform_visible_list = transform_visible_list + [ChannelRGB(idx=2)]
transform_visible = transforms.Compose(transform_visible_list)
end = time.time()
def fliplr(img):
'''flip horizontal'''
inv_idx = torch.arange(img.size(3) - 1, -1, -1).long() # N x C x H x W
img_flip = img.index_select(3, inv_idx)
return img_flip
def extract_gall_feat(gall_loader):
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
gall_feat_pool = np.zeros((ngall, pool_dim))
gall_feat_fc = np.zeros((ngall, pool_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
flip_input = fliplr(input)
input = Variable(input.cuda())
feat_pool, feat_fc = net(input, input, test_mode[0])
flip_input = Variable(flip_input.cuda())
feat_pool_1, feat_fc_1 = net(flip_input, flip_input, test_mode[0])
feature_pool = (feat_pool.detach() + feat_pool_1.detach()) / 2
feature_fc = (feat_fc.detach() + feat_fc_1.detach()) / 2
# feature_pool = feat_pool.detach()
# feature_fc = feat_fc.detach()
fnorm_pool = torch.norm(feature_pool, p=2, dim=1, keepdim=True)
feature_pool = feature_pool.div(fnorm_pool.expand_as(feature_pool))
fnorm_fc = torch.norm(feature_fc, p=2, dim=1, keepdim=True)
feature_fc = feature_fc.div(fnorm_fc.expand_as(feature_fc))
gall_feat_pool[ptr:ptr + batch_num, :] = feature_pool.cpu().numpy()
gall_feat_fc[ptr:ptr + batch_num, :] = feature_fc.cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
return gall_feat_pool, gall_feat_fc
def extract_query_feat(query_loader):
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat_pool = np.zeros((nquery, pool_dim))
query_feat_fc = np.zeros((nquery, pool_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
flip_input = fliplr(input)
input = Variable(input.cuda())
feat_pool, feat_fc = net(input, input, test_mode[1])
flip_input = Variable(flip_input.cuda())
feat_pool_1, feat_fc_1 = net(flip_input, flip_input, test_mode[1])
feature_pool = (feat_pool.detach() + feat_pool_1.detach()) / 2
feature_fc = (feat_fc.detach() + feat_fc_1.detach()) / 2
# feature_pool = feat_pool.detach()
# feature_fc = feat_fc.detach()
fnorm_pool = torch.norm(feature_pool, p=2, dim=1, keepdim=True)
feature_pool = feature_pool.div(fnorm_pool.expand_as(feature_pool))
fnorm_fc = torch.norm(feature_fc, p=2, dim=1, keepdim=True)
feature_fc = feature_fc.div(fnorm_fc.expand_as(feature_fc))
query_feat_pool[ptr:ptr + batch_num, :] = feature_pool.cpu().numpy()
query_feat_fc[ptr:ptr + batch_num, :] = feature_fc.cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
return query_feat_pool, query_feat_fc
if dataset == 'llcm':
print('==> Resuming from checkpoint..')
model_path = ""
if os.path.isfile(model_path):
print('==> loading checkpoint {}'.format(args.resume))
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['net'])
print('==> loaded checkpoint {} (epoch {})'
.format(args.resume, checkpoint['epoch']))
else:
print('==> no checkpoint found at {}'.format(args.resume))
# testing set
query_img, query_label, query_cam = process_query_llcm(data_path, mode=test_mode[1])
gall_img, gall_label, gall_cam = process_gallery_llcm(data_path, mode=test_mode[0], trial=0)
nquery = len(query_label)
ngall = len(gall_label)
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" query | {:5d} | {:8d}".format(len(np.unique(query_label)), nquery))
print(" gallery | {:5d} | {:8d}".format(len(np.unique(gall_label)), ngall))
print(" ------------------------------")
queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
print('Data Loading Time:\t {:.3f}'.format(time.time() - end))
query_feat_pool, query_feat_fc = extract_query_feat(query_loader)
for trial in range(10):
gall_img, gall_label, gall_cam = process_gallery_llcm(data_path, mode=test_mode[0], trial=trial)
trial_gallset = TestData(gall_img, gall_label, transform=transform_test, img_size=(args.img_w, args.img_h))
trial_gall_loader = data.DataLoader(trial_gallset, batch_size=args.test_batch, shuffle=False, num_workers=4)
gall_feat_pool, gall_feat_fc = extract_gall_feat(trial_gall_loader)
# pool5 feature
distmat_pool = np.matmul(query_feat_pool, np.transpose(gall_feat_pool))
cmc_pool, mAP_pool, mINP_pool = eval_llcm(-distmat_pool, query_label, gall_label, query_cam, gall_cam)
# fc feature
distmat = np.matmul(query_feat_fc, np.transpose(gall_feat_fc))
cmc, mAP, mINP = eval_llcm(-distmat, query_label, gall_label, query_cam, gall_cam)
if trial == 0:
all_cmc = cmc
all_mAP = mAP
all_mINP = mINP
all_cmc_pool = cmc_pool
all_mAP_pool = mAP_pool
all_mINP_pool = mINP_pool
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_mINP = all_mINP + mINP
all_cmc_pool = all_cmc_pool + cmc_pool
all_mAP_pool = all_mAP_pool + mAP_pool
all_mINP_pool = all_mINP_pool + mINP_pool
print('Test Trial: {}'.format(trial))
print(
'FC: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
print(
'POOL: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc_pool[0], cmc_pool[4], cmc_pool[9], cmc_pool[19], mAP_pool, mINP_pool))
if dataset == 'sysu':
print('==> Resuming from checkpoint..')
model_path = "./sle_hsl_cfl_ckpt/sysu/save_model/sysu_adp_joint_co_nog_ch_nog_sq1_aug_G_erase_0.5_p4_n8_lr_0.1_seed_0_otri_1_stage_23_depth_2_head_4_resume_1_graphw_1.0_gatw_1.0_mricT_3_whiten_1_theta_0.5_aicT_3_lambda1_1.3_best.t"
if os.path.isfile(model_path):
print('==> loading checkpoint {}'.format(model_path))
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['net'], strict=True)
print('==> loaded checkpoint {} (epoch {})'
.format(model_path, checkpoint['epoch']))
else:
print('==> no checkpoint found at {}'.format(model_path))
# testing set
query_img, query_label, query_cam = process_query_sysu(data_path, mode=args.mode)
gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=args.mode, trial=0)
nquery = len(query_label)
ngall = len(gall_label)
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" query | {:5d} | {:8d}".format(len(np.unique(query_label)), nquery))
print(" gallery | {:5d} | {:8d}".format(len(np.unique(gall_label)), ngall))
print(" ------------------------------")
queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
print('Data Loading Time:\t {:.3f}'.format(time.time() - end))
query_feat_pool, query_feat_fc = extract_query_feat(query_loader)
for trial in range(10):
gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=args.mode, trial=trial)
trial_gallset = TestData(gall_img, gall_label, transform=transform_visible, img_size=(args.img_w, args.img_h))
trial_gall_loader = data.DataLoader(trial_gallset, batch_size=args.test_batch, shuffle=False, num_workers=4)
gall_feat_pool, gall_feat_fc = extract_gall_feat(trial_gall_loader)
# pool5 feature
distmat_pool = np.matmul(query_feat_pool, np.transpose(gall_feat_pool))
cmc_pool, mAP_pool, mINP_pool = eval_sysu(-distmat_pool, query_label, gall_label, query_cam, gall_cam)
# fc feature
distmat = np.matmul(query_feat_fc, np.transpose(gall_feat_fc))
cmc, mAP, mINP = eval_sysu(-distmat, query_label, gall_label, query_cam, gall_cam)
if trial == 0:
all_cmc = cmc
all_mAP = mAP
all_mINP = mINP
all_cmc_pool = cmc_pool
all_mAP_pool = mAP_pool
all_mINP_pool = mINP_pool
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_mINP = all_mINP + mINP
all_cmc_pool = all_cmc_pool + cmc_pool
all_mAP_pool = all_mAP_pool + mAP_pool
all_mINP_pool = all_mINP_pool + mINP_pool
print('Test Trial: {}'.format(trial))
print(
'FC: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
print(
'POOL: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc_pool[0], cmc_pool[4], cmc_pool[9], cmc_pool[19], mAP_pool, mINP_pool))
elif dataset == 'regdb':
for trial in range(10):
test_trial = trial + 1
model_path = ''.format(
test_trial)
if os.path.isfile(model_path):
print('==> loading checkpoint {}'.format(model_path))
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['net'])
# testing set
query_img, query_label = process_test_regdb(data_path, trial=test_trial, modal='visible')
gall_img, gall_label = process_test_regdb(data_path, trial=test_trial, modal='thermal')
gallset = TestData(gall_img, gall_label, transform=transform_test, img_size=(args.img_w, args.img_h))
gall_loader = data.DataLoader(gallset, batch_size=args.test_batch, shuffle=False, num_workers=args.workers)
nquery = len(query_label)
ngall = len(gall_label)
queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
print('Data Loading Time:\t {:.3f}'.format(time.time() - end))
query_feat_pool, query_feat_fc = extract_query_feat(query_loader)
gall_feat_pool, gall_feat_fc = extract_gall_feat(gall_loader)
if args.tvsearch:
# pool5 feature
distmat_pool = np.matmul(gall_feat_pool, np.transpose(query_feat_pool))
cmc_pool, mAP_pool, mINP_pool = eval_regdb(-distmat_pool, gall_label, query_label)
# fc feature
distmat = np.matmul(gall_feat_fc, np.transpose(query_feat_fc))
cmc, mAP, mINP = eval_regdb(-distmat, gall_label, query_label)
else:
# pool5 feature
distmat_pool = np.matmul(query_feat_pool, np.transpose(gall_feat_pool))
cmc_pool, mAP_pool, mINP_pool = eval_regdb(-distmat_pool, query_label, gall_label)
# fc feature
distmat = np.matmul(query_feat_fc, np.transpose(gall_feat_fc))
cmc, mAP, mINP = eval_regdb(-distmat, query_label, gall_label)
if trial == 0:
all_cmc = cmc
all_mAP = mAP
all_mINP = mINP
all_cmc_pool = cmc_pool
all_mAP_pool = mAP_pool
all_mINP_pool = mINP_pool
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_mINP = all_mINP + mINP
all_cmc_pool = all_cmc_pool + cmc_pool
all_mAP_pool = all_mAP_pool + mAP_pool
all_mINP_pool = all_mINP_pool + mINP_pool
print('Test Trial: {}'.format(trial))
print(
'FC: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
print(
'POOL: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc_pool[0], cmc_pool[4], cmc_pool[9], cmc_pool[19], mAP_pool, mINP_pool))
cmc = all_cmc / 10
mAP = all_mAP / 10
mINP = all_mINP / 10
cmc_pool = all_cmc_pool / 10
mAP_pool = all_mAP_pool / 10
mINP_pool = all_mINP_pool / 10
print('All Average:')
print('FC: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
print('POOL: Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
cmc_pool[0], cmc_pool[4], cmc_pool[9], cmc_pool[19], mAP_pool, mINP_pool))