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test.py
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from __future__ import print_function
import argparse
import time
import torch.backends.cudnn as cudnn
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
from model 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-net1', default='sysu_lcnl_nr0_net1.t', type=str,
help='resume net1 from checkpoint')
parser.add_argument('--resume-net2', default='sysu_lcnl_nr0_net2.t', type=str,
help='resume net2 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('--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='dart', type=str,
metavar='m', help='method type: base or dart')
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('--seed', default=0, type=int,
metavar='t', help='random seed')
parser.add_argument('--gpu', default='3', 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('--data-path', default='../../dataset/SYSU-MM01', type=str, help='path to dataset')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
dataset = args.dataset
data_path = args.data_path
if dataset == 'sysu':
n_class = 395
test_mode = [1, 2]
elif dataset == 'regdb':
n_class = 206
test_mode = [2, 1]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
best_acc = 0 # best test accuracy
start_epoch = 0
pool_dim = 2048
print('==> Building model..')
if args.method == 'base':
net = embed_net(n_class, no_local='off', gm_pool='off', arch=args.arch)
else:
net1 = embed_net(n_class, no_local='on', gm_pool='on', arch=args.arch)
net2 = embed_net(n_class, no_local='on', gm_pool='on', arch=args.arch)
net1.to(device)
net2.to(device)
cudnn.benchmark = True
checkpoint_path = args.model_path
print('==> Loading data..')
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomCrop((args.img_h, args.img_w)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((args.img_h, args.img_w)),
transforms.ToTensor(),
normalize,
])
end = time.time()
def extract_gall_feat(gall_loader, net):
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
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)
input = input.cuda()
_, feat_fc = net(input, input, test_mode[0])
gall_feat_fc[ptr:ptr + batch_num, :] = feat_fc.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
return gall_feat_fc
def extract_query_feat(query_loader, net):
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
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)
input = input.cuda()
_, feat_fc = net(input, input, test_mode[1])
query_feat_fc[ptr:ptr + batch_num, :] = feat_fc.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
return query_feat_fc
if dataset == 'sysu':
print('==> Resuming from checkpoint..')
if len(args.resume_net1) > 0:
model_path1 = checkpoint_path + args.resume_net1
model_path2 = checkpoint_path + args.resume_net2
# model_path = checkpoint_path + 'sysu_awg_p4_n8_lr_0.1_seed_0_best.t'
if os.path.isfile(model_path1):
print('==> loading checkpoint {} and {}'.format(args.resume_net1, args.resume_net2))
checkpoint1 = torch.load(model_path1)
checkpoint2 = torch.load(model_path2)
net1.load_state_dict(checkpoint1['net'])
net2.load_state_dict(checkpoint2['net'])
print('==> loaded checkpoint {} (epoch {}), {} (epoch {})'
.format(args.resume_net1, checkpoint1['epoch'], args.resume_net2, checkpoint2['epoch']))
else:
print('==> no checkpoint found at {} and {}'.format(args.resume_net1, args.resume_net2))
# 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_fc1 = extract_query_feat(query_loader, net1)
query_feat_fc2 = extract_query_feat(query_loader, net2)
query_feat_fc = (query_feat_fc1 + query_feat_fc2) / 2.
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_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_fc1 = extract_gall_feat(trial_gall_loader, net1)
gall_feat_fc2 = extract_gall_feat(trial_gall_loader, net2)
gall_feat_fc = (gall_feat_fc1 + gall_feat_fc2) / 2.
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
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_mINP = all_mINP + mINP
print('Test Trial: {}'.format(trial))
print(
'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))
elif dataset == 'regdb':
for trial in range(10):
test_trial = trial + 1
model_path1 = checkpoint_path + (args.resume_net1).format(test_trial)
model_path2 = checkpoint_path + (args.resume_net2).format(test_trial)
if os.path.isfile(model_path1):
print('==> loading checkpoint {} and {}'.format((args.resume_net1).format(test_trial),
(args.resume_net2).format(test_trial)))
checkpoint1 = torch.load(model_path1)
checkpoint2 = torch.load(model_path2)
net1.load_state_dict(checkpoint1['net'])
net2.load_state_dict(checkpoint2['net'])
print('==> loaded checkpoint {} (epoch {}), {} (epoch {})'
.format((args.resume_net1).format(test_trial), checkpoint1['epoch'],
(args.resume_net2).format(test_trial), checkpoint2['epoch']))
else:
print('==> no checkpoint found at {} and {}'.format(args.resume_net1, args.resume_net2))
# 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_fc1 = extract_query_feat(query_loader, net1)
query_feat_fc2 = extract_query_feat(query_loader, net2)
query_feat_fc = (query_feat_fc1 + query_feat_fc2) / 2.
gall_feat_fc1 = extract_gall_feat(gall_loader, net1)
gall_feat_fc2 = extract_gall_feat(gall_loader, net2)
gall_feat_fc = (gall_feat_fc1 + gall_feat_fc2) / 2.
if args.tvsearch:
print('thermal to visible')
distmat = np.matmul(gall_feat_fc, np.transpose(query_feat_fc))
cmc, mAP, mINP = eval_regdb(-distmat, gall_label, query_label)
else:
print('visible to thermal')
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
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_mINP = all_mINP + mINP
print('Test Trial: {}'.format(trial))
print(
'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))
total_trial = 10
cmc = all_cmc / total_trial
mAP = all_mAP / total_trial
mINP = all_mINP / total_trial
print('All Average:')
print('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))