def test(epoch):
# switch to evaluation mode
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
gall_feat = np.zeros((ngall, 2048))
gall_feat_att = np.zeros((ngall, 2048))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat, feat_att = net(input, input, test_mode[0])
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
gall_feat_att[ptr:ptr + batch_num, :] = feat_att.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
# switch to evaluation
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat = np.zeros((nquery, 2048))
query_feat_att = np.zeros((nquery, 2048))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat, feat_att = net(input, input, test_mode[1])
query_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
query_feat_att[ptr:ptr + batch_num, :] = feat_att.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
start = time.time()
# compute the similarity
distmat = np.matmul(query_feat, np.transpose(gall_feat))
distmat_att = np.matmul(query_feat_att, np.transpose(gall_feat_att))
# evaluation
if dataset == 'regdb':
cmc, mAP, mINP = eval_regdb(-distmat, query_label, gall_label)
cmc_att, mAP_att, mINP_att = eval_regdb(-distmat_att, query_label, gall_label)
elif dataset == 'sysu':
cmc, mAP, mINP = eval_sysu(-distmat, query_label, gall_label, query_cam, gall_cam)
cmc_att, mAP_att, mINP_att = eval_sysu(-distmat_att, query_label, gall_label, query_cam, gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time() - start))
writer.add_scalar('rank1', cmc[0], epoch)
writer.add_scalar('mAP', mAP, epoch)
writer.add_scalar('mINP', mINP, epoch)
writer.add_scalar('rank1_att', cmc_att[0], epoch)
writer.add_scalar('mAP_att', mAP_att, epoch)
writer.add_scalar('mINP_att', mINP_att, epoch)
return cmc, mAP, mINP, cmc_att, mAP_att, mINP_att
def testPlus(epoch, rerank=False):
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
gall_feat = []
gall_feat2 = []
gall_feat3 = []
with torch.no_grad():
for batch_idx, (input, label) in enumerate(tqdm(gall_loader)):
input = Variable(input.cuda())
feat_pool, feat_pool2, feat, feat2 = net(
input, input, test_mode[0])
feat = feat.data
gall_feat.append(feat)
gall_feat = torch.cat(gall_feat, 0)
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
net.eval()
print('Extracting Query Feature...')
start = time.time()
query_feat = []
with torch.no_grad():
for batch_idx, (input, label) in enumerate(tqdm(query_loader)):
input = Variable(input.cuda())
feat_pool, feat_pool2, feat, feat2 = net(
input, input, test_mode[1])
feat = feat.data
query_feat.append(feat)
query_feat = torch.cat(query_feat, 0)
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
start = time.time()
if rerank:
print("==> reranking ....")
distmat = reranking(query_feat, gall_feat)
cmc, mAP = eval_sysu(distmat, query_label, gall_label, query_cam,
gall_cam)
else:
distmat = np.matmul(query_feat, np.transpose(gall_feat))
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam,
gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time() - start))
return cmc, mAP
def test(epoch):
# switch to evaluation mode
l2norm = Normalize()
net.eval()
print('Extracting Gallery Feature...')
start = time.clock()
ptr = 0
gall_feat = np.zeros((ngall, final_dim + 1024))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
_, _, feat, _, _, feat3 = net(input)
feat = l2norm(feat)
feat3 = l2norm(feat3)
feat = torch.cat((feat, feat3), dim=1)
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.clock() - start))
# switch to evaluation mode
net.eval()
print('Extracting Query Feature...')
start = time.clock()
ptr = 0
query_feat = np.zeros((nquery, final_dim + 1024))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
_, _, feat, _, _, feat3 = net(input)
feat = l2norm(feat)
feat3 = l2norm(feat3)
feat = torch.cat((feat, feat3), dim=1)
query_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.clock() - start))
start = time.clock()
distmat = np.matmul(query_feat, np.transpose(gall_feat))
# evaluation
if dataset == 'regdb':
cmc, mAP = eval_regdb(-distmat, query_label, gall_label)
elif dataset == 'sysu':
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam,
gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.clock() - start))
return cmc, mAP
def test(epoch):
# switch to evaluation mode
net.eval()
print ('Extracting Gallery Feature...')
start = time.time()
ptr = 0
gall_feat = np.zeros((ngall, 2048*7))
with torch.no_grad():
for batch_idx, (input, label ) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat,feat_part = net(input, input, test_mode[0])
feat_temp = torch.cat(feat_part,1)
feat_temp = torch.cat((feat_temp,feat),1)
gall_feat[ptr:ptr+batch_num,: ] = feat_temp.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time()-start))
# switch to evaluation mode
net.eval()
print ('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat = np.zeros((nquery, 2048*7))
with torch.no_grad():
for batch_idx, (input, label ) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat_part = net(input, input, test_mode[1])
feat_temp = torch.cat(feat_part,1)
feat_temp = torch.cat((feat_temp,feat),1)
query_feat[ptr:ptr+batch_num,: ] = feat_temp.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time()-start))
start = time.time()
# compute the similarity
gall_feat = 1.*gall_feat/np.repeat(np.linalg.norm(gall_feat,2,1,True),gall_feat.shape[1],1)
query_feat = 1.*query_feat/np.repeat(np.linalg.norm(query_feat,2,1,True),query_feat.shape[1],1)
distmat = np.matmul(query_feat, np.transpose(gall_feat))
# evaluation
if dataset =='regdb':
cmc, mAP = eval_regdb(-distmat, query_label, gall_label)
elif dataset =='sysu':
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam, gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time()-start))
return cmc, mAP
def test(epoch):
# switch to evaluation mode
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
gall_feat = np.zeros((ngall, args.low_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat_pool, feat, feat_vis, feat_inf, out_u, feat_u, p_v, p_i = net(
input, input, test_mode[0])
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
# switch to evaluation mode
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat = np.zeros((nquery, args.low_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat_pool, feat, feat_vis, feat_inf, out_u, feat_u, p_v, p_i = net(
input, input, test_mode[1])
query_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
start = time.time()
# compute the similarity
distmat = np.matmul(query_feat, np.transpose(gall_feat))
# evaluation
if dataset == 'regdb':
cmc, mAP = eval_regdb(-distmat, query_label, gall_label)
elif dataset == 'sysu':
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam,
gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time() - start))
return cmc, mAP
gall_feat, gall_feat_pool = extract_gall_feat(trial_gall_loader)
gall_feat = 1. * gall_feat / np.repeat(
np.linalg.norm(gall_feat, 2, 1, True), gall_feat.shape[1], 1)
gall_feat_pool = 1. * gall_feat_pool / np.repeat(
np.linalg.norm(gall_feat_pool, 2, 1, True),
gall_feat_pool.shape[1], 1)
query_feat = 1. * query_feat / np.repeat(
np.linalg.norm(query_feat, 2, 1, True), query_feat.shape[1], 1)
query_feat_pool = 1. * query_feat_pool / np.repeat(
np.linalg.norm(query_feat_pool, 2, 1, True),
query_feat_pool.shape[1], 1)
# fc feature
distmat = np.matmul(query_feat, np.transpose(gall_feat))
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam,
gall_cam)
# pool5 feature
distmat_pool = np.matmul(query_feat_pool, np.transpose(gall_feat_pool))
cmc_pool, mAP_pool = eval_sysu(-distmat_pool, query_label, gall_label,
query_cam, gall_cam)
if trial == 0:
all_cmc = cmc
all_mAP = mAP
all_cmc_pool = cmc_pool
all_mAP_pool = mAP_pool
else:
all_cmc = all_cmc + cmc
all_mAP = all_mAP + mAP
all_cmc_pool = all_cmc_pool + cmc_pool
all_mAP_pool = all_mAP_pool + mAP_pool
def test(epoch):
# switch to evaluation mode
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
if args.pcb == 'on':
feat_dim = args.num_strips * args.local_feat_dim
else:
feat_dim = 2048
gall_feat = np.zeros((ngall, feat_dim))
gall_feat_att = np.zeros((ngall, feat_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
if args.pcb == 'on':
feat = net(input, input, test_mode[0])
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
else:
feat, feat_att = net(input, input, test_mode[0])
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
gall_feat_att[ptr:ptr +
batch_num, :] = feat_att.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
# switch to evaluation
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat = np.zeros((nquery, feat_dim))
query_feat_att = np.zeros((nquery, feat_dim))
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
if args.pcb == 'on':
feat = net(input, input, test_mode[1])
query_feat[ptr:ptr +
batch_num, :] = feat.detach().cpu().numpy()
else:
feat, feat_att = net(input, input, test_mode[1])
query_feat[ptr:ptr +
batch_num, :] = feat.detach().cpu().numpy()
query_feat_att[ptr:ptr +
batch_num, :] = feat_att.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
start = time.time()
if args.re_rank == 'random_walk':
distmat = random_walk(query_feat, gall_feat)
if args.pcb == 'off':
distmat_att = random_walk(query_feat_att, gall_feat_att)
elif args.re_rank == 'k_reciprocal':
distmat = k_reciprocal(query_feat, gall_feat)
if args.pcb == 'off':
distmat_att = k_reciprocal(query_feat_att, gall_feat_att)
elif args.re_rank == 'no':
# compute the similarity
distmat = -np.matmul(query_feat, np.transpose(gall_feat))
if args.pcb == 'off':
distmat_att = -np.matmul(query_feat_att,
np.transpose(gall_feat_att))
# evaluation
if dataset == 'regdb':
cmc, mAP, mINP = eval_regdb(distmat, query_label, gall_label)
if args.pcb == 'off':
cmc_att, mAP_att, mINP_att = eval_regdb(distmat_att, query_label,
gall_label)
elif dataset == 'sysu':
cmc, mAP, mINP = eval_sysu(distmat, query_label, gall_label, query_cam,
gall_cam)
if args.pcb == 'off':
cmc_att, mAP_att, mINP_att = eval_sysu(distmat_att, query_label,
gall_label, query_cam,
gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time() - start))
writer.add_scalar('rank1', cmc[0], epoch)
writer.add_scalar('mAP', mAP, epoch)
writer.add_scalar('mINP', mINP, epoch)
if args.pcb == 'off':
writer.add_scalar('rank1_att', cmc_att[0], epoch)
writer.add_scalar('mAP_att', mAP_att, epoch)
writer.add_scalar('mINP_att', mINP_att, epoch)
return cmc, mAP, mINP, cmc_att, mAP_att, mINP_att
else:
return cmc, mAP, mINP
def test(net, epoch):
# switch to evaluation mode
net.eval()
print('Extracting Gallery Feature...')
start = time.time()
ptr = 0
gall_feat = np.zeros((ngall, args.low_dim)) #args.low_dim
label_g = []
with torch.no_grad():
for batch_idx, (input, label) in enumerate(gall_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat_pool, feat = net(input, input, test_mode[0])
label_g.append(label)
gall_feat[ptr:ptr + batch_num, :] = feat.detach().cpu().numpy()
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
# switch to evaluation mode
net.eval()
print('Extracting Query Feature...')
start = time.time()
ptr = 0
query_feat = np.zeros((nquery, args.low_dim))
label_q = []
with torch.no_grad():
for batch_idx, (input, label) in enumerate(query_loader):
batch_num = input.size(0)
input = Variable(input.cuda())
feat_pool, feat = net(input, input, test_mode[1])
query_feat[ptr:ptr +
batch_num, :] = feat.detach().cpu().numpy()
label_q.append(label)
ptr = ptr + batch_num
print('Extracting Time:\t {:.3f}'.format(time.time() - start))
start = time.time()
# compute the similarity
distmat = np.matmul(query_feat, np.transpose(gall_feat))
# output=np.concatenate((query_feat,gall_feat),0)
# labels=label_q+label_g
# figure = draw_cluster(output, labels, len(label_q),len(label_g))
# #
# plt.savefig('/home/zhangc/projects/cross_modality_V/BDTR_modal_loss_cbam_debug/image/cluster/' + str(
# epoch) + '_test_'+suffix_id+'.jpg')
# plt.show()
# plt.pause(1)
# plt.close()
# evaluation
if dataset == 'regdb':
cmc, mAP = eval_regdb(-distmat, query_label, gall_label)
elif dataset == 'sysu':
cmc, mAP = eval_sysu(-distmat, query_label, gall_label, query_cam,
gall_cam)
print('Evaluation Time:\t {:.3f}'.format(time.time() - start))
# uni_que = np.unique(query_label)
# n_q, n_g = 0, 0
# output = []
# labels = []
# for i in range(30):
# label_id = uni_que[randrange(0, len(uni_que))]
# index_q = [i for i, a in enumerate(query_label) if a == label_id]
# index_g = [i for i, a in enumerate(gall_label) if a == label_id]
# output.extend(np.concatenate((query_feat[index_q, :], gall_feat[index_g, :]), 0))
# tmp_q = query_label[index_q]
# tmp_g = gall_label[index_g]
# n_q += len(tmp_q)
# n_g += len(tmp_g)
# labels.extend(np.concatenate((tmp_q, tmp_g), 0))
#
# figure = draw_cluster(output, labels, n_q, n_g)
# #
# suffix_id = 'DEBUG_30'
# plt.savefig(
# '/home/zhangc/projects/cross_modality_V/BDTR_modal_loss_cbam_debug/image/cluster/' + '_train_' + suffix_id + '.jpg')
# plt.show()
# plt.pause(1)
# plt.close()
return cmc, mAP
