def test(model,
queryloader,
galleryloader,
train_query_loader,
train_gallery_loader,
use_gpu,
test_batch,
loss_type,
euclidean_distance_loss,
epoch,
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20],
return_distmat=False):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
tqf, tq_pids, tq_camids = [], [], []
for batch_idx, (imgs, pids, _, _, camids,
_) in enumerate(train_query_loader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
tqf.append(features)
tq_pids.extend(pids)
tq_camids.extend(camids)
tqf = torch.cat(tqf, 0)
tq_pids = np.asarray(tq_pids)
tq_camids = np.asarray(tq_camids)
print(
"Extracted features for train_query set, obtained {}-by-{} matrix".
format(tqf.size(0), tqf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
tgf, tg_pids, tg_camids = [], [], []
for batch_idx, (imgs, pids, _, _, camids,
_) in enumerate(train_gallery_loader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
tgf.append(features)
tg_pids.extend(pids)
tg_camids.extend(camids)
tgf = torch.cat(tgf, 0)
tg_pids = np.asarray(tg_pids)
tg_camids = np.asarray(tg_camids)
print(
"Extracted features for train_gallery set, obtained {}-by-{} matrix"
.format(tgf.size(0), tgf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
print("Start compute distmat.")
if loss_type in euclidean_distance_loss:
m, n = tqf.size(0), tgf.size(0)
distmat = torch.pow(tqf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(tgf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, tqf, tgf.t())
distmat = distmat.numpy()
elif loss_type == 'angle':
tvec_dot = torch.matmul(tqf, tgf.t())
tqf_len = tqf.norm(dim=1, keepdim=True)
tgf_len = tgf.norm(dim=1, keepdim=True)
tvec_len = torch.matmul(tqf_len, tgf_len.t()) + 1e-5
distmat = -torch.div(tvec_dot, tvec_len).numpy()
else:
raise KeyError("Unsupported loss: {}".format(loss_type))
print("Compute distmat done.")
print("distmat shape:", distmat.shape)
print("Start computing CMC and mAP")
start_time = time.time()
cmc, mAP = evaluate(distmat,
tq_pids,
tg_pids,
tq_camids,
tg_camids,
use_metric_cuhk03=use_metric_cuhk03,
use_cython=False)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Evaluate train data time (h:m:s): {}.".format(elapsed))
print("Train data results ----------")
print("Epoch {} trmAP: {:.2%}".format(epoch, mAP))
print("CMC curve")
for r in ranks:
print("Epoch {} trRank-{:<3}: {:.2%}".format(epoch, r, cmc[r - 1]))
print("------------------")
with torch.no_grad():
qf, q_pids, q_camids, q_paths = [], [], [], []
for batch_idx, (imgs, pids, _, _, camids,
paths) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
q_paths.extend(paths)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
q_paths = np.asarray(q_paths)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
gf, g_pids, g_camids, g_paths = [], [], [], []
for batch_idx, (imgs, pids, _, _, camids,
paths) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
g_paths.extend(paths)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
g_paths = np.asarray(g_paths)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
print("Start compute distmat.")
if loss_type in euclidean_distance_loss:
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
elif loss_type == 'angle':
vec_dot = torch.matmul(qf, gf.t())
qf_len = qf.norm(dim=1, keepdim=True)
gf_len = gf.norm(dim=1, keepdim=True)
vec_len = torch.matmul(qf_len, gf_len.t()) + 1e-5
distmat = -torch.div(vec_dot, vec_len).numpy()
else:
raise KeyError("Unsupported loss: {}".format(loss_type))
print("Compute distmat done.")
print("distmat shape:", distmat.shape)
# result = {'query_f': qf.numpy(),
# 'query_cam': q_camids, 'query_label': q_pids, 'quim_path': q_paths,
# 'gallery_f': gf.numpy(),
# 'gallery_cam': g_camids, 'gallery_label': g_pids, 'gaim_path': g_paths}
# scipy.io.savemat(os.path.join(args.save_dir, 'features_' + str(60) + '.mat'), result)
# dist_mat_dict = {'dist_mat': distmat}
# scipy.io.savemat(os.path.join(args.save_dir, 'features_' + str(60) + '_dist.mat'), dist_mat_dict)
print("Start computing CMC and mAP")
start_time = time.time()
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=use_metric_cuhk03,
use_cython=False)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Evaluate test data time (h:m:s): {}.".format(elapsed))
print("Test data results ----------")
print("Epoch {} temAP: {:.2%}".format(epoch, mAP))
print("CMC curve")
for r in ranks:
print("Epoch {} teRank-{:<3}: {:.2%}".format(epoch, r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0], mAP
def testCATfeature(model1,
model2,
queryloader,
galleryloader,
test_batch,
loss_type,
euclidean_distance_loss,
epoch,
use_metric_cuhk03=False,
ranks=[1, 5, 10, 20],
return_distmat=False):
batch_time = AverageMeter()
model1.eval()
model2.eval()
with torch.no_grad():
tqf, tq_pids, tq_camids = [], [], []
for batch_idx, (imgs1, imgs2, _, pids,
camids) in enumerate(queryloader):
imgs1 = Variable(imgs1.cuda())
imgs2 = Variable(imgs2.cuda())
end = time.time()
features1 = model1(imgs1)
features2 = model2(imgs2)
features = torch.cat((features1, features2), 1)
#features = features1
batch_time.update(time.time() - end)
features = features.data.cpu()
tqf.append(features)
tq_pids.extend(pids)
tq_camids.extend(camids)
tqf = torch.cat(tqf, 0)
tq_pids = np.asarray(tq_pids)
tq_camids = np.asarray(tq_camids)
print(
"Extracted features for train_query set, obtained {}-by-{} matrix".
format(tqf.size(0), tqf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
tgf, tg_pids, tg_camids = [], [], []
for batch_idx, (imgs1, imgs2, _, pids,
camids) in enumerate(galleryloader):
imgs1 = imgs1.cuda()
imgs2 = imgs2.cuda()
end = time.time()
features1 = model1(imgs1)
features2 = model2(imgs2)
features = torch.cat((features1, features2), 1)
#features=features1
batch_time.update(time.time() - end)
features = features.data.cpu()
tgf.append(features)
tg_pids.extend(pids)
tg_camids.extend(camids)
tgf = torch.cat(tgf, 0)
tg_pids = np.asarray(tg_pids)
tg_camids = np.asarray(tg_camids)
print(
"Extracted features for train_gallery set, obtained {}-by-{} matrix"
.format(tgf.size(0), tgf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, test_batch))
print("Start compute distmat.")
if loss_type in euclidean_distance_loss:
m, n = tqf.size(0), tgf.size(0)
distmat = torch.pow(tqf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(tgf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, tqf, tgf.t())
distmat = distmat.numpy()
elif loss_type == 'angle':
tvec_dot = torch.matmul(tqf, tgf.t())
tqf_len = tqf.norm(dim=1, keepdim=True)
tgf_len = tgf.norm(dim=1, keepdim=True)
tvec_len = torch.matmul(tqf_len, tgf_len.t()) + 1e-5
distmat = -torch.div(tvec_dot, tvec_len).numpy()
else:
raise KeyError("Unsupported loss: {}".format(loss_type))
print("Compute distmat done.")
print("distmat shape:", distmat.shape)
print("Start computing CMC and mAP")
start_time = time.time()
cmc, mAP = evaluate(distmat,
tq_pids,
tg_pids,
tq_camids,
tg_camids,
use_metric_cuhk03=use_metric_cuhk03,
use_cython=False)
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
print("Evaluate train data time (h:m:s): {}.".format(elapsed))
print("Train data results ----------")
print("Epoch {} trmAP: {:.2%}".format(epoch, mAP))
print("CMC curve")
for r in ranks:
print("Epoch {} trRank-{:<3}: {:.2%}".format(epoch, r, cmc[r - 1]))
print("------------------")
if return_distmat:
return distmat
return cmc[0], mAP