def eval():
net.is_train = False
net.eval()
print("Evaluating......")
query = np.concatenate([
net(inputs.to(device)).detach().cpu().numpy()
for inputs, _ in queryloader
])
test = np.concatenate([
net(inputs.to(device)).detach().cpu().numpy()
for inputs, _ in testloader
])
dist = cdist(query, test)
r = eval_tools.cmc(dist,
query_set.ids,
test_set.ids,
query_set.cameras,
test_set.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True,
same_cam_valid=True)
m_ap = eval_tools.mean_ap(dist,
query_set.ids,
test_set.ids,
query_set.cameras,
test_set.cameras,
same_cam_valid=True)
print(
colored(
'model:%s mAP=%f, r@1=%f, r@3=%f, r@5=%f, r@10=%f' %
(os.path.basename(args.checkpoint), m_ap, r[0], r[2], r[4], r[9]),
"red"))
def eval(epoch):
net.is_train = False
net.eval()
query = np.concatenate([
net(inputs.to(device)).detach().cpu().numpy()
for inputs, _ in queryloader
])
test = np.concatenate([
net(inputs.to(device)).detach().cpu().numpy()
for inputs, _ in testloader
])
dist = cdist(query, test)
r = eval_tools.cmc(dist,
data.query.ids,
data.test.ids,
data.query.cameras,
data.test.cameras,
separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True,
same_cam_valid=True)
m_ap = eval_tools.mean_ap(dist,
data.query.ids,
data.test.ids,
data.query.cameras,
data.test.cameras,
same_cam_valid=True)
print(
colored(
'epoch[%d]: mAP=%f, r@1=%f, r@3=%f, r@5=%f, r@10=%f' %
(epoch + 1, m_ap, r[0], r[2], r[4], r[9]), "yellow"))