def main():
conf = Conf()
conf.suppress_random()
device = conf.get_device()
args = parse(conf)
dest_path = args.dest_path / (Path(args.net1).name + '__vs__' +
Path(args.net2).name)
dest_path.mkdir(exist_ok=True, parents=True)
both_path = dest_path / 'both'
both_path.mkdir(exist_ok=True, parents=True)
net1_path = dest_path / Path(args.net1).name
net1_path.mkdir(exist_ok=True, parents=True)
net2_path = dest_path / Path(args.net2).name
net2_path.mkdir(exist_ok=True, parents=True)
orig_path = dest_path / 'orig'
orig_path.mkdir(exist_ok=True, parents=True)
# ---- Restore net
net1 = Saver.load_net(args.net1, args.chk_net1,
args.dataset_name).to(device)
net2 = Saver.load_net(args.net2, args.chk_net2,
args.dataset_name).to(device)
net1.eval()
net2.eval()
train_loader, query_loader, gallery_loader, queryimg_loader, galleryimg_loader = \
get_dataloaders(args.dataset_name, conf.nas_path, device, args)
# register hooks
hook_net_1, hook_net_2 = Hook(), Hook()
net1.backbone.features_layers[4].register_forward_hook(hook_net_1)
net2.backbone.features_layers[4].register_forward_hook(hook_net_2)
dst_idx = 0
for idx_batch, (vids,
*_) in enumerate(tqdm(galleryimg_loader, 'iterating..')):
if idx_batch < len(galleryimg_loader) - 50:
continue
net1.zero_grad()
net2.zero_grad()
hook_net_1.reset()
hook_net_2.reset()
vids = vids.to(device)
attn_1 = extract_grad_cam(net1, vids, device, hook_net_1)
attn_2 = extract_grad_cam(net2, vids, device, hook_net_2)
B, N_VIEWS = attn_1.shape[0], attn_1.shape[1]
for idx_b in range(B):
for idx_v in range(N_VIEWS):
el_img = vids[idx_b, idx_v]
el_attn_1 = attn_1[idx_b, idx_v]
el_attn_2 = attn_2[idx_b, idx_v]
el_img = el_img.cpu().numpy().transpose(1, 2, 0)
el_attn_1 = el_attn_1.cpu().numpy()
el_attn_2 = el_attn_2.cpu().numpy()
mean, var = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
el_img = (el_img * var) + mean
el_img = np.clip(el_img, 0, 1)
el_attn_1 = cv2.blur(el_attn_1, (3, 3))
el_attn_1 = cv2.resize(el_attn_1,
(el_img.shape[1], el_img.shape[0]),
interpolation=cv2.INTER_CUBIC)
el_attn_2 = cv2.blur(el_attn_2, (3, 3))
el_attn_2 = cv2.resize(el_attn_2,
(el_img.shape[1], el_img.shape[0]),
interpolation=cv2.INTER_CUBIC)
save_img(el_img, el_attn_1, net1_path / f'{dst_idx}.png')
save_img(el_img, el_attn_2, net2_path / f'{dst_idx}.png')
save_img(el_img, None, orig_path / f'{dst_idx}.png')
save_img(np.concatenate([el_img, el_img], 1),
np.concatenate([el_attn_1, el_attn_2], 1),
both_path / f'{dst_idx}.png')
dst_idx += 1
self._gen += 1
return student_net
if __name__ == '__main__':
conf = Conf()
device = conf.get_device()
args = parse(conf)
conf.suppress_random(set_determinism=args.set_determinism)
train_loader, query_loader, gallery_loader, queryimg_loader, galleryimg_loader = \
get_dataloaders(args.dataset_name, conf.nas_path, device, args)
teacher_net: TriNet = Saver.load_net(args.teacher,
args.teacher_chk_name, args.dataset_name).to(device)
student_net: TriNet = deepcopy(teacher_net) if args.student is None \
else Saver.load_net(args.student, args.student_chk_name, args.dataset_name)
student_net = student_net.to(device)
ev = Evaluator(student_net, query_loader, gallery_loader, queryimg_loader, galleryimg_loader,
DATA_CONFS[args.dataset_name], device)
print('v' * 100)
ev.eval(saver=None, iteration=None, verbose=True, do_tb=False)
print('v' * 100)
student_net.reinit_layers(args.reinit_l4, args.reinit_l3)
saver = Saver(conf.log_path, args.exp_name)
