def visualize_attn(args):
"""
Visualization of learned attention map.
"""
config = CONFIGS[args.model_type]
num_classes = 10 if args.dataset == "cifar10" else 100
model = VisionTransformer(config, args.img_size,
norm_type=args.norm_type,
zero_head=True,
num_classes=num_classes,
vis=True)
ckpt_file = os.path.join(args.output_dir, args.name + "_checkpoint.bin")
ckpt = torch.load(ckpt_file)
# use single card for visualize attn map
model.load_state_dict(ckpt)
model.to(args.device)
model.eval()
_, test_loader = get_loader(args)
sample_idx = 0
layer_ids = [0, 3, 6, 9]
head_id = 0
with torch.no_grad():
for step, batch in enumerate(test_loader):
batch = tuple(t.to(args.device) for t in batch)
x, y = batch
select_x = x[sample_idx].unsqueeze(0)
output, attn_weights = model(select_x)
# attn_weights is List[(1, number_of_head, len_h, len_h)]
for layer_id in layer_ids:
vis_attn(args, attn_weights[layer_id].squeeze(0)[head_id], layer_id=layer_id)
break # visualize the first sample in the first batch
print("done.")
exit(0)
imagenet_labels = dict(
enumerate(open('attention_data/ilsvrc2012_wordnet_lemmas.txt')))
img_url = "https://images.mypetlife.co.kr/content/uploads/2019/04/09192811/welsh-corgi-1581119_960_720.jpg"
urlretrieve(img_url, "attention_data/img.jpg")
# Prepare Model
config = CONFIGS["ViT-B_16"]
model = VisionTransformer(config,
num_classes=1000,
zero_head=False,
img_size=224,
vis=True)
model.load_from(np.load("attention_data/ViT-B_16-224.npz"))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
im = Image.open("attention_data/img.jpg")
x = transform(im)
x.size()
# %%
logits, att_mat = model(x.unsqueeze(0))
att_mat = torch.stack(att_mat).squeeze(1)
# Average the attention weights across all heads.
