test_model = torchvision.models.densenet121(
pretrained='imagenet').cuda().eval()
success_attacks = 0
for idx, temp_image_name in enumerate(tqdm(images_name)):
total_samples = len(images_name)
ori_img_path = os.path.join(dataset_dir_ori, temp_image_name)
adv_img_path = os.path.join(dataset_dir_adv, temp_image_name)
image_ori_np = load_image(data_format='channels_first',
shape=(224, 224),
bounds=(0, 1),
abs_path=True,
fpath=ori_img_path)
image_ori_var = numpy_to_variable(image_ori_np)
gt_out = test_model(image_ori_var).detach().cpu().numpy()
gt_label = np.argmax(gt_out)
image_adv_np = load_image(data_format='channels_first',
shape=(224, 224),
bounds=(0, 1),
abs_path=True,
fpath=adv_img_path)
image_adv_var = numpy_to_variable(image_adv_np)
pd_out = test_model(image_adv_var).detach().cpu().numpy()
pd_label = np.argmax(pd_out)
linf = int(np.max(abs(image_ori_np - image_adv_np)) * 255)
print('linf: ', linf)
with open('labels.txt', 'r') as inf:
imagenet_dict = eval(inf.read())
model = Vgg16()
internal = [i for i in range(29)]
attack = DispersionAttack_opt(model, epsilon=16. / 255, steps=200)
image_np = load_image(data_format='channels_first', fname='example.png')
image_pil = Image.fromarray(
np.transpose((image_np * 255).astype(np.uint8), (1, 2, 0)))
image_pil.save(os.path.join("results", 'original.png'))
image_var = numpy_to_variable(image_np)
internal_logits_var, pred_nat_var = model.prediction(image_var,
internal=internal)
pred_nat = pred_nat_var.detach().cpu().numpy()
gt_label = np.argmax(pred_nat)
print(gt_label)
pred_cls = imagenet_dict[gt_label]
print(pred_cls)
'''
for layer_idx, intermediate_logit_var in enumerate(tqdm(internal_logits_var)):
intermediate_features = intermediate_logit_var[0].detach().cpu().numpy()
visualize_features(intermediate_features, output_dir='results', file_prefix='ori_{0:02d}_'.format(layer_idx), data_format='channels_first', only_first_channel=True)
'''
cuda = torch.device('cuda:0')
# x_image = Variable(torch.zeros(1, 3, 244, 244), requires_grad=True)
'''
image, label = foolbox.utils.imagenet_example(data_format='channels_first')
origin = np.transpose(image, (1, 2, 0)).astype(np.uint8)
Image.fromarray(origin).save('/home/yunhan/Documents/bh-asia/images/0000.png')
'''
image = cv2.imread("images/origin.jpg")
image = cv2.resize(image, (224, 224))
image = image[..., ::-1]
image = (np.transpose(image, (2, 0, 1))).astype(np.float32)
x_image = numpy_to_variable(image, cuda)
model = Vgg16()
k = 8
pred = model(x_image)[k]
loss = pred.std()
#loss = torch.norm(pred, 2)
loss.backward(retain_graph=True)
grad = x_image.grad.data
print(k, pred.shape, loss)
x_pert = x_image
for i in range(8, 8):