parser.add_argument('--start_index', '-st', default=0, type=int)
parser.add_argument('--batch_size', default=1000, type=int)
parser.add_argument('--out_dir', default='../results/tar_adv_img/')
parser.add_argument('--defense', default=0, type=int)
args = parser.parse_known_args()[0]
imgPath = '../results/tar_adv_img/' # 加载原图
pretrained_path = '../results/adv_border_chk/'
out_dir = args.out_dir #保存目标攻击defense or 未经防御的对抗样本
os.makedirs(out_dir, exist_ok=True)
# Use GPUs
device = torch.device("cpu")
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
data_loader = ImageNet.get_data_loaders(data_path=imgPath,
batch_size=1,
num_workers=0,
normalize=False)
classifier = ImageNet.get_classifier().to(device)
classifier.eval()
chk_name = os.listdir(pretrained_path)
chk_name.sort() # necessary in linux
img_names = os.listdir('../results/adv_border_png/')
img_names.sort()
cnt = 0
cnt_effect = 0
cnt_total = 0
cnt_temp = 0
for i, (input, target) in enumerate(data_loader):
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Draw examples of attacked ImageNet examples')
parser.add_argument('--framing',
required=True,
help='Path to pretrained framing')
parser.add_argument('--output',
'-o',
default='examples.png',
help='Output file')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
_, data_loader = ImageNet.get_data_loaders(batch_size=BATCH_SIZE,
num_workers=0,
normalize=False,
shuffle_val=True)
framing = load_pretrained_imagenet_framing(args.framing).to(device)
classifier = resnet50(pretrained=True).to(device)
classifier.eval()
input, target = next(iter(data_loader))
input = input.to(device)
with torch.no_grad():
input_att, _ = framing(input, normalize=False)
normalized_input = input.clone()
normalized_input_att = input_att.clone()
for id in range(BATCH_SIZE):
