|
|
import argparse
|
|
|
from torch.utils.data import DataLoader
|
|
|
from utils_ import *
|
|
|
|
|
|
parser = argparse.ArgumentParser(description='Clip-based Generative Networks')
|
|
|
parser.add_argument('--test_dir', default='', help='Testing Data')
|
|
|
parser.add_argument('--batch_size', type=int, default=10, help='Batch Size')
|
|
|
parser.add_argument('--model_t',type=str, default= 'all', help ='Model under attack : vgg16, vgg19, ..., dense121')
|
|
|
parser.add_argument('--label_flag', type=str, default='N8', help='Label nums: N8, C20, C50, ...')
|
|
|
parser.add_argument('--finetune', action='store_true', help='Finetune for single class attack')
|
|
|
parser.add_argument('--finetune_class', type=int, help='Class id to be finetuned')
|
|
|
args = parser.parse_args()
|
|
|
print(args)
|
|
|
|
|
|
n_class = 1000
|
|
|
|
|
|
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
|
|
|
print(device)
|
|
|
|
|
|
dic = dict()
|
|
|
|
|
|
if args.model_t == 'all':
|
|
|
model_name_list = ['vgg16', 'googlenet', 'incv3', 'res152', 'dense121', 'incv4', 'inc_res_v2', 'adv_incv3', 'ens_inc_res_v2', 'res50_sin', 'res50_sin_in', 'res50_sin_fine_in']
|
|
|
elif args.model_t == 'robust':
|
|
|
model_name_list = ['adv_incv3', 'ens_inc_res_v2', 'res50_sin', 'res50_sin_in', 'res50_sin_fine_in']
|
|
|
elif args.model_t == 'normal':
|
|
|
model_name_list = ['vgg16', 'googlenet', 'incv3', 'res152', 'dense121', 'incv4', 'inc_res_v2']
|
|
|
else:
|
|
|
model_name_list = [args.model_t]
|
|
|
|
|
|
for model_name in model_name_list:
|
|
|
model_t = load_model(model_name)
|
|
|
|
|
|
model_t = model_t.to(device)
|
|
|
model_t.eval()
|
|
|
|
|
|
|
|
|
if model_name in ['incv3', 'incv4', 'inc_res_v2', 'adv_incv3', 'ens_inc_res_v2']:
|
|
|
img_size = 299
|
|
|
else:
|
|
|
img_size = 224
|
|
|
|
|
|
|
|
|
data_transform = transforms.Compose([
|
|
|
transforms.Resize(img_size),
|
|
|
transforms.ToTensor(),
|
|
|
])
|
|
|
|
|
|
if args.finetune:
|
|
|
class_ids = np.array([args.finetune_class])
|
|
|
else:
|
|
|
class_ids = get_classes(args.label_flag)
|
|
|
|
|
|
|
|
|
sr = np.zeros(len(class_ids))
|
|
|
for idx in range(len(class_ids)):
|
|
|
test_dir = '{}_t{}'.format(args.test_dir, class_ids[idx])
|
|
|
|
|
|
target_acc = 0.
|
|
|
target_test_size = 0.
|
|
|
test_set = datasets.ImageFolder(test_dir, data_transform)
|
|
|
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=4,
|
|
|
pin_memory=True)
|
|
|
for i, (img, _) in enumerate(test_loader):
|
|
|
img = img.to(device)
|
|
|
adv_out = model_t(normalize(img.clone().detach()))
|
|
|
target_acc += torch.sum(adv_out.argmax(dim=-1) == (class_ids[idx])).item()
|
|
|
target_test_size += img.size(0)
|
|
|
sr[idx] = target_acc / target_test_size
|
|
|
print('sr: {}'.format(sr))
|
|
|
print('model:{} \t target acc:{:.2%}\t target_test_size:{}'.format(model_name, sr.mean(), target_test_size))
|
|
|
dic[model_name] = sr.mean() * 100
|
|
|
print(dic)
|
|
|
|
