help='batch size, e.g. 16, 32, 64...', type=int)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
gpu_ids = args.gpu_id
if isinstance(gpu_ids, int):
gpu_ids = [gpu_ids]
batch_size = args.batch_size
target_model = args.target_model
#inputDir = args.input_dir
outputFile = args.output_file
################## Defense #######################
m = wide_resnet(num_classes=10, depth=28, widen_factor=10,
dropRate=0.3) # densenet_cifar(num_classes=110)##densenet121(num_classes=110)#wide_resnet(num_classes=110, depth=28, widen_factor=10, dropRate=0.3) ######
m2 = wide_resnet2(num_classes=10, depth=28, widen_factor=10,
dropRate=0.3) # densenet_cifar(num_classes=110)##densenet121(num_classes=110)#wide_resnet(num_classes=110, depth=28, widen_factor=10, dropRate=0.3) ######
# Loading data for ...densenet161(num_classes=110)
image_mean = torch.tensor([0.491, 0.482, 0.447]).view(1, 3, 1, 1)
image_std = torch.tensor([0.247, 0.243, 0.262]).view(1, 3, 1, 1)
device = torch.device('cuda:0' if (torch.cuda.is_available()) else 'cpu') # torch.device('cpu')
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(device)
model2 = NormalizedModel(model=m2, mean=image_mean, std=image_std).to(device)
#model = model.to(device)
print('loading data for defense using %s ....' % target_model)
test_loader = load_data_for_defense()['dev_data']
weight_norm = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2Norm_cifar10_ep_184_val_acc0.9515.pth'
weight_AT = '/media/unknown/Data/PLP/fast_adv/defenses/weights/cifar10_AT/cifar10acc0.8709999859333039_45.pth'
shuffle=True,
num_workers=args.workers,
drop_last=True,
pin_memory=True)
val_loader = data.DataLoader(val_set,
batch_size=100,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_loader = data.DataLoader(test_set,
batch_size=100,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
m = wide_resnet(num_classes=10, depth=28, widen_factor=10, dropRate=args.drop)
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(
DEVICE) # keep images in the [0, 1] range
#model_dict = torch.load('/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_1Attention_cifar10_ep_33_val_acc0.8890.pth')
model_file = 'weights/best/2AT_cifar10_ep_13_val_acc0.8770.pth'
#'/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_1Attention_cifar10_ep_33_val_acc0.8890.pth'
#'/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_2AT_cifar10_ep_29_val_acc0.8870.pth'
model_dict = model.state_dict()
save_model = torch.load(model_file, map_location=lambda storage, loc: storage)
state_dict = {k: v for k, v in save_model.items() if k in model_dict.keys()}
#print(state_dict.keys()) # dict_keys(['w', 'conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias'])
model_dict.update(state_dict)
model.load_state_dict(model_dict)
#model_dict = torch.load('/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_2AT_cifar10_ep_29_val_acc0.8870.pth')