()
| 22 | |
| 23 | |
| 24 | def parse_args(): |
| 25 | parser = argparse.ArgumentParser('argument for training') |
| 26 | |
| 27 | parser.add_argument('--batch-size', type=int, default=512, |
| 28 | help='batch_size') |
| 29 | parser.add_argument('--num-workers', type=int, default=10, |
| 30 | help='num of workers to use') |
| 31 | |
| 32 | parser.add_argument('--training_type', type=str, default="Normal", |
| 33 | help='Normal, LabelSmoothing, AdvReg, DP, MixupMMD, PATE') |
| 34 | parser.add_argument('--mode', type=str, default="shadow", |
| 35 | help='target, shadow') |
| 36 | |
| 37 | parser.add_argument('--epochs', type=int, default=100, |
| 38 | help='number of training epochs') |
| 39 | parser.add_argument('--gpu', type=int, default=0, |
| 40 | help='gpu index used for training') |
| 41 | |
| 42 | # model dataset |
| 43 | parser.add_argument('--model', type=str, default='resnet18') |
| 44 | parser.add_argument('--load-pretrained', type=str, default='no') |
| 45 | parser.add_argument('--task', type=str, default='mia', |
| 46 | help='specify the attack task, mia or ol') |
| 47 | parser.add_argument('--dataset', type=str, default='CIFAR10', |
| 48 | help='dataset') |
| 49 | parser.add_argument('--num-class', type=int, default=10, |
| 50 | help='number of classes') |
| 51 | parser.add_argument('--inference-dataset', type=str, default='CIFAR10', |
| 52 | help='if yes, load pretrained the attack model to inference') |
| 53 | parser.add_argument('--data-path', type=str, default='../datasets/', |
| 54 | help='data_path') |
| 55 | parser.add_argument('--input-shape', type=str, default="32,32,3", |
| 56 | help='comma delimited input shape input') |
| 57 | parser.add_argument('--log_path', type=str, |
| 58 | default='./save', help='data_path') |
| 59 | |
| 60 | args = parser.parse_args() |
| 61 | |
| 62 | args.input_shape = [int(item) for item in args.input_shape.split(',')] |
| 63 | args.device = 'cuda:%d' % args.gpu if torch.cuda.is_available() else 'cpu' |
| 64 | |
| 65 | return args |
| 66 | |
| 67 | |
| 68 | def get_target_model(name="resnet18", num_classes=10): |
no outgoing calls
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