liuyugeng / ml-doctor Goto Github PK

ML-Doctor/demoloader/dataloader.py", line 73, in __getitem__
    gender = int(attrs[1])
ValueError: invalid literal for int() with base 10: ''

ImportError: cannot import name 'module_modification' from 'opacus.utils'

Traceback (most recent call last):
  File "demo.py", line 250, in <module>
    main()
  File "demo.py", line 227, in main
    test_meminf(TARGET_PATH, device, num_classes, target_train, target_test, shadow_train, shadow_test, target_model, shadow_model, train_shadow, use_DP, noise, norm, delta)
  File "demo.py", line 104, in test_meminf
    attack_mode0(PATH + "_target.pth", PATH + "_shadow.pth", PATH, device, attack_trainloader, attack_testloader, target_model, shadow_model, attack_model, 1, num_classes)
  File "/mnt/f/PYProject/doctor/meminf.py", line 748, in attack_mode0
    res_train = attack.train(flag, RESULT_PATH)
  File "/mnt/f/PYProject/doctor/meminf.py", line 289, in train
    results = self.attack_model(output, prediction)
  File "/home/ddq/miniconda3/envs/opacus/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/mnt/f/PYProject/utils/define_models.py", line 48, in forward
    Prediction_Component_result = self.Prediction_Component(prediction)
  File "/home/ddq/miniconda3/envs/opacus/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/ddq/miniconda3/envs/opacus/lib/python3.7/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/ddq/miniconda3/envs/opacus/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/ddq/miniconda3/envs/opacus/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 103, in forward
    return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x64 and 1x128)

elif dataset_name.lower() == "mnist":
      num_classes = 10
      transform = transforms.Compose([
          transforms.Resize((64, 64)),
          transforms.ToTensor(),
          transforms.Normalize((0.1307,), (0.3081,))
      ])

      train_set = torchvision.datasets.MNIST(
              root=root, train=True, download=True, transform=transform)
      test_set = torchvision.datasets.MNIST(
              root=root, train=False, download=True, transform=transform)

      dataset = train_set + test_set
      input_channel = 1