关于main_test.py文件运行中提示FGSM，PGD对象没有set_training_mode属性的问题

[bluefier]

你好，在运行main_test.py时，我遇到如下报错

通过查看代码，发现FGSM和PGD类定义时，并没有set_training_mode属性，具体定义代码如下：

  class FGSM(Attack):

      def __init__(self, model, forward_function=None, eps=0.007, T=None, **kwargs):
          super().__init__("FGSM", model)
          self.eps = eps
          self._supported_mode = ['default', 'targeted']
          self.forward_function = forward_function
          self.T = T

      def forward(self, images, labels):
          r"""
          Overridden.
          """
          # images.unsqueeze_(1)
          # images = images.repeat(8, 1, 1, 1, 1)
          images = images.clone().detach().to(self.device)

          labels = labels.clone().detach().to(self.device)

          if self._targeted:
              target_labels = self._get_target_label(images, labels)

          loss = nn.CrossEntropyLoss()

          images.requires_grad = True
          if self.forward_function is not None:
              outputs = self.forward_function(self.model, images, self.T)
          else:
              outputs = self.model(images)

          # Calculate loss
          if self._targeted:
              cost = -loss(outputs, target_labels)
          else:
              cost = loss(outputs, labels)

          # Update adversarial images
          grad = torch.autograd.grad(cost, images, retain_graph=False, create_graph=False)[0]

          adv_images = images + self.eps*grad.sign()
          adv_images = torch.clamp(adv_images, min=0, max=1).detach()

          return adv_images

  class PGD(Attack):

      def __init__(self, model, forward_function=None, eps=0.3,
                   alpha=2/255, steps=40, random_start=True, T=None, **kwargs):
          super().__init__("PGD", model)
          self.eps = eps
          self.alpha = alpha
          self.steps = steps
          self.random_start = random_start
          self._supported_mode = ['default', 'targeted']
          self.forward_function = forward_function
          self.T = T

      def forward(self, images, labels):
          r"""
          Overridden.
          """

          images = images.clone().detach().to(self.device)
          labels = labels.clone().detach().to(self.device)

          if self._targeted:
              target_labels = self._get_target_label(images, labels)

          loss = nn.CrossEntropyLoss()

          adv_images = images.clone().detach()

          if self.random_start:
              # Starting at a uniformly random point
              adv_images = adv_images + torch.empty_like(adv_images).uniform_(-self.eps, self.eps)
              adv_images = torch.clamp(adv_images, min=0, max=1).detach()

          for _ in range(self.steps):
              adv_images.requires_grad = True
              if self.forward_function is not None:
                  outputs = self.forward_function(self.model, adv_images, self.T)
              else:
                  outputs = self.model(adv_images)

              # Calculate loss
              if self._targeted:
                  cost = -loss(outputs, target_labels)
              else:
                  cost = loss(outputs, labels)

              # Update adversarial images
              grad = torch.autograd.grad(cost, adv_images,
                                         retain_graph=False, create_graph=False)[0]

              adv_images = adv_images.detach() + self.alpha*grad.sign()
              delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps)
              adv_images = torch.clamp(images + delta, min=0, max=1).detach()

          return adv_images

请问是否是由于部分代码遗失导致的呢？请问该如何解决呢？

[putshua]

torchattacks版本导致