Merge branch 'dev_1.11.1' into random-sphere-clean

Author: beat-buesser

art/attacks/evasion/adversarial_patch/adversarial_patch.py

@@ -164,9 +164,6 @@ def generate(  # type: ignore
         """
         logger.info("Creating adversarial patch.")
 
-        if y is None:  # pragma: no cover
-            raise ValueError("Adversarial Patch attack requires target values `y`.")
-
         if len(x.shape) == 2:  # pragma: no cover
             raise ValueError(
                 "Feature vectors detected. The adversarial patch can only be applied to data with spatial "

art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py

@@ -682,13 +682,13 @@ def apply_patch(
         if mask is not None:
             mask = mask.copy()
         mask = self._check_mask(mask=mask, x=x)
-        x_tensor = torch.Tensor(x)
+        x_tensor = torch.Tensor(x).to(self.estimator.device)
         if mask is not None:
-            mask_tensor = torch.Tensor(mask)
+            mask_tensor = torch.Tensor(mask).to(self.estimator.device)
         else:
             mask_tensor = None
         if isinstance(patch_external, np.ndarray):
-            patch_tensor = torch.Tensor(patch_external)
+            patch_tensor = torch.Tensor(patch_external).to(self.estimator.device)
         else:
             patch_tensor = self._patch
         return (

art/estimators/classification/pytorch.py

@@ -322,7 +322,7 @@ def predict(  # pylint: disable=W0221
             output = model_outputs[-1]
             output = output.detach().cpu().numpy().astype(np.float32)
             if len(output.shape) == 1:
-                output = np.expand_dims(output.detach().cpu().numpy(), axis=1).astype(np.float32)
+                output = np.expand_dims(output, axis=1).astype(np.float32)
 
             results_list.append(output)