CI updates for torch==2.5.0

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py

@@ -567,7 +567,7 @@ class only supports targeted attack.
                 if decoded_output[local_batch_size_idx] == y[local_batch_size_idx]:
                     if loss_2nd_stage[local_batch_size_idx] < best_loss_2nd_stage[local_batch_size_idx]:
                         # Update the best loss at 2nd stage
-                        best_loss_2nd_stage[local_batch_size_idx] = (
+                        best_loss_2nd_stage[local_batch_size_idx] = (  # type: ignore
                             loss_2nd_stage[local_batch_size_idx].detach().cpu().numpy()
                         )
 
@@ -734,7 +734,7 @@ def _compute_masking_threshold(self, x: np.ndarray) -> tuple[np.ndarray, np.ndar
 
         theta_array = np.array(theta)
 
-        return theta_array, original_max_psd
+        return theta_array, original_max_psd  # type: ignore
 
     def _psd_transform(self, delta: "torch.Tensor", original_max_psd: np.ndarray) -> "torch.Tensor":
         """

art/attacks/evasion/saliency_map.py

@@ -88,7 +88,7 @@ def generate(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> np.n
 
         # Initialize variables
         dims = list(x.shape[1:])
-        self._nb_features = np.product(dims)
+        self._nb_features = np.prod(dims)
         x_adv = np.reshape(x.astype(ART_NUMPY_DTYPE), (-1, self._nb_features))
         preds = np.argmax(self.estimator.predict(x, batch_size=self.batch_size), axis=1)
 

art/estimators/classification/pytorch.py

@@ -855,6 +855,8 @@ def loss_gradient(
         else:
             loss.backward()
 
+        grads: torch.Tensor | np.ndarray
+
         if x_grad.grad is not None:
             if isinstance(x, torch.Tensor):
                 grads = x_grad.grad

art/estimators/object_detection/pytorch_object_detector.py

@@ -327,7 +327,7 @@ def _get_losses(
 
     def loss_gradient(
         self, x: np.ndarray | "torch.Tensor", y: list[dict[str, np.ndarray | "torch.Tensor"]], **kwargs
-    ) -> np.ndarray:
+    ) -> np.ndarray | "torch.Tensor":
         """
         Compute the gradient of the loss function w.r.t. `x`.
 
@@ -359,6 +359,8 @@ def loss_gradient(
         # Compute gradients
         loss.backward(retain_graph=True)  # type: ignore
 
+        grads: torch.Tensor | np.ndarray
+
         if x_grad.grad is not None:
             if isinstance(x, np.ndarray):
                 grads = x_grad.grad.cpu().numpy()
@@ -376,7 +378,8 @@ def loss_gradient(
         if not self.channels_first:
             if isinstance(x, np.ndarray):
                 grads = np.transpose(grads, (0, 2, 3, 1))
-            else:
+            elif isinstance(grads, torch.Tensor):
+                # grads_tensor: torch.Tensor = grads
                 grads = torch.permute(grads, (0, 2, 3, 1))
 
         assert grads.shape == x.shape

art/estimators/regression/pytorch.py

@@ -682,6 +682,8 @@ def loss_gradient(
         else:
             loss.backward()
 
+        grads: torch.Tensor | np.ndarray
+
         if x_grad.grad is not None:
             if isinstance(x, torch.Tensor):
                 grads = x_grad.grad