Fix latest torch and torchvision compatibility (#2548)

* disable weights only loading

* add transform wrapper method

src/anomalib/data/transforms/center_crop.py

@@ -142,3 +142,17 @@ def _transform(self, inpt: torch.Tensor, params: dict[str, Any]) -> torch.Tensor
         """
         del params
         return center_crop_image(inpt, output_size=self.size)
+
+    def transform(self, inpt: torch.Tensor, params: dict[str, Any]) -> torch.Tensor:
+        """Wrapper for self._transform.
+
+        This is to ensure compatibility with Torchvision 2.6+, where the `_transform` method was renamed to `transform`.
+
+        Args:
+            inpt (torch.Tensor): Input tensor to transform
+            params (dict[str, Any]): Transform parameters (unused)
+
+        Returns:
+            torch.Tensor: Center-cropped output tensor
+        """
+        return self._transform(inpt, params)

src/anomalib/deploy/inferencers/torch_inferencer.py

@@ -129,7 +129,7 @@ def _load_checkpoint(self, path: str | Path) -> dict:
             msg = f"Unknown PyTorch checkpoint format {path.suffix}. Make sure you save the PyTorch model."
             raise ValueError(msg)
 
-        return torch.load(path, map_location=self.device)
+        return torch.load(path, map_location=self.device, weights_only=False)
 
     def load_model(self, path: str | Path) -> nn.Module:
         """Load the PyTorch model.