Mentioning the padding policy in transforms.GaussianBlur docs (#8246)

Co-authored-by: Nicolas Hug <[email protected]>
Co-authored-by: Nicolas Hug <[email protected]>

Author: talcs

torchvision/transforms/functional.py

@@ -1307,7 +1307,9 @@ def erase(img: Tensor, i: int, j: int, h: int, w: int, v: Tensor, inplace: bool
 
 
 def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Tensor:
-    """Performs Gaussian blurring on the image by given kernel.
+    """Performs Gaussian blurring on the image by given kernel
+
+    The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape.
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means at most one leading dimension.
 

torchvision/transforms/v2/_misc.py

@@ -166,7 +166,9 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
 
 
 class GaussianBlur(Transform):
-    """Blurs image with randomly chosen Gaussian blur.
+    """Blurs image with randomly chosen Gaussian blur kernel.
+
+    The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape.
 
     If the input is a Tensor, it is expected
     to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.