Make torchscript parameter constraints more obvious (#3693)

torchvision/transforms/functional.py

@@ -358,6 +358,8 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
             the smaller edge of the image will be matched to this number maintaining
             the aspect ratio. i.e, if height > width, then image will be rescaled to
             :math:`\left(\text{size} \times \frac{\text{height}}{\text{width}}, \text{size}\right)`.
+
+            .. note::
                 In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`.
@@ -413,7 +415,10 @@ def pad(img: Tensor, padding: List[int], fill: int = 0, padding_mode: str = "con
             is used to pad all borders. If sequence of length 2 is provided this is the padding
             on left/right and top/bottom respectively. If a sequence of length 4 is provided
             this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a sequence of length 1: ``[padding, ]``.
+
+            .. note::
+                In torchscript mode padding as single int is not supported, use a sequence of
+                length 1: ``[padding, ]``.
         fill (number or str or tuple): Pixel fill value for constant fill. Default is 0.
             If a tuple of length 3, it is used to fill R, G, B channels respectively.
             This value is only used when the padding_mode is constant.
@@ -608,9 +613,10 @@ def perspective(
             For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
+
+            .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
-            If input is PIL Image, the options is only available for ``Pillow>=5.0.0``.
 
     Returns:
         PIL Image or Tensor: transformed Image.
@@ -938,9 +944,10 @@ def rotate(
             Default is the center of the image.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
+
+            .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
-            If input is PIL Image, the options is only available for ``Pillow>=5.2.0``.
 
     Returns:
         PIL Image or Tensor: Rotated image.
@@ -1010,9 +1017,10 @@ def affine(
             For backward compatibility integer values (e.g. ``PIL.Image.NEAREST``) are still acceptable.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
             image. If given a number, the value is used for all bands respectively.
+
+            .. note::
                 In torchscript mode single int/float value is not supported, please use a sequence
                 of length 1: ``[value, ]``.
-            If input is PIL Image, the options is only available for ``Pillow>=5.0.0``.
         fillcolor (sequence, int, float): deprecated argument and will be removed since v0.10.0.
             Please use the ``fill`` parameter instead.
         resample (int, optional): deprecated argument and will be removed since v0.10.0.
@@ -1173,12 +1181,18 @@ def gaussian_blur(img: Tensor, kernel_size: List[int], sigma: Optional[List[floa
         img (PIL Image or Tensor): Image to be blurred
         kernel_size (sequence of ints or int): Gaussian kernel size. Can be a sequence of integers
             like ``(kx, ky)`` or a single integer for square kernels.
-            In torchscript mode kernel_size as single int is not supported, use a sequence of length 1: ``[ksize, ]``.
+
+            .. note::
+                In torchscript mode kernel_size as single int is not supported, use a sequence of
+                length 1: ``[ksize, ]``.
         sigma (sequence of floats or float, optional): Gaussian kernel standard deviation. Can be a
             sequence of floats like ``(sigma_x, sigma_y)`` or a single float to define the
             same sigma in both X/Y directions. If None, then it is computed using
             ``kernel_size`` as ``sigma = 0.3 * ((kernel_size - 1) * 0.5 - 1) + 0.8``.
-            Default, None. In torchscript mode sigma as single float is
+            Default, None.
+
+            .. note::
+                In torchscript mode sigma as single float is
                 not supported, use a sequence of length 1: ``[sigma, ]``.
 
     Returns:

torchvision/transforms/transforms.py

@@ -241,6 +241,8 @@ class Resize(torch.nn.Module):
             smaller edge of the image will be matched to this number.
             i.e, if height > width, then image will be rescaled to
             (size * height / width, size).
+
+            .. note::
                 In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
@@ -345,7 +347,10 @@ class Pad(torch.nn.Module):
             is used to pad all borders. If sequence of length 2 is provided this is the padding
             on left/right and top/bottom respectively. If a sequence of length 4 is provided
             this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a sequence of length 1: ``[padding, ]``.
+
+            .. note::
+                In torchscript mode padding as single int is not supported, use a sequence of
+                length 1: ``[padding, ]``.
         fill (number or str or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
             length 3, it is used to fill R, G, B channels respectively.
             This value is only used when the padding_mode is constant.
@@ -523,7 +528,10 @@ class RandomCrop(torch.nn.Module):
             is used to pad all borders. If sequence of length 2 is provided this is the padding
             on left/right and top/bottom respectively. If a sequence of length 4 is provided
             this is the padding for the left, top, right and bottom borders respectively.
-            In torchscript mode padding as single int is not supported, use a sequence of length 1: ``[padding, ]``.
+
+            .. note::
+                In torchscript mode padding as single int is not supported, use a sequence of
+                length 1: ``[padding, ]``.
         pad_if_needed (boolean): It will pad the image if smaller than the
             desired size to avoid raising an exception. Since cropping is done
             after padding, the padding seems to be done at a random offset.
@@ -792,6 +800,8 @@ class RandomResizedCrop(torch.nn.Module):
         size (int or sequence): expected output size of the crop, for each edge. If size is an
             int instead of sequence like (h, w), a square output size ``(size, size)`` is
             made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
+
+            .. note::
                 In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
         scale (tuple of float): Specifies the lower and upper bounds for the random area of the crop,
             before resizing. The scale is defined with respect to the area of the original image.