Make torchscript parameter constraints more obvious (#3693)

Author: NicolasHug

torchvision/transforms/functional.py

@@ -358,7 +358,9 @@ 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)`.
-            In torchscript mode size as single int is not supported, use a sequence of length 1: ``[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``. If input is Tensor, only ``InterpolationMode.NEAREST``,
@@ -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.
-            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``.
+
+            .. note::
+                In torchscript mode single int/float value is not supported, please use a sequence
+                of length 1: ``[value, ]``.
 
     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.
-            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``.
+
+            .. note::
+                In torchscript mode single int/float value is not supported, please use a sequence
+                of length 1: ``[value, ]``.
 
     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.
-            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``.
+
+            .. note::
+                In torchscript mode single int/float value is not supported, please use a sequence
+                of length 1: ``[value, ]``.
         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.
@@ -1173,13 +1181,19 @@ 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
-            not supported, use a sequence of length 1: ``[sigma, ]``.
+            Default, None.
+
+            .. note::
+                In torchscript mode sigma as single float is
+                not supported, use a sequence of length 1: ``[sigma, ]``.
 
     Returns:
         PIL Image or Tensor: Gaussian Blurred version of the image.

torchvision/transforms/transforms.py

@@ -241,7 +241,9 @@ 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).
-            In torchscript mode size as single int is not supported, use a sequence of length 1: ``[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``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` and
@@ -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.
@@ -792,7 +800,9 @@ 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]).
-            In torchscript mode size as single int is not supported, use a sequence of length 1: ``[size, ]``.
+
+            .. 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.
         ratio (tuple of float): lower and upper bounds for the random aspect ratio of the crop, before