Minor updates in autoaugment, augment docstring v2 (#7317)

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

Author: vfdev-5

torchvision/transforms/v2/_augment.py

@@ -13,22 +13,22 @@
 
 
 class RandomErasing(_RandomApplyTransform):
-    """[BETA] Randomly selects a rectangle region in the input image or video and erases its pixels.
+    """[BETA] Randomly select a rectangle region in the input image or video and erase its pixels.
 
     .. betastatus:: RandomErasing transform
 
     This transform does not support PIL Image.
     'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/abs/1708.04896
 
     Args:
-         p: probability that the random erasing operation will be performed.
-         scale: range of proportion of erased area against input image.
-         ratio: range of aspect ratio of erased area.
-         value: erasing value. Default is 0. If a single int, it is used to
+        p (float, optional): probability that the random erasing operation will be performed.
+        scale (tuple of float, optional): range of proportion of erased area against input image.
+        ratio (tuple of float, optional): range of aspect ratio of erased area.
+        value (number or tuple of numbers): erasing value. Default is 0. If a single int, it is used to
             erase all pixels. If a tuple of length 3, it is used to erase
             R, G, B channels respectively.
             If a str of 'random', erasing each pixel with random values.
-         inplace: boolean to make this transform inplace. Default set to False.
+        inplace (bool, optional): boolean to make this transform inplace. Default set to False.
 
     Returns:
         Erased input.

torchvision/transforms/v2/_auto_augment.py

@@ -167,14 +167,16 @@ class AutoAugment(_AutoAugmentBase):
 
     .. betastatus:: AutoAugment transform
 
-    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    This transformation works on images and videos only.
+
+    If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
     If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
     Args:
-        policy (AutoAugmentPolicy): Desired policy enum defined by
+        policy (AutoAugmentPolicy, optional): Desired policy enum defined by
             :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
+        interpolation (InterpolationMode, optional): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
@@ -342,15 +344,17 @@ class RandAugment(_AutoAugmentBase):
 
     .. betastatus:: RandAugment transform
 
-    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    This transformation works on images and videos only.
+
+    If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
     If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
     Args:
-        num_ops (int): Number of augmentation transformations to apply sequentially.
-        magnitude (int): Magnitude for all the transformations.
-        num_magnitude_bins (int): The number of different magnitude values.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
+        num_ops (int, optional): Number of augmentation transformations to apply sequentially.
+        magnitude (int, optional): Magnitude for all the transformations.
+        num_magnitude_bins (int, optional): The number of different magnitude values.
+        interpolation (InterpolationMode, optional): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
@@ -423,13 +427,15 @@ class TrivialAugmentWide(_AutoAugmentBase):
 
     .. betastatus:: TrivialAugmentWide transform
 
-    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    This transformation works on images and videos only.
+
+    If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
     If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
     Args:
-        num_magnitude_bins (int): The number of different magnitude values.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
+        num_magnitude_bins (int, optional): The number of different magnitude values.
+        interpolation (InterpolationMode, optional): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed
@@ -492,18 +498,20 @@ class AugMix(_AutoAugmentBase):
 
     .. betastatus:: AugMix transform
 
-    If the image is torch Tensor, it should be of type torch.uint8, and it is expected
+    This transformation works on images and videos only.
+
+    If the input is :class:`torch.Tensor`, it should be of type ``torch.uint8``, and it is expected
     to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
     If img is PIL Image, it is expected to be in mode "L" or "RGB".
 
     Args:
-        severity (int): The severity of base augmentation operators. Default is ``3``.
-        mixture_width (int): The number of augmentation chains. Default is ``3``.
-        chain_depth (int): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3].
+        severity (int, optional): The severity of base augmentation operators. Default is ``3``.
+        mixture_width (int, optional): The number of augmentation chains. Default is ``3``.
+        chain_depth (int, optional): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3].
             Default is ``-1``.
-        alpha (float): The hyperparameter for the probability distributions. Default is ``1.0``.
-        all_ops (bool): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``.
-        interpolation (InterpolationMode): Desired interpolation enum defined by
+        alpha (float, optional): The hyperparameter for the probability distributions. Default is ``1.0``.
+        all_ops (bool, optional): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``.
+        interpolation (InterpolationMode, optional): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
         fill (sequence or number, optional): Pixel fill value for the area outside the transformed