Adding docs for RandAugment

docs/source/transforms.rst

@@ -214,8 +214,8 @@ Generic Transforms
     :members:
 
 
-AutoAugment Transforms
-----------------------
+Automatic Augmentation Transforms
+---------------------------------
 
 `AutoAugment <https://arxiv.org/pdf/1805.09501.pdf>`_ is a common Data Augmentation technique that can improve the accuracy of Image Classification models.
 Though the data augmentation policies are directly linked to their trained dataset, empirical studies show that
@@ -229,6 +229,10 @@ The new transform can be used standalone or mixed-and-matched with existing tran
 .. autoclass:: AutoAugment
     :members:
 
+`RandAugment <https://arxiv.org/abs/1909.13719>`_ is a simple high-performing Data Augmentation technique which improves the accuracy of Image Classification models.
+
+.. autoclass:: RandAugment
+    :members:
 
 .. _functional_transforms:
 

gallery/plot_transforms.py

@@ -245,6 +245,14 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 row_title = [str(policy).split('.')[-1] for policy in policies]
 plot(imgs, row_title=row_title)
 
+####################################
+# RandAugment
+# ~~~~~~~~~~~
+# The :class:`~torchvision.transforms.RandAugment` transform automatically augments the data.
+augmenter = T.RandAugment()
+imgs = [augmenter(orig_img) for _ in range(4)]
+plot(imgs)
+
 ####################################
 # Randomly-applied transforms
 # ---------------------------

torchvision/transforms/autoaugment.py

@@ -245,7 +245,7 @@ def __repr__(self) -> str:
 class RandAugment(torch.nn.Module):
     r"""RandAugment data augmentation method based on
     `"RandAugment: Practical automated data augmentation with a reduced search space"
-    <https://arxiv.org/abs/1909.13719>`.
+    <https://arxiv.org/abs/1909.13719>`_.
     If the image is 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".
@@ -293,6 +293,7 @@ def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str,
     def forward(self, img: Tensor) -> Tensor:
         """
             img (PIL Image or Tensor): Image to be transformed.
+
         Returns:
             PIL Image or Tensor: Transformed image.
         """