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[fbsync] Adding docs for RandAugment (#4349)
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Summary:
* Adding docs for RandAugment.

* Fix docs.

Reviewed By: fmassa

Differential Revision: D30793341

fbshipit-source-id: fb09e7eb0ef2b7c48ec7f948ec04a5d193761163
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datumbox authored and facebook-github-bot committed Sep 9, 2021
1 parent d306fad commit 2475f64
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8 changes: 6 additions & 2 deletions docs/source/transforms.rst
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Expand Up @@ -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
Expand All @@ -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:

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8 changes: 8 additions & 0 deletions gallery/plot_transforms.py
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Expand Up @@ -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
# ---------------------------
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3 changes: 2 additions & 1 deletion torchvision/transforms/autoaugment.py
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Expand Up @@ -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".
Expand Down Expand Up @@ -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.
"""
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