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# RandAugment

class torchvision.transforms.RandAugment(num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31, interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None)

  RandAugment data augmentation method based on “RandAugment: Practical automated data augmentation with a reduced search space”. 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”.

  Parameters:

    - 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 `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 image. If given a number, the value is used for all bands respectively.

  forward(img: Tensor) → Tensor

    > img (PIL Image or Tensor): Image to be transformed.

    Returns:

      Transformed image.

    Return type:

      PIL Image or Tensor

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