AugMix

class torchvision.transforms.AugMix(severity: int = 3, mixture_width: int = 3, chain_depth: int = -1, alpha: float = 1.0, all_ops: bool = True, interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>, fill: Optional[List[float]] = None)

AugMix data augmentation method based on “AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty”. 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

• 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]. 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 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.

Examples using AugMix:

Illustration of transforms

Illustration of transforms

forward(orig_img: torch.Tensor) → torch.Tensor

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

Returns

Transformed image.

Return type

PIL Image or Tensor