AutoAugment

class torchvision.transforms.AutoAugment(policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None)

AutoAugment data augmentation method based on “AutoAugment: Learning Augmentation Strategies from Data”. 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:

• policy (AutoAugmentPolicy) – Desired policy enum defined by torchvision.transforms.autoaugment.AutoAugmentPolicy. Default is AutoAugmentPolicy.IMAGENET.

• 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 AutoAugment:

Illustration of transforms

Illustration of transforms

forward(img: Tensor) → Tensor

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

Returns:

AutoAugmented image.

Return type:

PIL Image or Tensor

static get_params(transform_num: int) → Tuple[int, Tensor, Tensor]

Get parameters for autoaugment transformation

Returns:

params required by the autoaugment transformation