AutoAugment

class torchvision.transforms.v2.AutoAugment(policy: AutoAugmentPolicy = AutoAugmentPolicy.IMAGENET, interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, fill: Union[int, float, Sequence[int], Sequence[float], None, dict[Union[type, str], Union[int, float, collections.abc.Sequence[int], collections.abc.Sequence[float], NoneType]]] = None)

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

This transformation works on images and videos only.

If the input 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, optional) – Desired policy enum defined by torchvision.transforms.autoaugment.AutoAugmentPolicy. Default is AutoAugmentPolicy.IMAGENET.

• interpolation (InterpolationMode, optional) – 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(*inputs: Any) → Any

Do not override this! Use transform() instead.

static get_params(transform_num: int) → tuple[int, torch.Tensor, torch.Tensor]

Get parameters for autoaugment transformation

Returns:

params required by the autoaugment transformation