AutoAugment

class torchaug.transforms.AutoAugment(policy=AutoAugmentPolicy.IMAGENET, interpolation=InterpolationMode.NEAREST, fill=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.

Parameters:

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

• interpolation (Union[InterpolationMode, int], optional) – Desired interpolation enum defined by torchvision.transforms.InterpolationMode. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.BILINEAR are supported. Default: InterpolationMode.NEAREST

• fill (Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Union[int, float, Sequence[int], Sequence[float], None]]], optional) – Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. Default: None

forward(*inputs)

Performs forward pass of the transform.

Parameters:

inputs (Any) – Inputs to the transform.

Return type:

Any

Returns:

Transformed inputs.