RandAugment

class torchvision.transforms.v2.RandAugment(num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31, interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, fill: Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Optional[Union[int, float, Sequence[int], Sequence[float]]]]] = None)

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

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:

• num_ops (int, optional) – Number of augmentation transformations to apply sequentially.

• magnitude (int, optional) – Magnitude for all the transformations.

• num_magnitude_bins (int, optional) – The number of different magnitude values.

• 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 RandAugment:

Illustration of transforms

Illustration of transforms

forward(*inputs: Any) → Any

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.