TrivialAugmentWide

class torchvision.transforms.v2.TrivialAugmentWide(num_magnitude_bins: int = 31, interpolation: Union[str, InterpolationMode, int] = '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)

Dataset-independent data-augmentation with TrivialAugment Wide, as described in “TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation”.

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

• interpolation (str or InterpolationMode, optional) – Desired interpolation enum defined by torchvision.transforms.v2.InterpolationMode. Accepted string values are "nearest", "nearest-exact", "bilinear", "bicubic", "box", "hamming", and "lanczos". "box", "hamming", and "lanczos" are only supported for PIL images. The corresponding InterpolationMode enum values and Pillow integer constants, e.g. PIL.Image.BILINEAR are accepted as well.

• 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 TrivialAugmentWide:

Illustration of transforms

Illustration of transforms

forward(*inputs: Any) → Any

Do not override this! Use transform() instead.