- 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)¶
[BETA] RandAugment data augmentation method based on “RandAugment: Practical automated data augmentation with a reduced search space”.
The RandAugment transform is in Beta stage, and while we do not expect disruptive breaking changes, some APIs may slightly change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753.
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”.
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
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.
RandAugment:Illustration of transforms
- forward(*inputs: Any) Any ¶
Defines the computation performed at every call.
Should be overridden by all subclasses.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.