RandomShortestSize

class torchvision.transforms.v2.RandomShortestSize(min_size: Union[List[int], Tuple[int], int], max_size: Optional[int] = None, interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, antialias: Optional[bool] = True)

Randomly resize the input.

If the input is a torch.Tensor or a TVTensor (e.g. Image, Video, BoundingBoxes etc.) it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape. A bounding box can have [..., 4] shape.

Parameters:

• min_size (int or sequence of python:int) – Minimum spatial size. Single integer value or a sequence of integer values.

• max_size (int, optional) – Maximum spatial size. Default, None.

• interpolation (InterpolationMode, optional) – Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Default is InterpolationMode.BILINEAR. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.NEAREST_EXACT, InterpolationMode.BILINEAR and InterpolationMode.BICUBIC are supported. The corresponding Pillow integer constants, e.g. PIL.Image.BILINEAR are accepted as well.

• antialias (bool, optional) –

  Whether to apply antialiasing. It only affects tensors with bilinear or bicubic modes and it is ignored otherwise: on PIL images, antialiasing is always applied on bilinear or bicubic modes; on other modes (for PIL images and tensors), antialiasing makes no sense and this parameter is ignored. Possible values are:

  • True (default): will apply antialiasing for bilinear or bicubic modes. Other mode aren’t affected. This is probably what you want to use.

  • False: will not apply antialiasing for tensors on any mode. PIL images are still antialiased on bilinear or bicubic modes, because PIL doesn’t support no antialias.

  • None: equivalent to False for tensors and True for PIL images. This value exists for legacy reasons and you probably don’t want to use it unless you really know what you are doing.

  The default value changed from None to True in v0.17, for the PIL and Tensor backends to be consistent.

make_params(flat_inputs: List[Any]) → Dict[str, Any]

Method to override for custom transforms.

See How to write your own v2 transforms

transform(inpt: Any, params: Dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms