Resize

class torchvision.transforms.v2.Resize(size: Union[int, Sequence[int]], interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR, max_size: Optional[int] = None, antialias: Optional[Union[str, bool]] = 'warn')

[BETA] Resize the input to the given size.

Warning

The Resize transform is in Beta stage, and while we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes.

If the input is a torch.Tensor or a Datapoint (e.g. Image, Video, BoundingBox 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.

Warning

The output image might be different depending on its type: when downsampling, the interpolation of PIL images and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences in the performance of a network. Therefore, it is preferable to train and serve a model with the same input types. See also below the antialias parameter, which can help making the output of PIL images and tensors closer.

Parameters:

• size (sequence or int) –

  Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to this number. i.e, if height > width, then image will be rescaled to (size * height / width, size).

  Note

  In torchscript mode size as single int is not supported, use a sequence of length 1: [size, ].

• 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.

• max_size (int, optional) – The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater than max_size after being resized according to size, then the image is resized again so that the longer edge is equal to max_size. As a result, size might be overruled, i.e. the smaller edge may be shorter than size. This is only supported if size is an int (or a sequence of length 1 in torchscript mode).

• 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: 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 current default is None but will change to True in v0.17 for the PIL and Tensor backends to be consistent.