RandomResizedCrop

class torchvision.transforms.RandomResizedCrop(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: 'bilinear'>)

Crop a random portion of image and resize it to a given size.

If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions

A crop of the original image is made: the crop has a random area (H * W) and a random aspect ratio. This crop is finally resized to the given size. This is popularly used to train the Inception networks.

Parameters

• size (int or sequence) –

  expected output size of the crop, for each edge. If size is an int instead of sequence like (h, w), a square output size (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).

  Note

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

• scale (tuple of python:float) – Specifies the lower and upper bounds for the random area of the crop, before resizing. The scale is defined with respect to the area of the original image.

• ratio (tuple of python:float) – lower and upper bounds for the random aspect ratio of the crop, before resizing.

• interpolation (InterpolationMode) – Desired interpolation enum defined by torchvision.transforms.InterpolationMode. Default is InterpolationMode.BILINEAR. If input is Tensor, only InterpolationMode.NEAREST, InterpolationMode.BILINEAR and InterpolationMode.BICUBIC are supported. For backward compatibility integer values (e.g. PIL.Image.NEAREST) are still acceptable.

Examples using RandomResizedCrop:

Illustration of transforms

forward(img)

Parameters

img (PIL Image or Tensor) – Image to be cropped and resized.

Returns

Randomly cropped and resized image.

Return type

PIL Image or Tensor

static get_params(img: torch.Tensor, scale: List[float], ratio: List[float]) → Tuple[int, int, int, int]

Get parameters for crop for a random sized crop.

Parameters

• img (PIL Image or Tensor) – Input image.

• scale (list) – range of scale of the origin size cropped

• ratio (list) – range of aspect ratio of the origin aspect ratio cropped

Returns

params (i, j, h, w) to be passed to crop for a random sized crop.

Return type

tuple