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[.Resampling].NEAREST) are still accepted, but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.

Examples using RandomResizedCrop:

Illustration of transforms

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