torchvision.transforms.functional.resized_crop(img: torch.Tensor, top: int, left: int, height: int, width: int, size: List[int], interpolation: torchvision.transforms.functional.InterpolationMode = <InterpolationMode.BILINEAR: 'bilinear'>)torch.Tensor[source]

Crop the given image and resize it to desired size. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions

Notably used in RandomResizedCrop.

  • img (PIL Image or Tensor) – Image to be cropped. (0,0) denotes the top left corner of the image.

  • top (int) – Vertical component of the top left corner of the crop box.

  • left (int) – Horizontal component of the top left corner of the crop box.

  • height (int) – Height of the crop box.

  • width (int) – Width of the crop box.

  • size (sequence or int) – Desired output size. Same semantics as resize.

  • 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.


Cropped image.

Return type

PIL Image or Tensor

Examples using resized_crop:

Illustration of transforms

Illustration of transforms

Illustration of transforms


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