RandomResizedCrop¶

class
torchvision.transforms.
RandomResizedCrop
(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=<InterpolationMode.BILINEAR: 'bilinear'>)[source]¶ 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 isInterpolationMode.BILINEAR
. If input is Tensor, onlyInterpolationMode.NEAREST
,InterpolationMode.BILINEAR
andInterpolationMode.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
forward
(img)[source]¶  Parameters
img (PIL Image or Tensor) – Image to be cropped and resized.
 Returns
Randomly cropped and resized image.
 Return type
PIL Image or Tensor