FiveCrop

class torchvision.transforms.FiveCrop(size)

Crop the given image into four corners and the central crop. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions

Note

This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns. See below for an example of how to deal with this.

Parameters:

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

Example

>>> transform = Compose([
>>>    FiveCrop(size), # this is a list of PIL Images
>>>    Lambda(lambda crops: torch.stack([PILToTensor()(crop) for crop in crops])) # returns a 4D tensor
>>> ])
>>> #In your test loop you can do the following:
>>> input, target = batch # input is a 5d tensor, target is 2d
>>> bs, ncrops, c, h, w = input.size()
>>> result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
>>> result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops

Examples using FiveCrop:

Illustration of transforms

Illustration of transforms

forward(img)

Parameters:

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

Returns:

tuple of 5 images. Image can be PIL Image or Tensor