- class torchvision.transforms.FiveCrop(size)[source]¶
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
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.
size (sequence or int) – Desired output size of the crop. If size is an
intinstead 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, size).
>>> 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
FiveCrop:Illustration of transforms