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# TenCrop

class torchvision.transforms.TenCrop(size, vertical_flip=False)

  Crop the given image into four corners and the central crop plus the flipped version of these (horizontal flipping is used by default). 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 (size, size) is made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).

    - vertical_flip (bool) – Use vertical flipping instead of horizontal

  Example

      >>> transform = Compose([
      >>>    TenCrop(size), # this is a tuple 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

  forward(img)
    Parameters:

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

    Returns:

      tuple of 10 images. Image can be PIL Image or Tensor

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