FiveCrop — Torchvision 0.22 documentation (original) (raw)
class torchvision.transforms.v2.FiveCrop(size: Union[int, Sequence[int]])[source]¶
Crop the image or video into four corners and the central crop.
If the input is a torch.Tensor or a Image or aVideo it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape.
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 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[0], size[0]).
Example
class BatchMultiCrop(transforms.Transform): ... def forward(self, sample: Tuple[Tuple[Union[tv_tensors.Image, tv_tensors.Video], ...], int]): ... images_or_videos, labels = sample ... batch_size = len(images_or_videos) ... image_or_video = images_or_videos[0] ... images_or_videos = tv_tensors.wrap(torch.stack(images_or_videos), like=image_or_video) ... labels = torch.full((batch_size,), label, device=images_or_videos.device) ... return images_or_videos, labels ... image = tv_tensors.Image(torch.rand(3, 256, 256)) label = 3 transform = transforms.Compose([transforms.FiveCrop(224), BatchMultiCrop()]) images, labels = transform(image, label) images.shape torch.Size([5, 3, 224, 224]) labels tensor([3, 3, 3, 3, 3])
Examples using FiveCrop:
transform(inpt: Any, params: Dict[str, Any]) → Any[source]¶
Method to override for custom transforms.