RandomCrop

class torchvision.transforms.v2.RandomCrop(size: Union[int, Sequence[int]], padding: Optional[Union[int, Sequence[int]]] = None, pad_if_needed: bool = False, fill: Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Optional[Union[int, float, Sequence[int], Sequence[float]]]]] = 0, padding_mode: Literal['constant', 'edge', 'reflect', 'symmetric'] = 'constant')

Crop the input at a random location.

If the input is a torch.Tensor or a TVTensor (e.g. Image, Video, BoundingBoxes etc.) it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape. A bounding box can have [..., 4] shape.

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]).

• padding (int or sequence, optional) –

  Optional padding on each border of the image. Default is None. If a single int is provided this is used to pad all borders. If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively. If a sequence of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.

  Note

  In torchscript mode padding as single int is not supported, use a sequence of length 1: [padding, ].

• pad_if_needed (boolean, optional) – It will pad the image if smaller than the desired size to avoid raising an exception. Since cropping is done after padding, the padding seems to be done at a random offset.

• fill (number or tuple or dict, optional) – Pixel fill value used when the padding_mode is constant. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. Fill value can be also a dictionary mapping data type to the fill value, e.g. fill={tv_tensors.Image: 127, tv_tensors.Mask: 0} where Image will be filled with 127 and Mask will be filled with 0.

• padding_mode (str, optional) –

  Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.

  • constant: pads with a constant value, this value is specified with fill

  • edge: pads with the last value at the edge of the image.

  • reflect: pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]

  • symmetric: pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]

Examples using RandomCrop:

Getting started with transforms v2

Getting started with transforms v2

Illustration of transforms

Illustration of transforms

static get_params(img: Tensor, output_size: Tuple[int, int]) → Tuple[int, int, int, int]

Get parameters for crop for a random crop.

Parameters:

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

• output_size (tuple) – Expected output size of the crop.

Returns:

params (i, j, h, w) to be passed to crop for random crop.

Return type:

tuple

make_params(flat_inputs: List[Any]) → Dict[str, Any]

Method to override for custom transforms.

See How to write your own v2 transforms

transform(inpt: Any, params: Dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms