pad

torchvision.transforms.functional.pad(img: Tensor, padding: List[int], fill: Union[int, float] = 0, padding_mode: str = 'constant') → Tensor

Pad the given image on all sides with the given “pad” value. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means at most 2 leading dimensions for mode reflect and symmetric, at most 3 leading dimensions for mode edge, and an arbitrary number of leading dimensions for mode constant

Parameters:

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

• padding (int or sequence) –

  Padding on each border. 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, ].

• fill (number or tuple) – Pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. This value is only used when the padding_mode is constant. Only number is supported for torch Tensor. Only int or tuple value is supported for PIL Image.

• padding_mode (str) –

  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. If input a 5D torch Tensor, the last 3 dimensions will be padded instead of the last 2

  • 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]

Returns:

Padded image.

Return type:

PIL Image or Tensor

Examples using pad:

Illustration of transforms

Illustration of transforms