class torchvision.transforms.Pad(padding, fill=0, padding_mode='constant')[source]

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

  • 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.


    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]

Examples using Pad:

Illustration of transforms

Illustration of transforms

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


Padded image.

Return type:

PIL Image or Tensor


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