Pad¶
- class torchvision.transforms.v2.Pad(padding: Union[int, Sequence[int]], fill: Union[int, float, Sequence[int], Sequence[float], None, Dict[Type, Optional[Union[int, float, Sequence[int], Sequence[float]]]]] = 0, padding_mode: Literal['constant', 'edge', 'reflect', 'symmetric'] = 'constant')[source]¶
[BETA] Pad the input on all sides with the given “pad” value.
Warning
The Pad transform is in Beta stage, and while we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes.
If the input is a
torch.Tensor
or aDatapoint
(e.g.Image
,Video
,BoundingBox
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:
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 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={datapoints.Image: 127, datapoints.Mask: 0}
whereImage
will be filled with 127 andMask
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]