Pad¶
- class torchvision.transforms.v2.Pad(padding: Union[int, Sequence[int]], 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')[source]¶
Pad the input on all sides with the given “pad” value.
If the input is a
torch.Tensor
or aTVTensor
(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:
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={tv_tensors.Image: 127, tv_tensors.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]
Examples using
Pad
:Illustration of transforms