On this page
ConstantPad2d
class torch.nn.ConstantPad2d(padding, value)
[source]-
Pads the input tensor boundaries with a constant value.
For
N
-dimensional padding, usetorch.nn.functional.pad()
.- Parameters
-
padding (int, tuple) – the size of the padding. If is
int
, uses the same padding in all boundaries. If a 4-tuple
, uses ( , , , )
- Shape:
-
- Input: or .
Output: or , where
Examples:
>>> m = nn.ConstantPad2d(2, 3.5) >>> input = torch.randn(1, 2, 2) >>> input tensor([[[ 1.6585, 0.4320], [-0.8701, -0.4649]]]) >>> m(input) tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 1.6585, 0.4320, 3.5000, 3.5000], [ 3.5000, 3.5000, -0.8701, -0.4649, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]]) >>> # using different paddings for different sides >>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5) >>> m(input) tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000], [ 3.5000, 3.5000, 3.5000, 1.6585, 0.4320], [ 3.5000, 3.5000, 3.5000, -0.8701, -0.4649], [ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]])
© 2024, PyTorch Contributors
PyTorch has a BSD-style license, as found in the LICENSE file.
https://pytorch.org/docs/2.1/generated/torch.nn.ConstantPad2d.html