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ReplicationPad2d
class torch.nn.ReplicationPad2d(padding)
[source]-
Pads the input tensor using replication of the input boundary.
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.ReplicationPad2d(2) >>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3) >>> input tensor([[[[0., 1., 2.], [3., 4., 5.], [6., 7., 8.]]]]) >>> m(input) tensor([[[[0., 0., 0., 1., 2., 2., 2.], [0., 0., 0., 1., 2., 2., 2.], [0., 0., 0., 1., 2., 2., 2.], [3., 3., 3., 4., 5., 5., 5.], [6., 6., 6., 7., 8., 8., 8.], [6., 6., 6., 7., 8., 8., 8.], [6., 6., 6., 7., 8., 8., 8.]]]]) >>> # using different paddings for different sides >>> m = nn.ReplicationPad2d((1, 1, 2, 0)) >>> m(input) tensor([[[[0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [0., 0., 1., 2., 2.], [3., 3., 4., 5., 5.], [6., 6., 7., 8., 8.]]]])
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https://pytorch.org/docs/2.1/generated/torch.nn.ReplicationPad2d.html