ReplicationPad1d

class torch.nn.ReplicationPad1d(padding)

Pads the input tensor using replication of the input boundary.

For N-dimensional padding, use torch.nn.functional.pad().

Parameters:

padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 2-tuple, uses (\(\text{padding\_left}\), \(\text{padding\_right}\))

Shape:

• Input: \((C, W_{in})\) or \((N, C, W_{in})\).

• Output: \((C, W_{out})\) or \((N, C, W_{out})\), where

  \(W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}\)

Examples:

>>> m = nn.ReplicationPad1d(2)
>>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4)
>>> input
tensor([[[0., 1., 2., 3.],
         [4., 5., 6., 7.]]])
>>> m(input)
tensor([[[0., 0., 0., 1., 2., 3., 3., 3.],
         [4., 4., 4., 5., 6., 7., 7., 7.]]])
>>> # using different paddings for different sides
>>> m = nn.ReplicationPad1d((3, 1))
>>> m(input)
tensor([[[0., 0., 0., 0., 1., 2., 3., 3.],
         [4., 4., 4., 4., 5., 6., 7., 7.]]])