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ReplicationPad1d

class torch.nn.ReplicationPad1d(padding)[source]

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 (padding_left\text{padding\_left}, padding_right\text{padding\_right})

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

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

    Wout=Win+padding_left+padding_rightW_{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.]]])

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