ReflectionPad1d¶

class torch.nn.ReflectionPad1d(padding: Union[T, Tuple[T, T]])[source]¶

Pads the input tensor using the reflection 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: $(N, C, W_{in})$

• Output: $(N, C, W_{out})$ where

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

Examples:

>>> m = nn.ReflectionPad1d(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([[[2., 1., 0., 1., 2., 3., 2., 1.],
         [6., 5., 4., 5., 6., 7., 6., 5.]]])
>>> # using different paddings for different sides
>>> m = nn.ReflectionPad1d((3, 1))
>>> m(input)
tensor([[[3., 2., 1., 0., 1., 2., 3., 2.],
         [7., 6., 5., 4., 5., 6., 7., 6.]]])