ReflectionPad1d

class torch.nn.ReflectionPad1d(padding)

使用输入边界的反射来填充输入张量。

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

参数

padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 2-tuple, uses ($\text{padding\_left}$, $\text{padding\_right}$) Note that padding size should be less than the corresponding input dimension.

形状

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

• 输出： $(C, W_{out})$ 或 $(N, C, W_{out})$，其中

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

示例

>>> 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.]]])