Shortcuts

ReflectionPad1d

class torch.nn.ReflectionPad1d(padding)[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 (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.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.]]])

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources