ReflectionPad1d — PyTorch 2.7 documentation (original) (raw)

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