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ZeroPad1d

class torch.nn.ZeroPad1d(padding)[source]

Pads the input tensor boundaries with zero.

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 both 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.ZeroPad1d(2)
>>> input = torch.randn(1, 2, 4)
>>> input
tensor([[[-1.0491, -0.7152, -0.0749,  0.8530],
         [-1.3287,  1.8966,  0.1466, -0.2771]]])
>>> m(input)
tensor([[[ 0.0000,  0.0000, -1.0491, -0.7152, -0.0749,  0.8530,  0.0000,
           0.0000],
         [ 0.0000,  0.0000, -1.3287,  1.8966,  0.1466, -0.2771,  0.0000,
           0.0000]]])
>>> m = nn.ZeroPad1d(2)
>>> input = torch.randn(1, 2, 3)
>>> input
tensor([[[ 1.6616,  1.4523, -1.1255],
         [-3.6372,  0.1182, -1.8652]]])
>>> m(input)
tensor([[[ 0.0000,  0.0000,  1.6616,  1.4523, -1.1255,  0.0000,  0.0000],
         [ 0.0000,  0.0000, -3.6372,  0.1182, -1.8652,  0.0000,  0.0000]]])
>>> # using different paddings for different sides
>>> m = nn.ZeroPad1d((3, 1))
>>> m(input)
tensor([[[ 0.0000,  0.0000,  0.0000,  1.6616,  1.4523, -1.1255,  0.0000],
         [ 0.0000,  0.0000,  0.0000, -3.6372,  0.1182, -1.8652,  0.0000]]])

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