API

 torch / nn / torch.nn


AvgPool1d

class torch.nn.AvgPool1d(kernel_size: Union[T, Tuple[T]], stride: Union[T, Tuple[T]] = None, padding: Union[T, Tuple[T]] = 0, ceil_mode: bool = False, count_include_pad: bool = True)[source]

Applies a 1D average pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size (N,C,L)(N, C, L) , output (N,C,Lout)(N, C, L_{out}) and kernel_size kk can be precisely described as:

out(Ni,Cj,l)=1km=0k1input(Ni,Cj,stride×l+m)\text{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1} \text{input}(N_i, C_j, \text{stride} \times l + m)

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

The parameters kernel_size, stride, padding can each be an int or a one-element tuple.

Parameters
  • kernel_size – the size of the window

  • stride – the stride of the window. Default value is kernel_size

  • padding – implicit zero padding to be added on both sides

  • ceil_mode – when True, will use ceil instead of floor to compute the output shape

  • count_include_pad – when True, will include the zero-padding in the averaging calculation

Shape:
  • Input: (N,C,Lin)(N, C, L_{in})

  • Output: (N,C,Lout)(N, C, L_{out}) , where

    Lout=Lin+2×paddingkernel_sizestride+1L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor

Examples:

>>> # pool with window of size=3, stride=2
>>> m = nn.AvgPool1d(3, stride=2)
>>> m(torch.tensor([[[1.,2,3,4,5,6,7]]]))
tensor([[[ 2.,  4.,  6.]]])

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