pytorch / 2 / generated / torch.nn.avgpool1d.html

AvgPool1d

class torch.nn.AvgPool1d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=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 , L o u t ) (N, C, L_{out}) and kernel_size k k can be precisely described as:

out ( N i , C j , l ) = 1 k m = 0 k 1 input ( N i , C j , 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.

Note

When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.

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

Parameters
  • kernel_size (Union[int, Tuple[int]]) – the size of the window
  • stride (Union[int, Tuple[int]]) – the stride of the window. Default value is kernel_size
  • padding (Union[int, Tuple[int]]) – implicit zero padding to be added on both sides
  • ceil_mode (bool) – when True, will use ceil instead of floor to compute the output shape
  • count_include_pad (bool) – when True, will include the zero-padding in the averaging calculation
Shape:
  • Input: ( N , C , L i n ) (N, C, L_{in}) or ( C , L i n ) (C, L_{in}) .
  • Output: ( N , C , L o u t ) (N, C, L_{out}) or ( C , L o u t ) (C, L_{out}) , where

    L o u t = L i n + 2 × padding kernel_size stride + 1 L_{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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