AvgPool1d¶

class torch.nn.AvgPool1d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)[source][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 $(N, C, L)$, output $(N, C, L_{out})$ and kernel_size $k$ can be precisely described as:

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

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: $(N, C, L_{in})$ or $(C, L_{in})$.

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

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

  Per the note above, if ceil_mode is True and $(L_{out} - 1) \times \text{stride} \geq L_{in} + \text{padding}$, we skip the last window as it would start in the right padded region, resulting in $L_{out}$ being reduced by one.

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.]]])