# oneflow.nn.AvgPool1d¶

class oneflow.nn.AvgPool1d(kernel_size: Union[int, Tuple[int, int]], stride: Optional[Union[int, Tuple[int, int]]] = None, padding: Union[int, Tuple[int, int]] = 0, ceil_mode: bool = False, count_include_pad: bool = True)

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 $$(N, C, H, W)$$, output $$(N, C, H_{out}, W_{out})$$ and kernel_size $$k$$ can be precisely described as:

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

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.

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.

Parameters
• kernel_size – the size of the window.

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

• 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.

For example:

import oneflow as flow
import numpy as np


__init__(kernel_size: Union[int, Tuple[int, int]], stride: Optional[Union[int, Tuple[int, int]]] = None, padding: Union[int, Tuple[int, int]] = 0, ceil_mode: bool = False, count_include_pad: bool = True)
 __call__(*args, **kwargs) Call self as a function. __delattr__(name, /) Implement delattr(self, name). __dir__() Default dir() implementation. __eq__(value, /) Return self==value. __format__(format_spec, /) Default object formatter. __ge__(value, /) Return self>=value. __getattr__(name) __getattribute__(name, /) Return getattr(self, name). __gt__(value, /) Return self>value. __hash__() Return hash(self). __init__(kernel_size[, stride, padding, …]) Initialize self. __init_subclass__ This method is called when a class is subclassed. __le__(value, /) Return self<=value. __lt__(value, /) Return self