mindspore.ops.avg_pool1d

mindspore.ops.avg_pool1d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, count_include_pad=True)[source]

Applies a 1D average pooling over an input Tensor which can be regarded as a composition of 1D input planes.

Typically the input is of shape $(N_{i n}, C_{i n}, L_{i n})$ , avg_pool1d outputs regional average in the $(L_{i n})$ -dimension. Given kernel size $k s = l_{k e r}$ and stride $s = s_{0}$ , the operation is as follows.

output (N_{i}, C_{j}, l) = \frac{1}{l_{k e r}} \sum_{n = 0}^{l_{k e r} - 1} input (N_{i}, C_{j}, s_{0} \times l + n)

Warning

kernel_size is in the range [1, 255]. stride is in the range [1, 63].

Parameters

input_x (Tensor) – Tensor of shape $(N, C_{i n}, L_{i n})$ .
kernel_size (int) – The size of kernel window used to take the average value. Default: 1 .
stride (Union(int, tuple[int])) – The distance of kernel moving. stride can either be an int number or a tuple of one int number. Default: 1 .
padding (Union(int, tuple[int])) – The pad value to be filled. padding can either be an integer or a tuple of one integer. Default: 0 .
ceil_mode (bool) – If True, apply ceil instead of floor to compute the output shape. Default: False.
count_include_pad (bool) – If True, include the zero-padding in the averaging calculation. Default: True .

Returns

Tensor of shape $(N, C_{o u t}, L_{o u t})$ .

Raises

TypeError – If input_x is not a Tensor.
TypeError – If kernel_size or stride is not an int.
TypeError – If ceil_mode or count_include_pad is not a bool.
ValueError – If length of shape of input_x is not equal to 3.
ValueError – If kernel_size or stride is less than 1.
ValueError – If padding is not int nor a tuple whose length is equal to 2.
ValueError – If value(s) of padding is less than 0.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32)
>>> output = ops.avg_pool1d(input_x, kernel_size=6, stride=1)
>>> print(output.shape)
(1, 3, 1)