mindspore.mint.nn.functional.avg_pool3d

mindspore.mint.nn.functional.avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None)

Applies a 3D average pooling over an input Tensor which can be regarded as a composition of 3D input planes. Typically the input is of shape $(N,C,D_{in},H_{in},W_{in})$ , outputs regional average in the $(D_{in},H_{in},W_{in})$ -dimension. Given kernel size $(kD,kH,kW)$ and stride , the operation is as follows.

$$\begin{array}{r}\begin{array}{c}\text{output}(N_i,C_j,d,h,w)=\frac{1}{kD\ast kH\ast kW}\sum _{l=0}^{kD-1}\sum _{m=0}^{kH-1}\sum _{n=0}^{kW-1}\\ \text{input}(N_i,C_j,stride[0]\times d+l,stride[1]\times h+m,stride[2]\times w+n)\end{array}\end{array}$$

Warning

This is an experimental API that is subject to change or deletion.

Note

This interface currently does not support Atlas A2 training series products.

Parameters

• input (Tensor) – Tensor of shape $(N,C,D_{in},H_{in},W_{in})$ or $(C,D_{in},H_{in},W_{in})$.

• kernel_size (Union[int, tuple[int], list[int]]) – The size of kernel used to take the average value. Can be a single number or a tuple $(kD,kH,kW)$ .

• stride (Union[int, tuple[int], list[int]], optional) – The distance of kernel moving. Can be a single number or a tuple $(sD,sH,sW)$ . Default: None, where its value is equal to kernel_size.

• padding (Union[int, tuple[int], list[int]], optional) – Implicit zero padding to be added on both sides. Can be a single number or a tuple $(padD,padH,padW)$ . Default: 0.

• ceil_mode (bool, optional) – If True, apply ceil instead of floor to compute the output shape. Default: False.

• count_include_pad (bool, optional) – If True, include the zero-padding in the averaging calculation. Default: True .

• divisor_override (int, optional) – If specified, it will be used as divisor in the averaging calculation, otherwise size of pooling region will be used. Default: None.

Returns

Tensor, with shape $(N,C,D_{out},H_{out},W_{out})$ or $(C,D_{out},H_{out},W_{out})$.

$$\begin{array}{r}\begin{array}{l}\\ D_{out}=\frac{D_{in}+2\times padding[0]-kernel\mathrm{\_}size[0]}{stride[0]}+1\\ H_{out}=\frac{H_{in}+2\times padding[1]-kernel\mathrm{\_}size[0]}{stride[1]}+1\\ W_{out}=\frac{W_{in}+2\times padding[2]-kernel\mathrm{\_}size[1]}{stride[2]}+1\end{array}\end{array}$$

Raises

• TypeError – If input is not a Tensor.

• TypeError – If kernel_size or stride is neither int nor tuple.

• TypeError – If ceil_mode or count_include_pad is not a bool.

• TypeError – If divisor_override is not an int or None.

• ValueError – If the dimension of input is not equal to 4 or 5.

• ValueError – If kernel_size or stride is less than 1.

• ValueError – If value of padding is less than 0.

• ValueError – If kernel_size, padding or stride is a tuple whose length is not equal to 1 or 3.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input_x = Tensor(np.arange(1 * 2 * 2 * 2 * 3).reshape((1, 2, 2, 2, 3)), mindspore.float16)
>>> output = mint.nn.functional.avg_pool3d(input_x, kernel_size=2, stride=1)
>>> print(output)
[[[[[ 5.  6.]]]
  [[[17. 18.]]]]]