mindspore.mint.nn.AdaptiveAvgPool2d

class mindspore.mint.nn.AdaptiveAvgPool2d(output_size)[source]

Applies a 2D adaptive average pooling over an input signal composed of several input planes.

The output is of size \(H x W\) , for any input size. The number of output features is equal to the number of input planes.

Warning

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

Parameters: output_size (Union(int, tuple[int])) – the target output size of the image of the form \(H x W\) . Can be a tuple \((H, W)\) or a single \(H\) for square image \(H x H\) . \(H\) and \(W\) can be either a int , or None which means the size will be the same as that of the input.

Inputs:

input (Tensor) - The input with shape \((N, C, H, W)\) or \((C, H, W)\) .

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> input = Tensor(np.array([[[2, 1, 2], [2, 3, 5]]]), mindspore.float16)
>>> net = mint.nn.AdaptiveAvgPool2d((2, 2))
>>> output = net(input)
>>> print(output)
[[[1.5 1.5]
  [2.5 4. ]]]