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.

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. ]]]