mindspore.nn.AdaptiveAvgPool2d
- class mindspore.nn.AdaptiveAvgPool2d(output_size)[source]
This operator applies a 2D adaptive average pooling to an input signal composed of multiple input planes. That is, for any input size, the size of the specified output is H x W. The number of output features is equal to the number of input features.
The input and output data format can be "NCHW" and "CHW". N is the batch size, C is the number of channels, H is the feature height, and W is the feature width.
\[\begin{split}\begin{align} h_{start} &= floor(i * H_{in} / H_{out})\\ h_{end} &= ceil((i + 1) * H_{in} / H_{out})\\ w_{start} &= floor(j * W_{in} / W_{out})\\ w_{end} &= ceil((j + 1) * W_{in} / W_{out})\\ Output(i,j) &= \frac{\sum Input[h_{start}:h_{end}, w_{start}:w_{end}]}{(h_{end}- h_{start}) * (w_{end}- w_{start})} \end{align}\end{split}\]- Parameters
output_size (Union[int, tuple]) – The target output size is H x W. output_size can be a tuple consisted of int type H and W, or a single H for H x H, or None. If it is None, it means the output size is the same as the input size.
- Inputs:
input (Tensor) - The input of AdaptiveAvgPool2d, which is a 3D or 4D tensor, with float16, float32 or float64 data type.
- Outputs:
Tensor of shape \((N, C_{out}, H_{out}, W_{out})\).
- Raises
ValueError – If output_size is a tuple and the length of output_size is not 2.
TypeError – If input is not a Tensor.
TypeError – If dtype of input is not float16, float32 or float64.
ValueError – If the dimension of input is less than or equal to the dimension of output_size.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore as ms >>> import numpy as np >>> pool = ms.nn.AdaptiveAvgPool2d(2) >>> input_x = ms.Tensor(np.array([[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], ... [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], ... [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]]), ms.float32) >>> output = pool(input_x) >>> result = output.shape >>> print(result) (3, 2, 2)