mindspore.ops.adaptive_avg_pool2d

mindspore.ops.adaptive_avg_pool2d(input, output_size)[source]

Performs 2D adaptive average pooling on a multi-plane input signal. 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.

For adaptive average pooling for 2D:

\[\begin{split}\begin{align} h_{start} &= floor(i * H_{in} / H_{out})\\ h_{end} &= ceil((i + 1) * H_{in} / H_{out})\\ w_{start} &= floor(j * W_{in} / W_{out})\\ w_{end} &= ceil((j + 1) * W_{in} / W_{out})\\ Output(i,j) &= \frac{\sum Input[h_{start}:h_{end}, w_{start}:w_{end}]}{(h_{end}- h_{start}) * (w_{end}- w_{start})} \end{align}\end{split}\]

Warning

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

Parameters:

input (Tensor) – The input of adaptive_avg_pool2d, which is a 3D or 4D tensor, with float16, float32 or float64 data type.
output_size (Union[int, tuple]) – The target output size. output_size can be a tuple \((H, W)\), or an int H for \((H, H)\). \(H\) and \(W\) can be int or None. If it is None, it means the output size is the same as the input size.

Returns:

Tensor, with the same type as the input.

Shape of the output is input_shape[:len(input_shape) - len(out_shape)] + out_shape.

\[\begin{split}out\_shape = \begin{cases} input\_shape[-2] + output\_size[1], & \text{if } output\_size \text{ is (None, w);}\\ output\_size[0] + input\_shape[-1], & \text{if } output\_size \text{ is (h, None);}\\ input\_shape[-2:], & \text{if } output\_size \text{ is (None, None);}\\ (h, h), & \text{if } output\_size \text{ is h;}\\ (h, w), & \text{if } output\_size \text{ is (h, w)} \end{cases}\end{split}\]

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
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> # case 1: output_size=(None, 2)
>>> input = 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]]]), mindspore.float32)
>>> output = ops.adaptive_avg_pool2d(input, (None, 2))
>>> print(output)
[[[1.5 2.5]
  [4.5 5.5]
  [7.5 8.5]]
 [[1.5 2.5]
  [4.5 5.5]
  [7.5 8.5]]
 [[1.5 2.5]
  [4.5 5.5]
  [7.5 8.5]]]
>>> # case 2: output_size=2
>>> output = ops.adaptive_avg_pool2d(input, 2)
>>> print(output)
[[[3. 4.]
  [6. 7.]]
 [[3. 4.]
  [6. 7.]]
 [[3. 4.]
  [6. 7.]]]
>>> # case 3: output_size=(1, 2)
>>> output = ops.adaptive_avg_pool2d(input, (1, 2))
>>> print(output)
[[[4.5 5.5]]
 [[4.5 5.5]]
 [[4.5 5.5]]]