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.

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