mindspore.ops.AdaptiveAvgPool2D
- class mindspore.ops.AdaptiveAvgPool2D(output_size)[source]
AdaptiveAvgPool2D operation.
Refer to
mindspore.ops.adaptive_avg_pool2d()
for more details.Warning
This is an experimental API that is subject to change or deletion.
- Parameters
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.
- Inputs:
input_x (Tensor) - The input of AdaptiveAvgPool2D, which is a 3D or 4D tensor, with float16 ,float32 or float64 data type.
- Outputs:
Tensor, with the same type as the input_x.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> # case 1: output_size=(None, 2) >>> input_x = 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) >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((None, 2)) >>> output = adaptive_avg_pool_2d(input_x) >>> 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 >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D(2) >>> output = adaptive_avg_pool_2d(input_x) >>> print(output) [[[3. 4.] [6. 7.]] [[3. 4.] [6. 7.]] [[3. 4.] [6. 7.]]] >>> # case 3: output_size=(1, 2) >>> adaptive_avg_pool_2d = ops.AdaptiveAvgPool2D((1, 2)) >>> output = adaptive_avg_pool_2d(input_x) >>> print(output) [[[4.5 5.5]] [[4.5 5.5]] [[4.5 5.5]]]