mindspore.nn.AdaptiveAvgPool1d
- class mindspore.nn.AdaptiveAvgPool1d(output_size)[source]
1D adaptive average pooling for temporal data.
Applies a 1D adaptive average pooling over an input Tensor which can be regarded as a composition of 1D input planes.
Typically, the input is of shape \((N_{in}, C_{in}, L_{in})\), AdaptiveAvgPool1d outputs regional average in the \(L_{in}\)-dimension. The output is of shape \((N_{in}, C_{in}, L_{out})\), where \(L_{out}\) is defined by output_size.
Note
\(L_{in}\) must be divisible by output_size.
- Parameters
output_size (int) – the target output size \(L_{out}\).
- Inputs:
x (Tensor) - Tensor of shape \((N, C_{in}, L_{in})\), with float16 or float32 data type.
- Outputs:
Tensor of shape \((N, C_{in}, L_{out})\), has the same type as x.
- Raises
TypeError – If output_size is not an int.
TypeError – If x is neither float16 nor float32.
ValueError – If output_size is less than 1.
ValueError – If length of shape of x is not equal to 3.
ValueError – If the last dimension of x is smaller than output_size.
ValueError – If the last dimension of x is not divisible by output_size.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, nn >>> import numpy as np >>> pool = nn.AdaptiveAvgPool1d(output_size=2) >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32) >>> output = pool(x) >>> result = output.shape >>> print(result) (1, 3, 2)