mindspore.nn.AdaptiveMaxPool1d

class mindspore.nn.AdaptiveMaxPool1d(output_size)

Applies a 1D adaptive maximum pooling over an input Tensor which can be regarded as a composition of 1D input planes.

Typically, the input is of shape $(N_{in},C_{in},L_{in})$, AdaptiveMaxPool1d outputs regional maximum 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 x is neither float16 nor float32.

• TypeError – If output_size is not an int.

• ValueError – If output_size is less than 1.

• 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.

• ValueError – If length of shape of x is not equal to 3.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> pool = ms.nn.AdaptiveMaxPool1d(output_size=3)
>>> x = ms.Tensor(np.random.randint(0, 10, [1, 3, 6]), ms.float32)
>>> output = pool(x)
>>> result = output.shape
>>> print(result)
(1, 3, 3)