mindspore.mint.nn.AdaptiveMaxPool1d

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class mindspore.mint.nn.AdaptiveMaxPool1d(output_size, return_indices=False)[source]

Applies a 1D adaptive max pooling over an input signal composed of several input planes.

The output is of size \(L_{out}\) , for any input size. The number of output features is equal to the number of input planes.

Warning

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

Parameters

  • output_size (Union[int, tuple]) – the target output size \(L_{out}\) .

  • return_indices (bool, optional) – Whether to return the index of the maximum value. Default: False .

Inputs:

  • input (Tensor) - The input with shape \((N, C, L_{in})\) or \((C, L_{in})\) .

Outputs:

Union(Tensor, tuple(Tensor, Tensor)).

  • If return_indices is False, output is a Tensor, with shape \((N, C, L_{out})\). It has the same data type as x.

  • If return_indices is True, output is a Tuple of 2 Tensors, representing the result and where the max values are generated.

Raises

  • TypeError – If input is not a tensor.

  • TypeError – If dtype of input is not float16, float32 or float64.

  • TypeError – If output_size is not int or tuple.

  • TypeError – If return_indices is not a bool.

  • ValueError – If output_size is a tuple and the length of output_size is not 1.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> input = Tensor(np.array([[[2, 1, 2], [2, 3, 5]]]), mindspore.float16)
>>> net = mint.nn.AdaptiveMaxPool1d(3)
>>> output = net(input)
>>> print(output)
[[[2. 1. 2.]
  [2. 3. 5.]]]