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mindspore.mint.nn.functional.adaptive_max_pool1d

mindspore.mint.nn.functional.adaptive_max_pool1d(input, output_size, return_indices=False)

Performs 1D adaptive max pooling on a multi-plane input signal. That is, for any input size, the size of the specified output is L. The number of output features is equal to the number of input features.

Warning

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

Parameters

• input (Tensor) – The input of adaptive_max_pool1d, which is a 2D or 3D tensor, with float16, float32 or float64 data type.

• output_size (int) – The target output feature size. output_size is an integer.

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

Returns

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 input.

• 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
>>> input = Tensor([[2,3],[3,4]],dtype=mindspore.float16)
>>> output = mint.nn.functional.adaptive_max_pool1d(input, 3)
>>> print(output)
[[2.  3.  3. ]
 [3.  4.  4. ]]