Differences between torch.nn.AdaptiveMaxPool1d

The following mapping relationships can be found in this file.

PyTorch APIs	MindSpore APIs
torch.nn.AdaptiveMaxPool1d	mindspore.nn.AdaptiveMaxPool1d
torch.nn.functional.adaptive_max_pool1d	mindspore.ops.adaptive_max_pool1d

torch.nn.AdaptiveMaxPool1d

torch.nn.AdaptiveMaxPool1d(output_size, return_indices=False)(input) -> Tensor

For more information, see torch.nn.AdaptiveMaxPool1d.

mindspore.nn.AdaptiveMaxPool1d

mindspore.nn.AdaptiveMaxPool1d(output_size)(x) -> Tensor

For more information, see mindspore.nn.AdaptiveMaxPool1d.

Differences

PyTorch: Adaptive max pooling operation for temporal data, supporting 2D and 3D data.

MindSpore: This API in MindSpore currently only supports 3D data and requires the last dimension of the input data to be larger than that of the output data, and must divide the output_size. Currently not support index subscripts that return the maximum value.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	output_size	output_size	MindSpore currently only supports 3D data and requires the length of the last dimension of the input data to be divisible by output_size
	Parameter 2	return_indices	-	MindSpore does not have this parameter and does not support index subscripts that return the maximum value currently.
Input	Single input	input	x	Same function, different parameter names

Code Example 1

For 3D data, perform adaptive maximum pooling operation on the data when the output length can be divisiable by the input length.

# PyTorch
import torch
from torch import tensor
import numpy as np

max_pool = torch.nn.AdaptiveMaxPool1d(output_size=4)
x = tensor(np.arange(16).reshape(1, 2, 8), dtype=torch.float32)
output = max_pool(x)
print(output)
# tensor([[[ 1.,  3.,  5.,  7.],
#          [ 9., 11., 13., 15.]]])

# MindSpore
import mindspore
from mindspore import Tensor, nn
import numpy as np
pool = nn.AdaptiveMaxPool1d(output_size=4)
x = Tensor(np.arange(16).reshape(1, 2, 8), mindspore.float32)
output = pool(x)
print(output)
# [[[ 1.  3.  5.  7.]
#   [ 9. 11. 13. 15.]]]