Differences with torch.nn.functional.avg_pool1d

The following mapping relationships can be found in this file.

PyTorch APIs	MindSpore APIs
torch.nn.functional.avg_pool1d	mindspore.ops.avg_pool1d
torch.nn.functional.avg_pool2d	mindspore.ops.avg_pool2d
torch.nn.functional.avg_pool3d	mindspore.ops.avg_pool3d

torch.nn.functional.avg_pool1d

torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)

For more information, see torch.nn.functional.avg_pool1d.

mindspore.ops.avg_pool1d

mindspore.ops.avg_pool1d(input_x, kernel_size=1, stride=1, padding=0, ceil_mode=False, count_include_pad=True)

For more information, see mindspore.ops.avg_pool1d.

Differences

PyTorch: Perform average pooling operations on time series data.

MindSpore: MindSpore API function is basically the same as pytorch, with different default values for some inputs.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	input_x	Different parameter names
	Parameter 2	kernel_size	kernel_size	The pytorch parameter has no default value and the MindSpore parameter has a default value of 1.
	Parameter 3	stride	stride	The default value of pytorch parameter is None, which is consistent with kernel_size by default, and the default value of MindSpore Parameter is 1.
	Parameter 4	padding	padding
	Parameter 5	ceil_mode	ceil_mode
	Parameter 6	count_include_pad	count_include_pad

Code Example 1

# PyTorch
import torch
import numpy as np

input = torch.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=torch.float32)
output = torch.nn.functional.avg_pool1d(input, kernel_size=3, stride=2)
print(output)
# tensor([[[ 2.,  4.,  6.]]])

# MindSpore
import mindspore
from mindspore import Tensor, ops

input_x = Tensor([[[1, 2, 3, 4, 5, 6, 7]]], mindspore.float32)
output = ops.avg_pool1d(input_x, kernel_size=3, stride=2)
print(output)
# [[[ 2. 4. 6.]]]