比较与torch.nn.functional.avg_pool1d的差异

以下映射关系均可参考本文。

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)

更多内容详见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)

更多内容详见mindspore.ops.avg_pool1d。

差异对比

PyTorch：对时序数据进行平均池化运算。

MindSpore：MindSpore此API功能与pytorch基本一致，部分输入默认值不同。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	input	input_x	参数名不同
	参数2	kernel_size	kernel_size	pytorch参数无默认值，MindSpore参数默认值为1
	参数3	stride	stride	pytorch参数默认值为None，默认与kernel_size一致，MindSpore参数默认值为1
	参数4	padding	padding
	参数5	ceil_mode	ceil_mode
	参数6	count_include_pad	count_include_pad

代码示例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)
# ValueError: For avg_pool1d, input must have 3 dim, but got 1.