比较与torch.nn.functional.avg_pool1d的差异

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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)
# [[[ 2. 4. 6.]]]