作用：保留输入特征，同时减小数据量。

---

输入图像：

1	2	0	3	1
0	1	2	3	1
1	2	1	0	0
5	2	3	1	1
2	1	0	1	1

池化核：3*3，kernel_size=3

输出图像：ceil_mode=True

2	3
5	1

ceil_mode=False

2

输入：

from turtle import shape

import torch
from torch import nn
from torch.nn import MaxPool2d

input=torch.tensor([[1,2,0,3,1],
                   [0,1,2,3,1],
                   [1,2,1,0,0],
                   [5,2,3,1,1],
                   [2,1,0,1,1]],dtype=torch.float32)
input=torch.reshape(input,(-1,1,5,5))
print(input,shape)

class KELE(nn.Module):
    def __init__(self):
        super(KELE,self).__init__()
        self.maxpool1=MaxPool2d(kernel_size=3,ceil_mode=True)

    def forward (self,input):
        output=self.maxpool1(input)
        return output
kele=KELE()
output=kele(input)
print(output)

输出：

tensor([[[[1., 2., 0., 3., 1.],
          [0., 1., 2., 3., 1.],
          [1., 2., 1., 0., 0.],
          [5., 2., 3., 1., 1.],
          [2., 1., 0., 1., 1.]]]]) <function shape at 0x00000158F63DD0D0>
tensor([[[[2., 3.],
          [5., 1.]]]])