完整代码如下：

import torch
from torch import nn
from torch.nn import MaxPool2d
import cv2
import numpy as np
import os



#最大池化的下采样网络，卷积核大小为3，步长为2
class Mydele(nn.Module):
    def __init__(self):
        super(Mydele,self).__init__()
        self.maxpool1=MaxPool2d(kernel_size=3,stride=2,ceil_mode=True)


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

mydele = Mydele()

#image:cv2读取的图片
def down_sample(image,item):
    # 修改矩阵的维度：1080X1920X3------>1X3X1080X1920
    data = image.reshape(1, 3, image.shape[0], image.shape[1])
    #print(data.shape)
    # 修改矩阵类型：array--->tensor
    input = torch.tensor(data, dtype=torch.float32)

    # 做两次池化
    output = mydele(input)
    output = mydele(output)
    # print(output)

    # 修改数据类型：tensor--->array
    numpy_output = output.numpy()

    # 修改矩阵维数：1X3X270X480---->270X480X3
    numpy_output = numpy_output.reshape(numpy_output.shape[2], numpy_output.shape[3], 3)

    # 将矩阵改为可读取图片的格式
    result = numpy_output.astype(np.uint8)

    # 下载下采样后的图片
    cv2.imwrite(item, result)



#批量图片的下采样：输入图片的文件夹路径和要保存的下采样的文件夹路径
img_path=r"D:\AI\data\red_green_light\img_biaozhu"
img_save=r"D:\AI\data\red_green_light\down_sample"

ls=os.listdir(img_path)
for item in ls:
    print(item)
    image_path=os.path.join(img_path,item)#图片的具体路径
    image=cv2.imread(image_path)#cv2读取图片
    down_image_saves=os.path.join(img_save,item)#保存下采样图片的路径
    down_sample(image, down_image_saves)#下采样图片，并将其保存