深度学习图像去雾python代码

以下是使用深度学习进行图像去雾的Python代码示例，使用的是AOD-Net去雾网络的PyTorch实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image

# 定义AOD-Net去雾网络
class AODNet(nn.Module):
    def __init__(self):
        super(AODNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(3)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(3)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(3)
        self.relu3 = nn.ReLU(inplace=True)
        self.conv4 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn4 = nn.BatchNorm2d(3)
        self.relu4 = nn.ReLU(inplace=True)
        self.conv5 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn5 = nn.BatchNorm2d(3)
        self.relu5 = nn.ReLU(inplace=True)
        self.conv6 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn6 = nn.BatchNorm2d(3)
        self.relu6 = nn.ReLU(inplace=True)
        self.conv7 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn7 = nn.BatchNorm2d(3)
        self.relu7 = nn.ReLU(inplace=True)
        self.conv8 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn8 = nn.BatchNorm2d(3)
        self.relu8 = nn.ReLU(inplace=True)
        self.conv9 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn9 = nn.BatchNorm2d(3)
        self.relu9 = nn.ReLU(inplace=True)
        self.conv10 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn10 = nn.BatchNorm2d(3)
        self.relu10 = nn.ReLU(inplace=True)
        self.conv11 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn11 = nn.BatchNorm2d(3)
        self.relu11 = nn.ReLU(inplace=True)
        self.conv12 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn12 = nn.BatchNorm2d(3)
        self.relu12 = nn.ReLU(inplace=True)
        self.conv13 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn13 = nn.BatchNorm2d(3)
        self.relu13 = nn.ReLU(inplace=True)
        self.conv14 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn14 = nn.BatchNorm2d(3)
        self.relu14 = nn.ReLU(inplace=True)
        self.conv15 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn15 = nn.BatchNorm2d(3)
        self.relu15 = nn.ReLU(inplace=True)
        self.conv16 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn16 = nn.BatchNorm2d(3)
        self.relu16 = nn.ReLU(inplace=True)
        self.conv17 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn17 = nn.BatchNorm2d(3)
        self.relu17 = nn.ReLU(inplace=True)
        self.conv18 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn18 = nn.BatchNorm2d(3)
        self.relu18 = nn.ReLU(inplace=True)
        self.conv19 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn19 = nn.BatchNorm2d(3)
        self.relu19 = nn.ReLU(inplace=True)
        self.conv20 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn20 = nn.BatchNorm2d(3)
        self.relu20 = nn.ReLU(inplace=True)
        self.conv21 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn21 = nn.BatchNorm2d(3)
        self.relu21 = nn.ReLU(inplace=True)
        self.conv22 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn22 = nn.BatchNorm2d(3)
        self.relu22 = nn.ReLU(inplace=True)
        self.conv23 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn23 = nn.BatchNorm2d(3)
        self.relu23 = nn.ReLU(inplace=True)
        self.conv24 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn24 = nn.BatchNorm2d(3)
        self.relu24 = nn.ReLU(inplace=True)
        self.conv25 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn25 = nn.BatchNorm2d(3)
        self.relu25 = nn.ReLU(inplace=True)
        self.conv26 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn26 = nn.BatchNorm2d(3)
        self.relu26 = nn.ReLU(inplace=True)
        self.conv27 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn27 = nn.BatchNorm2d(3)
        self.relu27 = nn.ReLU(inplace=True)
        self.conv28 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn28 = nn.BatchNorm2d(3)
        self.relu28 = nn.ReLU(inplace=True)
        self.conv29 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn29 = nn.BatchNorm2d(3)
        self.relu29 = nn.ReLU(inplace=True)
        self.conv30 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn30 = nn.BatchNorm2d(3)
        self.relu30 = nn.ReLU(inplace=True)
        self.conv31 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn31 = nn.BatchNorm2d(3)
        self.relu31 = nn.ReLU(inplace=True)
        self.conv32 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn32 = nn.BatchNorm2d(3)
        self.relu32 = nn.ReLU(inplace=True)
        self.conv33 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn33 = nn.BatchNorm2d(3)
        self.relu33 = nn.ReLU(inplace=True)
        self.conv34 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn34 = nn.BatchNorm2d(3)
        self.relu34 = nn.ReLU(inplace=True)
        self.conv35 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn35 = nn.BatchNorm2d(3)
        self.relu35 = nn.ReLU(inplace=True)
        self.conv36 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn36 = nn.BatchNorm2d(3)
        self.relu36 = nn.ReLU(inplace=True)
        self.conv37 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn37 = nn.BatchNorm2d(3)
        self.relu37 = nn.ReLU(inplace=True)
        self.conv38 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn38 = nn.BatchNorm2d(3)
        self.relu38 = nn.ReLU(inplace=True)
        self.conv39 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn39 = nn.BatchNorm2d(3)
        self.relu39 = nn.ReLU(inplace=True)
        self.conv40 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn40 = nn.BatchNorm2d(3)
        self.relu40 = nn.ReLU(inplace=True)
        self.conv41 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn41 = nn.BatchNorm2d(3)
        self.relu41 = nn.ReLU(inplace=True)
        self.conv42 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn42 = nn.BatchNorm2d(3)
        self.relu42 = nn.ReLU(inplace=True)
        self.conv43 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn43 = nn.BatchNorm2d(3)
        self.relu43 = nn.ReLU(inplace=True)
        self.conv44 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn44 = nn.BatchNorm2d(3)
        self.relu44 = nn.ReLU(inplace=True)
        self.conv45 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn45 = nn.BatchNorm2d(3)
        self.relu45 = nn.ReLU(inplace=True)
        self.conv46 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn46 = nn.BatchNorm2d(3)
        self.relu46 = nn.ReLU(inplace=True)
        self.conv47 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn47 = nn.BatchNorm2d(3)
        self.relu47 = nn.ReLU(inplace=True)
        self.conv48 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn48 = nn.BatchNorm2d(3)
        self.relu48 = nn.ReLU(inplace=True)
        self.conv49 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn49 = nn.BatchNorm2d(3)
        self.relu49 = nn.ReLU(inplace=True)
        self.conv50 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn50 = nn.BatchNorm2d(3)
        self.relu50 = nn.ReLU(inplace=True)
        self.conv51 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn51 = nn.BatchNorm2d(3)
        self.relu51 = nn.ReLU(inplace=True)
        self.conv52 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn52 = nn.BatchNorm2d(3)
        self.relu52 = nn.ReLU(inplace=True)
        self.conv53 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn53 = nn.BatchNorm2d(3)
        self.relu53 = nn.ReLU(inplace=True)
        self.conv54 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn54 = nn.BatchNorm2d(3)
        self.relu54 = nn.ReLU(inplace=True)
        self.conv55 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn55 = nn.BatchNorm2d(3)
        self.relu55 = nn.ReLU(inplace=True)
        self.conv56 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn56 = nn.BatchNorm2d(3)
        self.relu56 = nn.ReLU(inplace=True)
        self.conv57 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)