用pytorch基于深度残差对图片能见度检测的代码

以下是基于PyTorch实现的深度残差网络用于图像能见度检测的代码。

首先，我们需要导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
import os

接下来，我们需要定义一个数据集类，以便读取并处理数据：

class VisibilityDataset(Dataset):
    def __init__(self, image_dir, label_dir, transform=None):
        self.image_dir = image_dir
        self.label_dir = label_dir
        self.transform = transform
        self.image_files = os.listdir(image_dir)

    def __len__(self):
        return len(self.image_files)

    def __getitem__(self, idx):
        image_path = os.path.join(self.image_dir, self.image_files[idx])
        label_path = os.path.join(self.label_dir, self.image_files[idx][:-4] + '_visibility.png')
        image = Image.open(image_path).convert('RGB')
        label = Image.open(label_path).convert('L')
        if self.transform:
            image = self.transform(image)
        return image, label

然后，我们需要定义一个深度残差网络模型：

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.layer1 = self._make_layer(64, 2)
        self.layer2 = self._make_layer(128, 2, stride=2)
        self.layer3 = self._make_layer(256, 2, stride=2)
        self.layer4 = self._make_layer(512, 2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, 1)

    def _make_layer(self, planes, blocks, stride=1):
        layers = []
        layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(planes))
        layers.append(nn.ReLU(inplace=True))
        for i in range(1, blocks):
            layers.append(nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(planes))
            layers.append(nn.ReLU(inplace=True))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

接下来，我们需要设置一些超参数：

batch_size = 32
learning_rate = 0.001
num_epochs = 10

然后，我们需要定义数据预处理：

transform = transforms.Compose([
    transforms.Resize((256,256)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

接下来，我们可以初始化数据集并分割数据集：

data_dir = './data'
train_image_dir = os.path.join(data_dir, 'train_images')
train_label_dir = os.path.join(data_dir, 'train_labels')
test_image_dir = os.path.join(data_dir, 'test_images')
test_label_dir = os.path.join(data_dir, 'test_labels')

train_dataset = VisibilityDataset(train_image_dir, train_label_dir, transform=transform)
test_dataset = VisibilityDataset(test_image_dir, test_label_dir, transform=transform)

train_size = int(0.8 * len(train_dataset))
val_size = len(train_dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, [train_size, val_size])

接下来，我们可以初始化数据加载器：

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

然后，我们可以初始化模型、损失函数和优化器：

model = ResNet()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

接下来，我们可以训练模型并在验证集上进行评估：

for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels.float())
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 10 == 9:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

    model.eval()
    val_loss = 0.0
    with torch.no_grad():
        for images, labels in val_loader:
            outputs = model(images)
            loss = criterion(outputs, labels.float())
            val_loss += loss.item()
    print('Epoch %d: validation loss: %.3f' % (epoch + 1, val_loss / len(val_loader)))

print('Finished training')

最后，我们可以在测试集上进行评估：

model.eval()
test_loss = 0.0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        loss = criterion(outputs, labels.float())
        test_loss += loss.item()
print('Test loss: %.3f' % (test_loss / len(test_loader)))

这就是基于PyTorch实现的深度残差网络用于图像能见度检测的代码。