用pytorch基于深度残差能见度检测的代码

以下是一个基于PyTorch的深度残差网络用于能见度检测的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim


class ResNet(nn.Module):
    def __init__(self, num_classes=1):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * 1 * 1, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes),
            )

        layers = []
        layers.append(Bottleneck(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for _ in range(1, blocks):
            layers.append(Bottleneck(self.inplanes, planes))

        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


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


def train(model, dataloader, criterion, optimizer, device):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(dataloader):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    return running_loss / len(dataloader)


def evaluate(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
    with torch.no_grad():
        for i, data in enumerate(dataloader):
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            running_loss += loss.item()

    return running_loss / len(dataloader)


def main():
    # Set up device
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    # Set up data loaders
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=32, shuffle=False)

    # Set up model
    model = ResNet(num_classes=1).to(device)

    # Set up loss function and optimizer
    criterion = nn.MSELoss()
    optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

    # Train and evaluate model
    for epoch in range(num_epochs):
        train_loss = train(model, train_loader, criterion, optimizer, device)
        val_loss = evaluate(model, val_loader, criterion, device)
        print(f"Epoch {epoch+1}: Train Loss = {train_loss:.4f}, Val Loss = {val_loss:.4f}")

    # Save model
    torch.save(model.state_dict(), "model.pth")


if __name__ == "__main__":
    main()

在这个示例代码中，我们使用了一个ResNet网络，它被称为深度残差网络，以进行能见度检测。我们使用了MSE作为损失函数，并使用SGD作为优化器。我们在训练集上训练网络，并在验证集上评估其性能。最后，我们将训练好的模型保存到文件中。请注意，这里的代码只是一个示例，你需要根据你的具体数据集和任务进行相应的修改。