写一个基于pytorch-unet网络的深度学习代码包括模型训练和精度评估模块。

以下是基于PyTorch的Unet网络的深度学习代码，包括模型训练和精度评估模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, 3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.conv(x)
        return x

class UNet(nn.Module):
    def __init__(self, in_channels, out_channels, features=[64, 128, 256, 512]):
        super(UNet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part of U-Net
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part of U-Net
        for feature in reversed(features):
            self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2))
            self.ups.append(DoubleConv(feature*2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []
        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1]

        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:
                x = F.interpolate(x, size=skip_connection.shape[2:], mode='bilinear', align_corners=True)

            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](concat_skip)

        return self.final_conv(x)

def train(model, device, train_loader, criterion, optimizer):
    model.train()
    train_loss = 0.0
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)

    train_loss /= len(train_loader.dataset)
    return train_loss

def evaluate(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0.0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    return test_loss, accuracy

if __name__ == '__main__':
    # Set device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Load data
    train_data = ...
    test_data = ...
    train_loader = DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4)
    test_loader = DataLoader(test_data, batch_size=32, shuffle=False, num_workers=4)

    # Initialize model
    model = UNet(in_channels=3, out_channels=2).to(device)

    # Set criterion and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)

    # Train model
    for epoch in range(10):
        train_loss = train(model, device, train_loader, criterion, optimizer)
        test_loss, accuracy = evaluate(model, device, test_loader, criterion)
        print("Epoch: {}, Train Loss: {:.6f}, Test Loss: {:.6f}, Accuracy: {:.2f}%".format(epoch+1, train_loss, test_loss, accuracy))

在这个代码中，我们定义了一个DoubleConv类，它包含两个卷积层、批归一化和ReLU激活函数。我们还定义了一个UNet类，它实现了U-Net的架构。我们使用nn.ModuleList来存储U-Net的上采样和下采样模块。我们使用train函数来训练模型，该函数使用给定的数据加载器、损失函数和优化器来训练模型。我们使用evaluate函数来评估模型的性能，该函数使用给定的数据加载器和损失函数来计算测试数据的损失和准确性。在主函数中，我们实例化数据加载器、模型、损失函数和优化器，并在训练循环中使用它们来训练和评估模型。