使用DenseNet预训练模型对cifar10数据集进行训练

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import os

# 设置中文字体支持
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

# 检查GPU是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")

# 1. 数据预处理（训练集增强，测试集标准化）
train_transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
    transforms.RandomRotation(15),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])

test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
])

# 2. 加载CIFAR-10数据集
train_dataset = datasets.CIFAR10(
    root='./data',
    train=True,
    download=True,
    transform=train_transform
)

test_dataset = datasets.CIFAR10(
    root='./data',
    train=False,
    transform=test_transform
)

# 3. 创建数据加载器
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 4. 定义DenseNet模型
def create_densenet(pretrained=True, num_classes=10):
    # 修改为加载DenseNet121预训练模型
    model = models.densenet121(pretrained=pretrained)
    
    # 修改最后一层全连接层
    in_features = model.classifier.in_features
    model.classifier = nn.Linear(in_features, num_classes)
    
    return model.to(device)

# 5. 冻结/解冻模型层的函数
def freeze_model(model, freeze=True):
    """冻结或解冻模型的卷积层参数"""
    # 冻结/解冻除classifier层外的所有参数
    for name, param in model.named_parameters():
        if 'classifier' not in name:
            param.requires_grad = not freeze
    
    # 打印冻结状态
    frozen_params = sum(p.numel() for p in model.parameters() if not p.requires_grad)
    total_params = sum(p.numel() for p in model.parameters())
    
    if freeze:
        print(f"已冻结模型卷积层参数 ({frozen_params}/{total_params} 参数)")
    else:
        print(f"已解冻模型所有参数 ({total_params}/{total_params} 参数可训练)")
    
    return model

# 6. 训练函数（支持阶段式训练）
def train_with_freeze_schedule(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs, freeze_epochs=5):
    """
    前freeze_epochs轮冻结卷积层，之后解冻所有层进行训练
    """
    train_loss_history = []
    test_loss_history = []
    train_acc_history = []
    test_acc_history = []
    all_iter_losses = []
    iter_indices = []
    
    # 初始冻结卷积层
    if freeze_epochs > 0:
        model = freeze_model(model, freeze=True)
    
    for epoch in range(epochs):
        # 解冻控制：在指定轮次后解冻所有层
        if epoch == freeze_epochs:
            model = freeze_model(model, freeze=False)
            # 解冻后调整优化器（可选）
            optimizer.param_groups[0]['lr'] = 1e-4  # 降低学习率防止过拟合
        
        model.train()  # 设置为训练模式
        running_loss = 0.0
        correct_train = 0
        total_train = 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()
            
            # 记录Iteration损失
            iter_loss = loss.item()
            all_iter_losses.append(iter_loss)
            iter_indices.append(epoch * len(train_loader) + batch_idx + 1)
            
            # 统计训练指标
            running_loss += iter_loss
            _, predicted = output.max(1)
            total_train += target.size(0)
            correct_train += predicted.eq(target).sum().item()
            
            # 每100批次打印进度
            if (batch_idx + 1) % 100 == 0:
                print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx+1}/{len(train_loader)} "
                      f"| 单Batch损失: {iter_loss:.4f}")
        
        # 计算 epoch 级指标
        epoch_train_loss = running_loss / len(train_loader)
        epoch_train_acc = 100. * correct_train / total_train
        
        # 测试阶段
        model.eval()
        correct_test = 0
        total_test = 0
        test_loss = 0.0
        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()
                _, predicted = output.max(1)
                total_test += target.size(0)
                correct_test += predicted.eq(target).sum().item()
        
        epoch_test_loss = test_loss / len(test_loader)
        epoch_test_acc = 100. * correct_test / total_test
        
        # 记录历史数据
        train_loss_history.append(epoch_train_loss)
        test_loss_history.append(epoch_test_loss)
        train_acc_history.append(epoch_train_acc)
        test_acc_history.append(epoch_test_acc)
        
        # 更新学习率调度器
        if scheduler is not None:
            scheduler.step(epoch_test_loss)
        
        # 打印 epoch 结果
        print(f"Epoch {epoch+1} 完成 | 训练损失: {epoch_train_loss:.4f} "
              f"| 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%")
    
    # 绘制损失和准确率曲线
    plot_iter_losses(all_iter_losses, iter_indices)
    plot_epoch_metrics(train_acc_history, test_acc_history, train_loss_history, test_loss_history)
    
    return epoch_test_acc  # 返回最终测试准确率

# 7. 绘制Iteration损失曲线
def plot_iter_losses(losses, indices):
    plt.figure(figsize=(10, 4))
    plt.plot(indices, losses, 'b-', alpha=0.7)
    plt.xlabel('Iteration（Batch序号）')
    plt.ylabel('损失值')
    plt.title('训练过程中的Iteration损失变化')
    plt.grid(True)
    plt.show()

# 8. 绘制Epoch级指标曲线
def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):
    epochs = range(1, len(train_acc) + 1)
    
    plt.figure(figsize=(12, 5))
    
    # 准确率曲线
    plt.subplot(1, 2, 1)
    plt.plot(epochs, train_acc, 'b-', label='训练准确率')
    plt.plot(epochs, test_acc, 'r-', label='测试准确率')
    plt.xlabel('Epoch')
    plt.ylabel('准确率 (%)')
    plt.title('准确率随Epoch变化')
    plt.legend()
    plt.grid(True)
    
    # 损失曲线
    plt.subplot(1, 2, 2)
    plt.plot(epochs, train_loss, 'b-', label='训练损失')
    plt.plot(epochs, test_loss, 'r-', label='测试损失')
    plt.xlabel('Epoch')
    plt.ylabel('损失值')
    plt.title('损失值随Epoch变化')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

# 主函数：训练模型
def main():
    # 参数设置
    epochs = 40  # 总训练轮次
    freeze_epochs = 5  # 冻结卷积层的轮次
    learning_rate = 1e-3  # 初始学习率
    weight_decay = 1e-4  # 权重衰减
    
    # 创建DenseNet模型（加载预训练权重）
    model = create_densenet(pretrained=True, num_classes=10)
    
    # 定义优化器和损失函数
    optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
    criterion = nn.CrossEntropyLoss()
    
    # 定义学习率调度器
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(
        optimizer, mode='min', factor=0.5, patience=2
    )
    
    # 开始训练（前5轮冻结卷积层，之后解冻）
    final_accuracy = train_with_freeze_schedule(
        model=model,
        train_loader=train_loader,
        test_loader=test_loader,
        criterion=criterion,
        optimizer=optimizer,
        scheduler=scheduler,
        device=device,
        epochs=epochs,
        freeze_epochs=freeze_epochs
    )
    
    print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")
    
    # # 保存模型
    # torch.save(model.state_dict(), 'resnet18_cifar10_finetuned.pth')
    # print("模型已保存至: resnet18_cifar10_finetuned.pth")

if __name__ == "__main__":
    main()

注意：

1.DenseNet 最后一层全连接层的名称是 classifier ，并非 fc 因此在 freeze_model 函数里，要注意改一下名。

2.残差连接

在传统的神经网络中，每一层的输出都是对上一层输入进行一系列非线性变换的结果。而在残差网络（ResNet）中，引入了残差块（Residual Block），残差块通过残差连接将输入直接加到经过非线性变换后的输出上。

假设一个神经网络层的输入为 $x$，期望学习的映射为 $H(x)$，在传统网络中，该层需要直接学习 $H(x)$。而在残差网络中，将该层改为学习残差函数 $F(x) = H(x) - x$，则输出变为 $y = F(x) + x$，这里的 $x$ 到 $y$ 的连接就是残差连接。

@浙大疏锦行