Day44

m0_704887892025-12-25 12:36

DAY 44 预训练模型+CBAM 模块

知识点回顾：

  1. resnet结构解析

  2. CBAM 放置位置的思考

  3. 针对预训练模型的训练策略

a. 差异化学学习率

b. 三阶段微调

作业:

  1. 好好理解下 resnet18 的模型结构

  2. 尝试对 vgg16+cbam 进行微调策略

python 复制代码 
# ===================== 1. 环境准备与库导入 =====================
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
import time

# 设置中文字体
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False

# 设备配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")

# ===================== 2. CBAM模块定义（复用经典实现） =====================
class ChannelAttention(nn.Module):
    def __init__(self, in_channels, ratio=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // ratio, bias=False),
            nn.ReLU(),
            nn.Linear(in_channels // ratio, in_channels, bias=False)
        )
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        b, c, h, w = x.shape
        avg_out = self.fc(self.avg_pool(x).view(b, c))
        max_out = self.fc(self.max_pool(x).view(b, c))
        attention = self.sigmoid(avg_out + max_out).view(b, c, 1, 1)
        return x * attention

class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super().__init__()
        self.conv = nn.Conv2d(2, 1, kernel_size, padding=kernel_size//2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        pool_out = torch.cat([avg_out, max_out], dim=1)
        attention = self.conv(pool_out)
        return x * self.sigmoid(attention)

class CBAM(nn.Module):
    def __init__(self, in_channels, ratio=16, kernel_size=7):
        super().__init__()
        self.channel_attn = ChannelAttention(in_channels, ratio)
        self.spatial_attn = SpatialAttention(kernel_size)

    def forward(self, x):
        x = self.channel_attn(x)
        x = self.spatial_attn(x)
        return x

# ===================== 3. VGG16+CBAM模型构建 =====================
class VGG16_CBAM(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        # 加载预训练VGG16（去掉分类头）
        vgg16_pretrained = torchvision.models.vgg16(pretrained=True)
        self.features = vgg16_pretrained.features  # VGG16特征提取层
        
        # 在VGG16的5个卷积块后插入CBAM（适配VGG16的features结构）
        # VGG16 features结构：block1(0-4), block2(5-9), block3(10-16), block4(17-23), block5(24-30)
        self.cbam1 = CBAM(in_channels=64)   # block1输出通道64
        self.cbam2 = CBAM(in_channels=128)  # block2输出通道128
        self.cbam3 = CBAM(in_channels=256)  # block3输出通道256
        self.cbam4 = CBAM(in_channels=512)  # block4输出通道512
        self.cbam5 = CBAM(in_channels=512)  # block5输出通道512
        
        # 替换VGG16的分类头（适配CIFAR10的10分类）
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),  # CIFAR10经VGG16 features后尺寸为1x1
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        # 前向传播：VGG16卷积块 + 对应CBAM
        x = self.features[:5](x)    # block1
        x = self.cbam1(x)
        x = self.features[5:10](x)  # block2
        x = self.cbam2(x)
        x = self.features[10:17](x) # block3
        x = self.cbam3(x)
        x = self.features[17:24](x) # block4
        x = self.cbam4(x)
        x = self.features[24:](x)   # block5
        x = self.cbam5(x)
        
        # 展平+分类头
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# ===================== 4. 分阶段微调核心函数 =====================
def set_trainable_layers(model, trainable_parts):
    """
    冻结所有层，仅解冻包含trainable_parts关键词的层
    """
    print(f"\n---> 解冻以下部分并设为可训练: {trainable_parts}")
    for name, param in model.named_parameters():
        param.requires_grad = False  # 先全冻结
        for part in trainable_parts:
            if part in name:
                param.requires_grad = True
                break

def train_staged_finetuning(model, criterion, train_loader, test_loader, device, epochs):
    optimizer = None
    # 初始化历史记录
    all_iter_losses, iter_indices = [], []
    train_acc_history, test_acc_history = [], []
    train_loss_history, test_loss_history = [], []

    for epoch in range(1, epochs + 1):
        epoch_start_time = time.time()
        
        # --- 分阶段调整冻结层和学习率 ---
        if epoch == 1:
            print("\n" + "="*50 + "\n🚀 **阶段 1：训练CBAM模块和分类头**\n" + "="*50)
            # 解冻CBAM（cbam1-cbam5）和分类头（classifier）
            set_trainable_layers(model, ["cbam", "classifier"])
            optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3)
        elif epoch == 6:
            print("\n" + "="*50 + "\n✈️ **阶段 2：解冻VGG16高层卷积（features.24:）**\n" + "="*50)
            # 解冻CBAM + 分类头 + VGG16 block5（features.24后）
            set_trainable_layers(model, ["cbam", "classifier", "features.24", "features.25", "features.26", "features.27", "features.28", "features.29", "features.30"])
            optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)
        elif epoch == 21:
            print("\n" + "="*50 + "\n🛰️ **阶段 3：解冻所有层，全局微调**\n" + "="*50)
            # 解冻所有层
            for param in model.parameters():
                param.requires_grad = True
            optimizer = optim.Adam(model.parameters(), lr=1e-5)
        
        # --- 训练循环 ---
        model.train()
        running_loss, correct, total = 0.0, 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()
            
            # 记录每个iteration的损失
            iter_loss = loss.item()
            all_iter_losses.append(iter_loss)
            iter_indices.append((epoch - 1) * len(train_loader) + batch_idx + 1)
            
            running_loss += iter_loss
            _, predicted = output.max(1)
            total += target.size(0)
            correct += predicted.eq(target).sum().item()
            
            # 每100个batch打印一次
            if (batch_idx + 1) % 100 == 0:
                print(f'Epoch: {epoch}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} '
                      f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}')
        
        # 计算epoch级训练指标
        epoch_train_loss = running_loss / len(train_loader)
        epoch_train_acc = 100. * correct / total
        train_loss_history.append(epoch_train_loss)
        train_acc_history.append(epoch_train_acc)

        # --- 测试循环 ---
        model.eval()
        test_loss, correct_test, total_test = 0, 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()
        
        # 计算epoch级测试指标
        epoch_test_loss = test_loss / len(test_loader)
        epoch_test_acc = 100. * correct_test / total_test
        test_loss_history.append(epoch_test_loss)
        test_acc_history.append(epoch_test_acc)
        
        # 打印epoch结果
        print(f'Epoch {epoch}/{epochs} 完成 | 耗时: {time.time() - epoch_start_time:.2f}s | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%')
    
    # 训练结束绘图
    print("\n训练完成! 开始绘制结果图表...")
    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

# ===================== 5. 可视化函数 =====================
def plot_iter_losses(losses, indices):
    plt.figure(figsize=(10, 4))
    plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss')
    plt.xlabel('Iteration（Batch序号）')
    plt.ylabel('损失值')
    plt.title('每个 Iteration 的训练损失')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):
    epochs = range(1, len(train_acc) + 1)
    plt.figure(figsize=(12, 4))
    
    # 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('训练和测试准确率')
    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('训练和测试损失')
    plt.legend()
    plt.grid(True)
    
    plt.tight_layout()
    plt.show()

# ===================== 6. 数据加载（CIFAR10） =====================
# 数据预处理（适配VGG16的输入要求）
transform_train = transforms.Compose([
    transforms.Resize((224, 224)),  # VGG16默认输入224x224
    transforms.RandomHorizontalFlip(),
    transforms.RandomCrop(224, padding=4),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # ImageNet归一化
])

transform_test = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# 加载CIFAR10数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform_test)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=2)

# ===================== 7. 执行训练 =====================
if __name__ == "__main__":
    # 初始化模型
    model = VGG16_CBAM(num_classes=10).to(device)
    criterion = nn.CrossEntropyLoss()
    epochs = 50

    # 开始分阶段微调
    print("开始使用带分阶段微调策略的VGG16+CBAM模型进行训练...")
    final_accuracy = train_staged_finetuning(model, criterion, train_loader, test_loader, device, epochs)
    print(f"训练完成！最终测试准确率: {final_accuracy:.2f}%")

    # 保存模型（可选）
    # torch.save(model.state_dict(), 'vgg16_cbam_finetuned.pth')
    # print("模型已保存为: vgg16_cbam_finetuned.pth")

复习日

作业: day43的时候我们安排大家对自己找的数据集用简单cnn训练,现在可以尝试下借助这几天的知识来实现精度的进一步提高

#神经网络调参指南

知识点回顾:

  1. 随机种子

  2. 内参的初始化

  3. 神经网络调参指南

a. 参数的分类

b. 调参的顺序

c. 各部分参数的调整心得

作业: 对于 day41 的简单 cnn，看看是否可以借助调参指南进一步提高精度。

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