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# 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

# ===================== 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 * 1 * 1, 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")

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