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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
   1. 差异化学习率
   2. 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

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 numpy as np
import time# 设置中文字体支持
plt.rcParams["font.family"] = ["SimHei", "WenQuanYi Micro Hei", "Heiti TC"]
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题# 通道注意力模块
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.shapeavg_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# 空间注意力模块
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)# CBAM模块
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# 自定义ResNet18+CBAM模型
class ResNet18_CBAM(nn.Module):def __init__(self, num_classes=10, pretrained=True, cbam_ratio=16, cbam_kernel=7):super().__init__()self.backbone = models.resnet18(pretrained=pretrained)# 修改首层卷积以适应32x32输入self.backbone.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False)self.backbone.maxpool = nn.Identity()  # 移除原始MaxPool层# 在每个残差块组后添加CBAM模块self.cbam_layer1 = CBAM(in_channels=64, ratio=cbam_ratio, kernel_size=cbam_kernel)self.cbam_layer2 = CBAM(in_channels=128, ratio=cbam_ratio, kernel_size=cbam_kernel)self.cbam_layer3 = CBAM(in_channels=256, ratio=cbam_ratio, kernel_size=cbam_kernel)self.cbam_layer4 = CBAM(in_channels=512, ratio=cbam_ratio, kernel_size=cbam_kernel)# 修改分类头self.backbone.fc = nn.Linear(in_features=512, out_features=num_classes)def forward(self, x):x = self.backbone.conv1(x)x = self.backbone.bn1(x)x = self.backbone.relu(x)x = self.backbone.layer1(x)x = self.cbam_layer1(x)x = self.backbone.layer2(x)x = self.cbam_layer2(x)x = self.backbone.layer3(x)x = self.cbam_layer3(x)x = self.backbone.layer4(x)x = self.cbam_layer4(x)x = self.backbone.avgpool(x)x = torch.flatten(x, 1)x = self.backbone.fc(x)return x# 设置可训练层函数
def set_trainable_layers(model, trainable_parts):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 = Falsefor part in trainable_parts:if part in name:param.requires_grad = Truebreak# 分阶段微调训练函数
def train_staged_finetuning(model, criterion, train_loader, test_loader, device, epochs):optimizer = Noneall_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()# 动态调整学习率和冻结层if epoch == 1:print("\n" + "="*50 + "\n🚀 **阶段 1：训练注意力模块和分类头**\n" + "="*50)set_trainable_layers(model, ["cbam", "backbone.fc"])optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3)elif epoch == 6:print("\n" + "="*50 + "\n✈️ **阶段 2：解冻高层卷积层 (layer3, layer4)**\n" + "="*50)set_trainable_layers(model, ["cbam", "backbone.fc", "backbone.layer3", "backbone.layer4"])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，进行全局微调**\n" + "="*50)for param in model.parameters(): param.requires_grad = Trueoptimizer = optim.Adam(model.parameters(), lr=1e-5)# 训练循环model.train()running_loss, correct, total = 0.0, 0, 0for 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()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()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}')# 修复：使用 len(train_loader) 而不是 lenepoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct / totaltrain_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, 0with 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_testtest_loss_history.append(epoch_test_loss)test_acc_history.append(epoch_test_acc)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}%')# 训练结束后调用绘图函数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# 绘图函数
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))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)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()# 主程序
def main():# 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}")# 数据预处理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))])# 加载数据集print("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)train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)print(f"训练集大小: {len(train_dataset)} | 测试集大小: {len(test_dataset)}")print(f"类别: {train_dataset.classes}")# 初始化模型print("初始化ResNet18+CBAM模型...")model = ResNet18_CBAM().to(device)criterion = nn.CrossEntropyLoss()epochs = 50print("开始使用带分阶段微调策略的ResNet18+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torch.save(model.state_dict(), 'resnet18_cbam_finetuned.pth')print("模型已保存为: resnet18_cbam_finetuned.pth")if __name__ == "__main__":main()

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