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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 time# 通道注意力机制
class ChannelAttentionModule(nn.Module):def __init__(self, channels, reduction=16):super().__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.max_pool = nn.AdaptiveMaxPool2d(1)self.mlp = nn.Sequential(nn.Linear(channels, channels // reduction, bias=False),nn.ReLU(),nn.Linear(channels // reduction, channels, bias=False))self.sigmoid = nn.Sigmoid()def forward(self, x):b, c, _, _ = x.size()avg_features = self.mlp(self.avg_pool(x).view(b, c))max_features = self.mlp(self.max_pool(x).view(b, c))weights = self.sigmoid(avg_features + max_features).view(b, c, 1, 1)return x * weights# 空间注意力机制
class SpatialAttentionModule(nn.Module):def __init__(self, kernel=7):super().__init__()self.conv = nn.Conv2d(2, 1, kernel, padding=kernel//2, bias=False)self.sigmoid = nn.Sigmoid()def forward(self, x):avg_features = torch.mean(x, dim=1, keepdim=True)max_features, _ = torch.max(x, dim=1, keepdim=True)combined = torch.cat([avg_features, max_features], dim=1)spatial_weights = self.sigmoid(self.conv(combined))return x * spatial_weights# 结合通道和空间注意力
class CBAMBlock(nn.Module):def __init__(self, channels, reduction=16, kernel=7):super().__init__()self.channel_attention = ChannelAttentionModule(channels, reduction)self.spatial_attention = SpatialAttentionModule(kernel)def forward(self, x):x = self.channel_attention(x)x = self.spatial_attention(x)return x# 配置绘图环境
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False# 设置计算设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")# 数据预处理
train_augmentation = 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
cifar_train = datasets.CIFAR10('./data', train=True, download=True, transform=train_augmentation)
cifar_test = datasets.CIFAR10('./data', train=False, transform=test_transform)
train_loader = DataLoader(cifar_train, batch_size=64, shuffle=True)
test_loader = DataLoader(cifar_test, batch_size=64, shuffle=False)# 增强型ResNet模型
class EnhancedResNet(nn.Module):def __init__(self, num_classes=10, pretrained=True, reduction=16, kernel=7):super().__init__()# 加载预训练模型base_model = models.resnet18(pretrained=pretrained)# 调整输入层适应小尺寸图像base_model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)base_model.maxpool = nn.Identity()# 添加注意力模块self.attention1 = CBAMBlock(64, reduction, kernel)self.attention2 = CBAMBlock(128, reduction, kernel)self.attention3 = CBAMBlock(256, reduction, kernel)self.attention4 = CBAMBlock(512, reduction, kernel)# 替换分类层base_model.fc = nn.Linear(512, num_classes)self.base = base_modeldef forward(self, x):x = self.base.conv1(x)x = self.base.bn1(x)x = self.base.relu(x)# 残差块与注意力模块交替x = self.base.layer1(x)x = self.attention1(x)x = self.base.layer2(x)x = self.attention2(x)x = self.base.layer3(x)x = self.attention3(x)x = self.base.layer4(x)x = self.attention4(x)# 分类输出x = self.base.avgpool(x)x = torch.flatten(x, 1)return self.base.fc(x)# 配置模型训练参数
def configure_optimizer(model, stage):if stage == 1:for param in model.parameters():param.requires_grad = Falsefor name, param in model.named_parameters():if "attention" in name or "fc" in name:param.requires_grad = Truereturn optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3)elif stage == 2:for name, param in model.named_parameters():if "layer3" in name or "layer4" in name or "attention" in name or "fc" in name:param.requires_grad = Truereturn optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4)else:  # stage 3for param in model.parameters():param.requires_grad = Truereturn optim.Adam(model.parameters(), lr=1e-5)# 训练和验证过程
def run_training(model, criterion, train_loader, test_loader, device, total_epochs):batch_losses = []epoch_losses = []train_acc_history = []test_acc_history = []optimizer = Nonefor epoch in range(1, total_epochs + 1):start_time = time.time()# 分阶段配置优化器if epoch == 1:print("\n" + "="*50 + "\n阶段1：训练注意力模块和分类层\n" + "="*50)optimizer, lr = configure_optimizer(model, 1)elif epoch == 6:print("\n" + "="*50 + "\n阶段2：解冻高层卷积层\n" + "="*50)optimizer, lr = configure_optimizer(model, 2)elif epoch == 21:print("\n" + "="*50 + "\n阶段3：全局微调\n" + "="*50)optimizer, lr = configure_optimizer(model, 3)# 训练阶段model.train()running_loss = 0.0correct = 0total_samples = 0for batch_idx, (inputs, targets) in enumerate(train_loader):inputs, targets = inputs.to(device), targets.to(device)optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, targets)loss.backward()optimizer.step()# 记录损失current_loss = loss.item()batch_losses.append(current_loss)running_loss += current_loss# 计算准确率_, predicted = outputs.max(1)total_samples += targets.size(0)correct += predicted.eq(targets).sum().item()# 定期打印进度if (batch_idx + 1) % 100 == 0:avg_loss = running_loss / (batch_idx + 1)print(f'周期: {epoch}/{total_epochs} | 批次: {batch_idx+1}/{len(train_loader)} 'f'| 当前损失: {current_loss:.4f} | 平均损失: {avg_loss:.4f}')# 计算训练统计train_loss = running_loss / len(train_loader)train_acc = 100. * correct / total_samplesepoch_losses.append(train_loss)train_acc_history.append(train_acc)# 验证阶段model.eval()test_loss = 0.0test_correct = 0test_total = 0with torch.no_grad():for inputs, targets in test_loader:inputs, targets = inputs.to(device), targets.to(device)outputs = model(inputs)test_loss += criterion(outputs, targets).item()_, predicted = outputs.max(1)test_total += targets.size(0)test_correct += predicted.eq(targets).sum().item()test_loss /= len(test_loader)test_acc = 100. * test_correct / test_totaltest_acc_history.append(test_acc)# 打印周期结果epoch_time = time.time() - start_timeprint(f'周期 {epoch}/{total_epochs} 完成 | 用时: {epoch_time:.2f}s | 'f'训练准确率: {train_acc:.2f}% | 测试准确率: {test_acc:.2f}%')# 可视化结果visualize_results(batch_losses, epoch_losses, train_acc_history, test_acc_history)return test_acc_history[-1]# 结果可视化
def visualize_results(batch_losses, epoch_losses, train_acc, test_acc):plt.figure(figsize=(15, 5))# 批次损失plt.subplot(1, 3, 1)plt.plot(batch_losses, 'b-', alpha=0.7)plt.xlabel('训练批次')plt.ylabel('损失值')plt.title('批次训练损失')plt.grid(True)# 周期损失plt.subplot(1, 3, 2)plt.plot(epoch_losses, 'r-')plt.xlabel('训练周期')plt.ylabel('平均损失')plt.title('周期训练损失')plt.grid(True)# 准确率曲线plt.subplot(1, 3, 3)plt.plot(train_acc, 'g-', label='训练准确率')plt.plot(test_acc, 'b-', label='测试准确率')plt.xlabel('训练周期')plt.ylabel('准确率 (%)')plt.title('训练和测试准确率')plt.legend()plt.grid(True)plt.tight_layout()plt.show()# 主执行流程
if __name__ == "__main__":# 初始化模型net = EnhancedResNet().to(device)loss_fn = nn.CrossEntropyLoss()print("开始训练增强型ResNet模型...")final_acc = run_training(net, loss_fn, train_loader, test_loader, device, 50)print(f"训练完成! 最终测试准确率: {final_acc:.2f}%")# 保存模型torch.save(net.state_dict(), 'enhanced_resnet_cifar10.pth')print("模型已保存至: enhanced_resnet_cifar10.pth")

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