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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 models, datasets, transforms
from torch.utils.data import DataLoader
import copy# 定义CBAM模块
class ChannelAttention(nn.Module):def __init__(self, in_channels, reduction_ratio=16):super(ChannelAttention, self).__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.max_pool = nn.AdaptiveMaxPool2d(1)self.fc = nn.Sequential(nn.Conv2d(in_channels, in_channels // reduction_ratio, 1, bias=False),nn.ReLU(),nn.Conv2d(in_channels // reduction_ratio, in_channels, 1, bias=False))self.sigmoid = nn.Sigmoid()def forward(self, x):avg_out = self.fc(self.avg_pool(x))max_out = self.fc(self.max_pool(x))out = avg_out + max_outreturn self.sigmoid(out)class SpatialAttention(nn.Module):def __init__(self, kernel_size=7):super(SpatialAttention, self).__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)out = torch.cat([avg_out, max_out], dim=1)out = self.conv(out)return self.sigmoid(out)class CBAM(nn.Module):def __init__(self, in_channels, reduction_ratio=16, kernel_size=7):super(CBAM, self).__init__()self.channel_att = ChannelAttention(in_channels, reduction_ratio)self.spatial_att = SpatialAttention(kernel_size)def forward(self, x):x = x * self.channel_att(x)x = x * self.spatial_att(x)return x# 修改VGG16模型，插入CBAM模块
class VGG16_CBAM(nn.Module):def __init__(self, num_classes=1000, pretrained=True):super(VGG16_CBAM, self).__init__()# 加载预训练的VGG16vgg16 = models.vgg16(pretrained=pretrained)self.features = vgg16.features# 在每个MaxPool2d后插入CBAM模块new_features = []cbam_idx = 0for module in self.features:new_features.append(module)if isinstance(module, nn.MaxPool2d):# 不在第一个MaxPool后添加CBAMif cbam_idx > 0:in_channels = list(module.parameters())[0].shape[1]new_features.append(CBAM(in_channels))cbam_idx += 1self.features = nn.Sequential(*new_features)self.avgpool = vgg16.avgpoolself.classifier = vgg16.classifier# 修改最后一层以适应指定的类别数if num_classes != 1000:self.classifier[-1] = nn.Linear(self.classifier[-1].in_features, num_classes)def forward(self, x):x = self.features(x)x = self.avgpool(x)x = torch.flatten(x, 1)x = self.classifier(x)return x# 三阶段微调策略
def train_model_three_phase(model, dataloaders, criterion, device, num_epochs=25):# 第一阶段：冻结所有层，只训练分类器print("第一阶段：只训练分类器")for param in model.parameters():param.requires_grad = False# 解冻分类器参数for param in model.classifier.parameters():param.requires_grad = Trueoptimizer = optim.SGD(model.classifier.parameters(), lr=0.001, momentum=0.9)model = train_one_phase(model, dataloaders, criterion, optimizer, device, num_epochs=5)# 第二阶段：解冻部分层 + 分类器，使用差异化学习率print("\n第二阶段：解冻部分层并使用差异化学习率")# 解冻最后两个特征块和CBAM模块for i in range(24, len(model.features)):for param in model.features[i].parameters():param.requires_grad = True# 为不同层设置不同的学习率params_to_update = []# 特征部分学习率低params_to_update.append({'params': [param for param in model.features.parameters() if param.requires_grad],'lr': 0.0001})# 分类器部分学习率高params_to_update.append({'params': model.classifier.parameters(),'lr': 0.001})optimizer = optim.SGD(params_to_update, momentum=0.9)model = train_one_phase(model, dataloaders, criterion, optimizer, device, num_epochs=10)# 第三阶段：解冻所有层，使用低学习率微调整个网络print("\n第三阶段：微调整个网络")for param in model.parameters():param.requires_grad = Trueoptimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.9)model = train_one_phase(model, dataloaders, criterion, optimizer, device, num_epochs=10)return model# 辅助函数：执行一个阶段的训练
def train_one_phase(model, dataloaders, criterion, optimizer, device, num_epochs=5):best_model_wts = copy.deepcopy(model.state_dict())best_acc = 0.0model.to(device)for epoch in range(num_epochs):print(f'Epoch {epoch}/{num_epochs-1}')print('-' * 10)for phase in ['train', 'val']:if phase == 'train':model.train()else:model.eval()running_loss = 0.0running_corrects = 0for inputs, labels in dataloaders[phase]:inputs = inputs.to(device)labels = labels.to(device)optimizer.zero_grad()with torch.set_grad_enabled(phase == 'train'):outputs = model(inputs)_, preds = torch.max(outputs, 1)loss = criterion(outputs, labels)if phase == 'train':loss.backward()optimizer.step()running_loss += loss.item() * inputs.size(0)running_corrects += torch.sum(preds == labels.data)epoch_loss = running_loss / len(dataloaders[phase].dataset)epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')if phase == 'val' and epoch_acc > best_acc:best_acc = epoch_accbest_model_wts = copy.deepcopy(model.state_dict())print()print(f'Best val Acc: {best_acc:4f}')model.load_state_dict(best_model_wts)return model# 数据加载和预处理
def load_data(data_dir):data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),'val': transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),}image_datasets = {x: datasets.ImageFolder(data_dir + x, data_transforms[x]) for x in ['train', 'val']}dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4) for x in ['train', 'val']}dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}class_names = image_datasets['train'].classesreturn dataloaders# 主函数
def main():# 假设数据目录结构为：data/train/ 和 data/val/data_dir = "data/"dataloaders = load_data(data_dir)# 创建模型model = VGG16_CBAM(num_classes=2, pretrained=True)# 定义损失函数和优化器criterion = nn.CrossEntropyLoss()# 设备配置device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")# 执行三阶段微调model_ft = train_model_three_phase(model, dataloaders, criterion, device)# 保存模型torch.save(model_ft.state_dict(), 'vgg16_cbam_finetuned.pth')if __name__ == "__main__":main()    

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