打卡第45天：预训练模型-优快云博客

知识点回顾：
1.预训练的概念
2.常见的分类预训练模型
3.图像预训练模型的发展史
4.预训练的策略
5.预训练代码实战：resnet18

作业：
1.尝试在cifar10对比如下其他的预训练模型，观察差异，尽可能和他人选择的不同
2.尝试通过ctrl进入resnet的内部，观察残差究竟是什么


import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torchvision.models import resnet18, densenet121
from torchsummary import summary  # 查看模型结构
import matplotlib.pyplot as plt
 
# 设备配置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
# CIFAR10 数据预处理
transform = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
train_set = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
test_set = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)
 
class DenseNetC10(nn.Module):
    def __init__(self, num_classes=10):
        super(DenseNetC10, self).__init__()
        # 压缩原版 DenseNet121，减少层数和通道数
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            # 3个密集块，每个块含3层
            self._make_dense_block(32, 32, num_layers=3),
            self._make_dense_block(64, 32, num_layers=3),
            self._make_dense_block(96, 32, num_layers=3),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1))
        )
        self.classifier = nn.Linear(128, num_classes)
    
    def _make_dense_block(self, in_channels, growth_rate, num_layers):
        layers = []
        for _ in range(num_layers):
            layers.append(nn.Conv2d(in_channels, growth_rate, kernel_size=3, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(growth_rate))
            layers.append(nn.ReLU(inplace=True))
            in_channels += growth_rate
        return nn.Sequential(*layers)
    
    def forward(self, x):
        features = self.features(x)
        out = features.view(features.size(0), -1)
        out = self.classifier(out)
        return out
 
# 初始化模型
models = {
    'DenseNet-C10': DenseNetC10().to(device),
    'MobileViT': MobileViT().to(device),
    'RepVGG': RepVGG().to(device),
    'ResNet18': resnet18(pretrained=False, num_classes=10).to(device)  # 对比基准
}
 
# 训练超参数
criterion = nn.CrossEntropyLoss()
accuracies = {}
 
for model_name, model in models.items():
    print(f'\nTraining {model_name}...')
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
    best_acc = 0.0
    for epoch in range(1, 201):
        train_model(model, criterion, optimizer, epoch)
        acc = test_model(model, criterion)
        if acc > best_acc:
            best_acc = acc
    accuracies[model_name] = best_acc
 
# 打印对比结果
print('\nFinal Accuracy Comparison:')
for name, acc in accuracies.items():
    print(f'{name}: {acc:.2f}%')
 
def visualize_residual(model, data):
    # 注册钩子函数捕捉残差块输出
    residuals = []
    def hook(module, input, output):
        residual = output - input[0]  # 残差 = 输出 - 输入
        residuals.append(residual.detach().cpu())
    
    # 选择ResNet18的第一个残差块（layer1[0]）
    model.layer1[0].register_forward_hook(hook)
    with torch.no_grad():
        model(data.to(device))
    
    # 可视化残差图（取第一个样本的第一个通道）
    residual = residuals[0][0, 0, :, :]  # 形状(32,32)
    plt.figure(figsize=(6, 4))
    plt.subplot(1, 2, 1)
    plt.imshow(data[0].permute(1, 2, 0))  # 原始图像
    plt.title('Input Image')
    plt.subplot(1, 2, 2)
    plt.imshow(residual, cmap='coolwarm')  # 残差热力图
    plt.title('Residual Map')
    plt.colorbar()
    plt.show()
 
# 测试残差可视化（用ResNet18和测试集中的一张图像）
resnet_model = resnet18(num_classes=10).to(device)
data, _ = next(iter(test_loader))
visualize_residual(resnet_model, data[:1])  # 取第一个样本

使用ResNet50模型，结果