打卡day44

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 torch.utils.data import DataLoader
   import matplotlib.pyplot as plt

   设置中文字体支持

   plt.rcParams["font.family"] = ["SimHei"]
   plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题

   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}")

   1. 数据预处理（训练集增强，测试集标准化）

   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))
   ])

   2. 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
   )

   3. 创建数据加载器（可调整batch_size）

   batch_size = 64
   train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
   test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

   4. 训练函数（支持学习率调度器）

   def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs):
   model.train() # 设置为训练模式
   train_loss_history = []
   test_loss_history = []
   train_acc_history = []
   test_acc_history = []
   all_iter_losses = []
   iter_indices = []

    for epoch in range(epochs):
        running_loss = 0.0
        correct_train = 0
        total_train = 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Iteration损失
            iter_loss = loss.item()
            all_iter_losses.append(iter_loss)
            iter_indices.append(epoch * len(train_loader) + batch_idx + 1)
            
            # 统计训练指标
            running_loss += iter_loss
            _, predicted = output.max(1)
            total_train += target.size(0)
            correct_train += predicted.eq(target).sum().item()
            
            # 每100批次打印进度
            if (batch_idx + 1) % 100 == 0:
                print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx+1}/{len(train_loader)} "
                      f"| 单Batch损失: {iter_loss:.4f}")
        
        # 计算 epoch 级指标
        epoch_train_loss = running_loss / len(train_loader)
        epoch_train_acc = 100. * correct_train / total_train
        
        # 测试阶段
        model.eval()
        correct_test = 0
        total_test = 0
        test_loss = 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()
        
        epoch_test_loss = test_loss / len(test_loader)
        epoch_test_acc = 100. * correct_test / total_test
        
        # 记录历史数据
        train_loss_history.append(epoch_train_loss)
        test_loss_history.append(epoch_test_loss)
        train_acc_history.append(epoch_train_acc)
        test_acc_history.append(epoch_test_acc)
        
        # 更新学习率调度器
        if scheduler is not None:
            scheduler.step(epoch_test_loss)
        
        # 打印 epoch 结果
        print(f"Epoch {epoch+1} 完成 | 训练损失: {epoch_train_loss:.4f} "
              f"| 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%")
    
    # 绘制损失和准确率曲线
    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  # 返回最终测试准确率

   5. 绘制Iteration损失曲线

   def plot_iter_losses(losses, indices):
   plt.figure(figsize=(10, 4))
   plt.plot(indices, losses, 'b-', alpha=0.7)
   plt.xlabel('Iteration（Batch序号）')
   plt.ylabel('损失值')
   plt.title('训练过程中的Iteration损失变化')
   plt.grid(True)
   plt.show()

   6. 绘制Epoch级指标曲线

   def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):
   epochs = range(1, len(train_acc) + 1)

    plt.figure(figsize=(12, 5))
    
    # 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('准确率随Epoch变化')
    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('损失值随Epoch变化')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

   导入ResNet模型

   from torchvision.models import resnet18

   定义ResNet18模型（支持预训练权重加载）

   def create_resnet18(pretrained=True, num_classes=10):
   # 加载预训练模型（ImageNet权重）
   model = resnet18(pretrained=pretrained)

    # 修改最后一层全连接层，适配CIFAR-10的10分类任务
    in_features = model.fc.in_features
    model.fc = nn.Linear(in_features, num_classes)
    
    # 将模型转移到指定设备（CPU/GPU）
    model = model.to(device)
    return model

   创建ResNet18模型（加载ImageNet预训练权重，不进行微调）

   model = create_resnet18(pretrained=True, num_classes=10)
   model.eval() # 设置为推理模式

   测试单张图片（示例）

   from torchvision import utils

   从测试数据集中获取一张图片

   dataiter = iter(test_loader)
   images, labels = next(dataiter)
   images = images[:1].to(device) # 取第1张图片

   前向传播

   with torch.no_grad():
   outputs = model(images)
   _, predicted = torch.max(outputs.data, 1)

   显示图片和预测结果

   plt.imshow(utils.make_grid(images.cpu(), normalize=True).permute(1, 2, 0))
   plt.title(f"预测类别: {predicted.item()}")
   plt.axis('off')
   plt.show()

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 os

# 设置中文字体支持
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

# 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}")

# 1. 数据预处理（训练集增强，测试集标准化）
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))
])

# 2. 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
)

# 3. 创建数据加载器
batch_size = 64
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 4. 定义ResNet18模型
def create_resnet18(pretrained=True, num_classes=10):
    model = models.resnet18(pretrained=pretrained)
    
    # 修改最后一层全连接层
    in_features = model.fc.in_features
    model.fc = nn.Linear(in_features, num_classes)
    
    return model.to(device)

# 5. 冻结/解冻模型层的函数
def freeze_model(model, freeze=True):
    """冻结或解冻模型的卷积层参数"""
    # 冻结/解冻除fc层外的所有参数
    for name, param in model.named_parameters():
        if 'fc' not in name:
            param.requires_grad = not freeze
    
    # 打印冻结状态
    frozen_params = sum(p.numel() for p in model.parameters() if not p.requires_grad)
    total_params = sum(p.numel() for p in model.parameters())
    
    if freeze:
        print(f"已冻结模型卷积层参数 ({frozen_params}/{total_params} 参数)")
    else:
        print(f"已解冻模型所有参数 ({total_params}/{total_params} 参数可训练)")
    
    return model

# 6. 训练函数（支持阶段式训练）
def train_with_freeze_schedule(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs, freeze_epochs=5):
    """
    前freeze_epochs轮冻结卷积层，之后解冻所有层进行训练
    """
    train_loss_history = []
    test_loss_history = []
    train_acc_history = []
    test_acc_history = []
    all_iter_losses = []
    iter_indices = []
    
    # 初始冻结卷积层
    if freeze_epochs > 0:
        model = freeze_model(model, freeze=True)
    
    for epoch in range(epochs):
        # 解冻控制：在指定轮次后解冻所有层
        if epoch == freeze_epochs:
            model = freeze_model(model, freeze=False)
            # 解冻后调整优化器（可选）
            optimizer.param_groups[0]['lr'] = 1e-4  # 降低学习率防止过拟合
        
        model.train()  # 设置为训练模式
        running_loss = 0.0
        correct_train = 0
        total_train = 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Iteration损失
            iter_loss = loss.item()
            all_iter_losses.append(iter_loss)
            iter_indices.append(epoch * len(train_loader) + batch_idx + 1)
            
            # 统计训练指标
            running_loss += iter_loss
            _, predicted = output.max(1)
            total_train += target.size(0)
            correct_train += predicted.eq(target).sum().item()
            
            # 每100批次打印进度
            if (batch_idx + 1) % 100 == 0:
                print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx+1}/{len(train_loader)} "
                      f"| 单Batch损失: {iter_loss:.4f}")
        
        # 计算 epoch 级指标
        epoch_train_loss = running_loss / len(train_loader)
        epoch_train_acc = 100. * correct_train / total_train
        
        # 测试阶段
        model.eval()
        correct_test = 0
        total_test = 0
        test_loss = 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()
        
        epoch_test_loss = test_loss / len(test_loader)
        epoch_test_acc = 100. * correct_test / total_test
        
        # 记录历史数据
        train_loss_history.append(epoch_train_loss)
        test_loss_history.append(epoch_test_loss)
        train_acc_history.append(epoch_train_acc)
        test_acc_history.append(epoch_test_acc)
        
        # 更新学习率调度器
        if scheduler is not None:
            scheduler.step(epoch_test_loss)
        
        # 打印 epoch 结果
        print(f"Epoch {epoch+1} 完成 | 训练损失: {epoch_train_loss:.4f} "
              f"| 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%")
    
    # 绘制损失和准确率曲线
    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  # 返回最终测试准确率


# 7. 绘制Iteration损失曲线
def plot_iter_losses(losses, indices):
    plt.figure(figsize=(10, 4))
    plt.plot(indices, losses, 'b-', alpha=0.7)
    plt.xlabel('Iteration（Batch序号）')
    plt.ylabel('损失值')
    plt.title('训练过程中的Iteration损失变化')
    plt.grid(True)
    plt.show()

# 8. 绘制Epoch级指标曲线
def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):
    epochs = range(1, len(train_acc) + 1)
    
    plt.figure(figsize=(12, 5))
    
    # 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('准确率随Epoch变化')
    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('损失值随Epoch变化')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

# 主函数：训练模型
def main():
    # 参数设置
    epochs = 40  # 总训练轮次
    freeze_epochs = 5  # 冻结卷积层的轮次
    learning_rate = 1e-3  # 初始学习率
    weight_decay = 1e-4  # 权重衰减
    
    # 创建ResNet18模型（加载预训练权重）
    model = create_resnet18(pretrained=True, num_classes=10)
    
    # 定义优化器和损失函数
    optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
    criterion = nn.CrossEntropyLoss()
    
    # 定义学习率调度器
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(
    optimizer, mode='min', factor=0.5, patience=2
)

    # 开始训练（前5轮冻结卷积层，之后解冻）
    final_accuracy = train_with_freeze_schedule(
        model=model,
        train_loader=train_loader,
        test_loader=test_loader,
        criterion=criterion,
        optimizer=optimizer,
        scheduler=scheduler,
        device=device,
        epochs=epochs,
        freeze_epochs=freeze_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_cifar10_finetuned.pth')
    # print("模型已保存至: resnet18_cifar10_finetuned.pth")

if __name__ == "__main__":
    main()

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

# 设置设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")

# 数据预处理和增强
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomCrop(32, padding=4),
        transforms.RandomHorizontalFlip(),
        transforms.Resize(224),  # DenseNet期望输入尺寸为224x224
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ]),
    'val': transforms.Compose([
        transforms.Resize(224),
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
    ]),
}

# 加载CIFAR-10数据集
batch_size = 32
image_datasets = {
    'train': datasets.CIFAR10(
        root='./data', train=True, download=True, transform=data_transforms['train']),
    'val': datasets.CIFAR10(
        root='./data', train=False, download=True, transform=data_transforms['val'])
}

dataloaders = {
    x: torch.utils.data.DataLoader(
        image_datasets[x], batch_size=batch_size, 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'].classes

# 加载预训练的DenseNet模型
model_ft = models.densenet121(pretrained=True)

# 冻结大部分参数，只训练最后几层
for param in list(model_ft.parameters())[:-100]:  # 保留最后100个参数进行训练
    param.requires_grad = False

# 获取最后一层的输入特征数
num_ftrs = model_ft.classifier.in_features

# 替换最后一层以适应CIFAR-10的10个类别
model_ft.classifier = nn.Linear(num_ftrs, 10)

model_ft = model_ft.to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()

# 只优化那些requires_grad为True的参数
optimizer_ft = optim.SGD(
    [p for p in model_ft.parameters() if p.requires_grad],
    lr=0.001,
    momentum=0.9
)

# 学习率调度器，每7个epoch将学习率乘以0.1
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

# 训练模型的函数
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print(f'Epoch {epoch}/{num_epochs - 1}')
        print('-' * 10)

        # 每个epoch都有训练和验证阶段
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # 设置模型为训练模式
            else:
                model.eval()   # 设置模型为评估模式

            running_loss = 0.0
            running_corrects = 0

            # 迭代数据
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 清零梯度
                optimizer.zero_grad()

                # 前向传播
                # 只有在训练时才追踪历史
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 训练阶段进行反向传播和优化
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # 统计
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

            # 深度复制模型
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
    print(f'Best val Acc: {best_acc:4f}')

    # 加载最佳模型权重
    model.load_state_dict(best_model_wts)
    return model

# 训练模型（这里使用较少的epoch进行演示，实际应用中可以增加）
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=10)

# 保存最佳模型
torch.save(model_ft.state_dict(), 'densenet_cifar10_best.pth')

# 可视化一些预测结果
def imshow(inp, title=None):
    """显示张量图像"""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.4914, 0.4822, 0.4465])
    std = np.array([0.2023, 0.1994, 0.2010])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.figure(figsize=(10, 10))
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # 暂停一下，让 plots 更新

def visualize_model(model, num_images=6):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 2, images_so_far)
                ax.axis('off')
                ax.set_title(f'预测: {class_names[preds[j]]}')
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)

# 可视化预测结果
import numpy as np
visualize_model(model_ft)
plt.show()

@浙大疏锦行