預訓練模型發展史
預訓練模型的訓練策略
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# 設置中文字體支持
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False # 解決負號顯示問題# 檢查GPU是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用設備: {device}")# 1. 數據預處理（訓練集增強，測試集標準化）
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. 加載CIFAR-10數據集
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. 創建數據加載器（可調整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. 訓練函數（支持學習率調度器）
def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs):model.train() # 設置為訓練模式train_loss_history = []test_loss_history = []train_acc_history = []test_acc_history = []all_iter_losses = []iter_indices = []for epoch in range(epochs):running_loss = 0.0correct_train = 0total_train = 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()# 記錄Iteration損失iter_loss = loss.item()all_iter_losses.append(iter_loss)iter_indices.append(epoch * len(train_loader) + batch_idx + 1)# 統計訓練指標running_loss += iter_loss_, predicted = output.max(1)total_train += target.size(0)correct_train += predicted.eq(target).sum().item()# 每100批次打印進度if (batch_idx + 1) % 100 == 0:print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx+1}/{len(train_loader)} "f"| 單Batch損失: {iter_loss:.4f}")# 計算 epoch 級指標epoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct_train / total_train# 測試階段model.eval()correct_test = 0total_test = 0test_loss = 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_test# 記錄歷史數據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)# 更新學習率調度器if scheduler is not None:scheduler.step(epoch_test_loss)# 打印 epoch 結果print(f"Epoch {epoch+1} 完成 | 訓練損失: {epoch_train_loss:.4f} "f"| 訓練準確率: {epoch_train_acc:.2f}% | 測試準確率: {epoch_test_acc:.2f}%")# 繪制損失和準確率曲線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 # 返回最終測試準確率# 5. 繪制Iteration損失曲線
def plot_iter_losses(losses, indices):plt.figure(figsize=(10, 4))plt.plot(indices, losses, 'b-', alpha=0.7)plt.xlabel('Iteration（Batch序號）')plt.ylabel('損失值')plt.title('訓練過程中的Iteration損失變化')plt.grid(True)plt.show()# 6. 繪制Epoch級指標曲線
def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):epochs = range(1, len(train_acc) + 1)plt.figure(figsize=(12, 5))# 準確率曲線plt.subplot(1, 2, 1)plt.plot(epochs, train_acc, 'b-', label='訓練準確率')plt.plot(epochs, test_acc, 'r-', label='測試準確率')plt.xlabel('Epoch')plt.ylabel('準確率 (%)')plt.title('準確率隨Epoch變化')plt.legend()plt.grid(True)# 損失曲線plt.subplot(1, 2, 2)plt.plot(epochs, train_loss, 'b-', label='訓練損失')plt.plot(epochs, test_loss, 'r-', label='測試損失')plt.xlabel('Epoch')plt.ylabel('損失值')plt.title('損失值隨Epoch變化')plt.legend()plt.grid(True)plt.tight_layout()plt.show()# 導入ResNet模型
from torchvision.models import resnet18# 定義ResNet18模型（支持預訓練權重加載）
def create_resnet18(pretrained=True, num_classes=10):# 加載預訓練模型（ImageNet權重）model = resnet18(pretrained=pretrained)# 修改最后一層全連接層，適配CIFAR-10的10分類任務in_features = model.fc.in_featuresmodel.fc = nn.Linear(in_features, num_classes)# 將模型轉移到指定設備（CPU/GPU）model = model.to(device)return model# 創建ResNet18模型（加載ImageNet預訓練權重，不進行微調）
model = create_resnet18(pretrained=True, num_classes=10)
model.eval() # 設置為推理模式# 測試單張圖片（示例）
from torchvision import utils# 從測試數據集中獲取一張圖片
dataiter = iter(test_loader)
images, labels = dataiter.next()
images = images[:1].to(device) # 取第1張圖片# 前向傳播
with torch.no_grad():outputs = model(images)_, predicted = torch.max(outputs.data, 1)# 顯示圖片和預測結果
plt.imshow(utils.make_grid(images.cpu(), normalize=True).permute(1, 2, 0))
plt.title(f"預測類別: {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# 設置中文字體支持
plt.rcParams["font.family"] = ["SimHei"]
plt.rcParams['axes.unicode_minus'] = False # 解決負號顯示問題# 檢查GPU是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"使用設備: {device}")# 1. 數據預處理（訓練集增強，測試集標準化）
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. 加載CIFAR-10數據集
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. 創建數據加載器
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. 定義ResNet18模型
def create_resnet18(pretrained=True, num_classes=10):model = models.resnet18(pretrained=pretrained)# 修改最后一層全連接層in_features = model.fc.in_featuresmodel.fc = nn.Linear(in_features, num_classes)return model.to(device)# 5. 凍結/解凍模型層的函數
def freeze_model(model, freeze=True):"""凍結或解凍模型的卷積層參數"""# 凍結/解凍除fc層外的所有參數for name, param in model.named_parameters():if 'fc' not in name:param.requires_grad = not freeze# 打印凍結狀態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"已凍結模型卷積層參數 ({frozen_params}/{total_params} 參數)")else:print(f"已解凍模型所有參數 ({total_params}/{total_params} 參數可訓練)")return model# 6. 訓練函數（支持階段式訓練）
def train_with_freeze_schedule(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs, freeze_epochs=5):"""前freeze_epochs輪凍結卷積層，之后解凍所有層進行訓練"""train_loss_history = []test_loss_history = []train_acc_history = []test_acc_history = []all_iter_losses = []iter_indices = []# 初始凍結卷積層if freeze_epochs > 0:model = freeze_model(model, freeze=True)for epoch in range(epochs):# 解凍控制：在指定輪次后解凍所有層if epoch == freeze_epochs:model = freeze_model(model, freeze=False)# 解凍后調整優化器（可選）optimizer.param_groups[0]['lr'] = 1e-4 # 降低學習率防止過擬合model.train() # 設置為訓練模式running_loss = 0.0correct_train = 0total_train = 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()# 記錄Iteration損失iter_loss = loss.item()all_iter_losses.append(iter_loss)iter_indices.append(epoch * len(train_loader) + batch_idx + 1)# 統計訓練指標running_loss += iter_loss_, predicted = output.max(1)total_train += target.size(0)correct_train += predicted.eq(target).sum().item()# 每100批次打印進度if (batch_idx + 1) % 100 == 0:print(f"Epoch {epoch+1}/{epochs} | Batch {batch_idx+1}/{len(train_loader)} "f"| 單Batch損失: {iter_loss:.4f}")# 計算 epoch 級指標epoch_train_loss = running_loss / len(train_loader)epoch_train_acc = 100. * correct_train / total_train# 測試階段model.eval()correct_test = 0total_test = 0test_loss = 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_test# 記錄歷史數據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)# 更新學習率調度器if scheduler is not None:scheduler.step(epoch_test_loss)# 打印 epoch 結果print(f"Epoch {epoch+1} 完成 | 訓練損失: {epoch_train_loss:.4f} "f"| 訓練準確率: {epoch_train_acc:.2f}% | 測試準確率: {epoch_test_acc:.2f}%")# 繪制損失和準確率曲線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 # 返回最終測試準確率# 7. 繪制Iteration損失曲線
def plot_iter_losses(losses, indices):plt.figure(figsize=(10, 4))plt.plot(indices, losses, 'b-', alpha=0.7)plt.xlabel('Iteration（Batch序號）')plt.ylabel('損失值')plt.title('訓練過程中的Iteration損失變化')plt.grid(True)plt.show()# 8. 繪制Epoch級指標曲線
def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):epochs = range(1, len(train_acc) + 1)plt.figure(figsize=(12, 5))# 準確率曲線plt.subplot(1, 2, 1)plt.plot(epochs, train_acc, 'b-', label='訓練準確率')plt.plot(epochs, test_acc, 'r-', label='測試準確率')plt.xlabel('Epoch')plt.ylabel('準確率 (%)')plt.title('準確率隨Epoch變化')plt.legend()plt.grid(True)# 損失曲線plt.subplot(1, 2, 2)plt.plot(epochs, train_loss, 'b-', label='訓練損失')plt.plot(epochs, test_loss, 'r-', label='測試損失')plt.xlabel('Epoch')plt.ylabel('損失值')plt.title('損失值隨Epoch變化')plt.legend()plt.grid(True)plt.tight_layout()plt.show()# 主函數：訓練模型
def main():# 參數設置epochs = 40 # 總訓練輪次freeze_epochs = 5 # 凍結卷積層的輪次learning_rate = 1e-3 # 初始學習率weight_decay = 1e-4 # 權重衰減# 創建ResNet18模型（加載預訓練權重）model = create_resnet18(pretrained=True, num_classes=10)# 定義優化器和損失函數optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)criterion = nn.CrossEntropyLoss()# 定義學習率調度器scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2, verbose=True)# 開始訓練（前5輪凍結卷積層，之后解凍）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"訓練完成！最終測試準確率: {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()@浙大疏錦行
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