已经把重要的配置全部拎出来了，每次训练只需要修改配置文件就可以了。

train/train_food_classifier.py

@@ -16,6 +16,7 @@ import time
 # sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'net'))
 # from food_net import create_food_cnn
 from net import create_food_cnn
+from settings import settings
 
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 指定默认字体
@@ -135,15 +136,14 @@ def test(model, test_loader, device, class_names):
 
 if __name__ == '__main__':
     # 加载数据集
-    train_dataset = datasets.ImageFolder('../dataset/train', transform=transform_train)
-    val_dataset = datasets.ImageFolder('../dataset/val', transform=transform_test)
-    test_dataset = datasets.ImageFolder('../dataset/test', transform=transform_test)
+    train_dataset = datasets.ImageFolder(settings.TRAIN_DATA_DIR, transform=transform_train)
+    val_dataset = datasets.ImageFolder(settings.VAL_DATA_DIR, transform=transform_test)
+    test_dataset = datasets.ImageFolder(settings.TEST_DATA_DIR, transform=transform_test)
 
     # 创建数据加载器
-    batch_size = 32
-    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
-    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
-    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
+    train_loader = DataLoader(train_dataset, batch_size=settings.BATCH_SIZE, shuffle=True, num_workers=settings.NUM_WORKERS)
+    val_loader = DataLoader(val_dataset, batch_size=settings.BATCH_SIZE, shuffle=False, num_workers=settings.NUM_WORKERS)
+    test_loader = DataLoader(test_dataset, batch_size=settings.BATCH_SIZE, shuffle=False, num_workers=settings.NUM_WORKERS)
 
     # 类别名称
     class_names = train_dataset.classes
@@ -156,23 +156,22 @@ if __name__ == '__main__':
     model = create_food_cnn().to(device)
     print(f"模型参数数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
 
-    # 定义损失函数和优化器（使用与CIFAR10相同的超参数）
+    # 定义损失函数和优化器
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)
-    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
+    optimizer = optim.Adam(model.parameters(), lr=settings.LEARNING_RATE, weight_decay=settings.WEIGHT_DECAY)
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=settings.SCHEDULER_STEP_SIZE, gamma=settings.SCHEDULER_GAMMA)
 
     # 训练模型
-    num_epochs = 100
     train_losses = []
     train_accuracies = []
     val_losses = []
     val_accuracies = []
 
     best_val_acc = 0.0
-    best_model_path = '../model/02/best_food_model.pth'
 
     print("开始训练...")
     start_time = time.time()  # 记录训练开始时间
+    num_epochs = settings.NUM_EPOCHS
     for epoch in range(num_epochs):
         print(f'\nEpoch {epoch+1}/{num_epochs}')
         print('-' * 50)
@@ -199,7 +198,8 @@ if __name__ == '__main__':
         # 保存最佳模型
         if val_acc > best_val_acc:
             best_val_acc = val_acc
-            torch.save(model.state_dict(), best_model_path)
+            best_model_path = settings.BEST_MODEL_PATH
+            torch.save(model.state_dict(), settings.BEST_MODEL_PATH)
             print(f'保存最佳模型，验证准确率: {best_val_acc:.2f}%')
 
     end_time = time.time()  # 记录训练结束时间