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。

README.md

@@ -25,7 +25,7 @@ pip install -r requirements.txt
 
 ```bash
 cd train
-python food_classifier.py
+python train_food_classifier.py
 ```
 
 确保您的数据集结构如下：

settings/settings.py

@@ -0,0 +1,51 @@
+"""
+食物分类器配置文件
+包含训练参数、路径配置等
+"""
+
+import os
+
+# 基础路径配置
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+# 数据集路径
+DATASET_DIR = os.path.join(BASE_DIR, 'dataset')
+TRAIN_DATA_DIR = os.path.join(DATASET_DIR, 'train')
+VAL_DATA_DIR = os.path.join(DATASET_DIR, 'val')
+TEST_DATA_DIR = os.path.join(DATASET_DIR, 'test')
+
+# 模型保存路径
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '03')
+BEST_MODEL_PATH = os.path.join(MODEL_DIR, 'best_food_model.pth')
+TRAINING_CURVES_PATH = os.path.join(MODEL_DIR, 'training_curves.png')
+
+# 训练参数
+# NUM_EPOCHS = 100
+NUM_EPOCHS = 3
+BATCH_SIZE = 32
+LEARNING_RATE = 0.001
+WEIGHT_DECAY = 1e-4
+
+# 学习率调度器参数
+SCHEDULER_STEP_SIZE = 30
+SCHEDULER_GAMMA = 0.1
+
+# 数据预处理参数
+IMAGE_SIZE = (32, 32)
+NORMALIZE_MEAN = (0.485, 0.456, 0.406)
+NORMALIZE_STD = (0.229, 0.224, 0.225)
+
+# 数据增强参数
+RANDOM_HORIZONTAL_FLIP_P = 0.5
+RANDOM_ROTATION_DEGREES = 10
+COLOR_JITTER_BRIGHTNESS = 0.2
+COLOR_JITTER_CONTRAST = 0.2
+COLOR_JITTER_SATURATION = 0.2
+COLOR_JITTER_HUE = 0.1
+
+# 模型参数
+NUM_CLASSES = 3
+
+# 其他配置
+NUM_WORKERS = 0  # Windows下建议设为0
+DEVICE = 'cuda'  # 'cuda' 或 'cpu'，程序会自动检测可用性

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)
+    train_dataset = datasets.ImageFolder(settings.TRAIN_DATA_DIR, transform=transform_train)
-    val_dataset = datasets.ImageFolder('../dataset/val', transform=transform_test)
+    val_dataset = datasets.ImageFolder(settings.VAL_DATA_DIR, transform=transform_test)
-    test_dataset = datasets.ImageFolder('../dataset/test', 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=settings.BATCH_SIZE, shuffle=True, num_workers=settings.NUM_WORKERS)
-    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_dataset, batch_size=settings.BATCH_SIZE, shuffle=False, num_workers=settings.NUM_WORKERS)
-    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
+    test_loader = DataLoader(test_dataset, batch_size=settings.BATCH_SIZE, shuffle=False, num_workers=settings.NUM_WORKERS)
-    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
 
     # 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)
+    optimizer = optim.Adam(model.parameters(), lr=settings.LEARNING_RATE, weight_decay=settings.WEIGHT_DECAY)
-    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
+    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