修改训练代码，兼容三个模型的训练。

settings/settings.py

@@ -52,4 +52,7 @@ NUM_CLASSES = 4
 
 # 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
+DEVICE = 'cuda'  # 'cuda' 或 'cpu'，程序会自动检测可用性
+
+# 中心损失权重
+CENTER_LOSS_WEIGHT = 0.1

train/train_embedding.py

@@ -24,6 +24,103 @@ sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 from net.resnet_embedding import create_resnet50_embedding
 from settings import settings
 
+# 任务配置
+from dataclasses import dataclass
+
+@dataclass
+class TaskConfig:
+    name: str
+    train_dir: str
+    val_dir: str
+    embedding_dim: int
+    batch_size: int
+    lr: float
+    triplet_margin: float
+    center_loss_weight: float
+    aug_strength: str  # "strong" | "medium" | "shape"
+
+# 使用你已创建的目录结构
+TASKS = {
+    "dish": TaskConfig(
+        name="DishClassification",
+        train_dir=os.path.join(settings.BASE_DIR, "dataset", "DishClassification", "train"),
+        val_dir=os.path.join(settings.BASE_DIR, "dataset", "DishClassification", "val"),
+        embedding_dim=512,
+        batch_size=16,
+        lr=1e-3,
+        triplet_margin=0.3,
+        center_loss_weight=0.1,
+        aug_strength="medium",
+    ),
+    "whole_ingredient": TaskConfig(
+        name="WholeIngredientRecognition",
+        train_dir=os.path.join(settings.BASE_DIR, "dataset", "WholeIngredientRecognition", "train"),
+        val_dir=os.path.join(settings.BASE_DIR, "dataset", "WholeIngredientRecognition", "val"),
+        embedding_dim=512,
+        batch_size=32,
+        lr=8e-4,
+        triplet_margin=0.35,
+        center_loss_weight=0.1,
+        aug_strength="medium",
+    ),
+    "processed_ingredient": TaskConfig(
+        name="ProcessedIngredientRecognition",
+        train_dir=os.path.join(settings.BASE_DIR, "dataset", "ProcessedIngredientRecognition", "train"),
+        val_dir=os.path.join(settings.BASE_DIR, "dataset", "ProcessedIngredientRecognition", "val"),
+        embedding_dim=512,
+        batch_size=16,
+        lr=1e-3,
+        triplet_margin=0.25,
+        center_loss_weight=0.1,
+        aug_strength="shape",
+    ),
+}
+
+def build_transforms(aug_strength: str):
+    if aug_strength == "strong":
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomRotation(20),
+            transforms.ColorJitter(0.3,0.3,0.3,0.1),
+            transforms.RandomAffine(degrees=0, translate=(0.12,0.12)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+        ]), transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+        ])
+    if aug_strength == "medium":
+        return transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomRotation(15),
+            transforms.ColorJitter(0.2,0.2,0.2,0.1),
+            transforms.RandomAffine(degrees=0,translate=(0.1,0.1)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+        ]), transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+        ])
+    # shape: 强调几何与尺度，弱化强色抖动
+    return transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.RandomHorizontalFlip(p=0.5),
+        transforms.RandomAffine(degrees=15, translate=(0.1,0.1), scale=(0.9,1.1)),
+        transforms.RandomPerspective(distortion_scale=0.3, p=0.3),
+        transforms.GaussianBlur(kernel_size=3, sigma=(0.1,1.0)),
+        transforms.ColorJitter(0.1,0.1,0.1,0.03),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+    ]), transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
+    ])
+
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
 plt.rcParams['axes.unicode_minus'] = False
@@ -266,6 +363,7 @@ class EarlyStopping:
         self.min_delta = min_delta
         self.counter = 0
         self.best_loss = float('inf')
+
         
     def __call__(self, val_loss: float) -> bool:
         """
@@ -309,6 +407,8 @@ def train_epoch(model, train_loader, triplet_criterion, center_criterion,
     total_triplet_loss = 0.0
     total_center_loss = 0.0
     num_batches = 0
+
+
     
     train_bar = tqdm(train_loader, desc=f'Epoch {epoch+1} 训练中')
     
@@ -330,7 +430,7 @@ def train_epoch(model, train_loader, triplet_criterion, center_criterion,
         center_loss = center_criterion(anchor_emb, labels)
         
         # 总损失
-        loss = triplet_loss + 0.1 * center_loss  # 中心损失权重为0.1
+        loss = triplet_loss + settings.CENTER_LOSS_WEIGHT * center_loss  # 可配置的中心损失权重
         
         # 反向传播
         optimizer.zero_grad()
@@ -401,7 +501,7 @@ def validate_epoch(model, val_loader, triplet_criterion, center_criterion, devic
             # 计算损失
             triplet_loss = triplet_criterion(anchor_emb, positive_emb, negative_emb)
             center_loss = center_criterion(anchor_emb, labels)
-            loss = triplet_loss + 0.1 * center_loss
+            loss = triplet_loss + settings.CENTER_LOSS_WEIGHT * center_loss
             
             # 计算准确率（基于最近邻分类）
             # 这里简化为检查正样本距离是否小于负样本距离
@@ -510,52 +610,38 @@ def save_training_results(results, save_path):
         f.write(f"设备: {results['device']}\n")
 
 
-def main():
+def main(task_key: str = "dish"):
     """主训练函数"""
     
     # 创建保存目录
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    save_dir = os.path.join(settings.BASE_DIR, 'model', f'embedding_{timestamp}')
+    cfg = TASKS[task_key]
+    save_dir = os.path.join(settings.BASE_DIR, 'model', cfg.name, f'embedding_{timestamp}')
     os.makedirs(save_dir, exist_ok=True)
     
-    logger.info(f"模型保存目录: {save_dir}")
+    logger.info(f"[{cfg.name}] 模型保存目录: {save_dir}")
     
     # 训练参数
-    EMBEDDING_DIM = 512
+    EMBEDDING_DIM = cfg.embedding_dim
-    BATCH_SIZE = 16  # 三元组训练通常使用较小的batch size
+    BATCH_SIZE = cfg.batch_size  # 三元组训练通常使用较小的batch size
-    LEARNING_RATE = 0.001
+    LEARNING_RATE = cfg.lr
     NUM_EPOCHS = 50
-    TRIPLET_MARGIN = 0.3
+    TRIPLET_MARGIN = cfg.triplet_margin
-    CENTER_LOSS_WEIGHT = 0.1
+    CENTER_LOSS_WEIGHT = cfg.center_loss_weight
     PATIENCE = 10
     
     # 数据预处理
-    transform_train = transforms.Compose([
+    transform_train, transform_val = build_transforms(cfg.aug_strength)
-        transforms.Resize((224, 224)),
-        transforms.RandomHorizontalFlip(p=0.5),
-        transforms.RandomRotation(15),
-        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
-        transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-
-    # 验证不需要做图片增强
-    transform_val = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
     
     # 创建数据集
     train_dataset = TripletDataset(
-        dataset_path=settings.TRAIN_DATA_DIR,
+        dataset_path=cfg.train_dir,
         transform=transform_train,
         samples_per_class=550
     )
     
     val_dataset = TripletDataset(
-        dataset_path=settings.VAL_DATA_DIR,
+        dataset_path=cfg.val_dir,
         transform=transform_val,
         samples_per_class=50
     )
@@ -721,4 +807,8 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument("task", choices=list(TASKS.keys()), nargs="?", default="dish")
+    args = parser.parse_args()
+    main(args.task)