feat(step8): 外部模型从单文件升级为母文件夹多模型字典扫描

src/core/prediction/inference_batch.py

@@ -26,19 +26,24 @@ from sklearn.model_selection import train_test_split
 class WaterQualityInference:
     """水质参数反演推理类"""
 
-    def __init__(self, artifacts_dir: str = "models/artifacts"):
+    def __init__(self, artifacts_dir: str = "models/artifacts",
+                 external_model=None, external_model_path=None):
         """
         初始化推理类
 
         Args:
             artifacts_dir: 模型保存目录
+            external_model: 外部预训练模型对象（来自 GUI 导入，跳过磁盘加载）
+            external_model_path: 外部模型文件路径（仅用于日志）
         """
         self.artifacts_dir = Path(artifacts_dir)
         if not self.artifacts_dir.exists():
             print(f"警告: 模型目录不存在: {artifacts_dir}，将在需要时创建")
-            
+
         self.best_model_info = None
         self.loaded_model_data = None
+        self.external_model = external_model
+        self.external_model_path = external_model_path
 
     def load_sampling_data(self, csv_path: str) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
         """
@@ -745,7 +750,11 @@ class WaterQualityInference:
             # 1. 加载模型
             print("\n步骤1: 加载模型")
             print("-" * 40)
-            if model_file_path:
+            if self.external_model is not None:
+                # 外部预训练模型已注入，直接使用，跳过磁盘加载
+                self.loaded_model_data = self.external_model
+                print(f"  使用外部预训练模型: type={type(self.external_model).__name__}")
+            elif model_file_path:
                 self.load_specific_model(model_file_path)
             else:
                 self.load_best_model(metric=metric)
@@ -863,10 +872,12 @@ class WaterQualityInference:
         print(f"\n批量推理完成，共处理 {len(csv_files)} 个文件")
         return results
 
-    def batch_inference_multi_models(self, models_root_dir: str, sampling_csv_path: str, 
-                                   output_dir: str, metric: str = 'test_r2', 
+    def batch_inference_multi_models(self, models_root_dir: str, sampling_csv_path: str,
+                                   output_dir: str, metric: str = 'test_r2',
                                    prediction_column: str = 'prediction',
-                                   output_format: str = 'csv'):
+                                   output_format: str = 'csv',
+                                   external_model=None,
+                                   external_model_path=None):
         """
         使用多个子文件夹中的模型进行批量推理
         
@@ -881,7 +892,18 @@ class WaterQualityInference:
         models_root = Path(models_root_dir)
         output_path = Path(output_dir)
         output_path.mkdir(parents=True, exist_ok=True)
-        
+
+        # 优先级：外部预训练模型 > 从磁盘加载
+        if external_model is not None:
+            effective_model = external_model
+            model_desc = (
+                f"外部导入模型 ({external_model_path or 'unknown'}), "
+                f"type={type(external_model).__name__}"
+            )
+            print(f"\n使用外部预训练模型: {model_desc}")
+        else:
+            effective_model = None
+
         # 查找所有子文件夹
         subdirs = [d for d in models_root.iterdir() if d.is_dir()]
         
@@ -900,9 +922,16 @@ class WaterQualityInference:
                 print(f"\n{'='*60}")
                 print(f"处理模型文件夹: {subdir_name}")
                 print(f"{'='*60}")
-                
-                # 创建新的推理实例，使用当前子文件夹作为artifacts_dir
-                model_inferencer = WaterQualityInference(str(subdir))
+
+                # 创建推理实例：外部模型优先注入，跳过磁盘查找
+                if effective_model is not None:
+                    model_inferencer = WaterQualityInference(
+                        str(subdir),
+                        external_model=effective_model,
+                        external_model_path=external_model_path,
+                    )
+                else:
+                    model_inferencer = WaterQualityInference(str(subdir))
                 
                 # 根据输出格式设置文件扩展名
                 file_ext = f".{output_format}"