chore: backup before migration to new repo

scripts/scene_classifier.py

@@ -162,9 +162,13 @@ class SceneClassifier:
             model_path: Core ML 模型路徑 (可選)
         """
         self.model_path = model_path
+        self.places365_model_path = (
+            "/Users/accusys/momentry/models/resnet18_places365.pth.tar"
+        )
         self.model = None
         self.coreml_model = None
         self.transform = None
+        self.model_type = "unknown"
 
         # 圖像預處理
         self.transform = transforms.Compose(
@@ -189,23 +193,57 @@ class SceneClassifier:
             try:
                 print(f"[SCENE] Loading Core ML model: {self.model_path}")
                 self.coreml_model = ct.models.MLModel(self.model_path)
+                self.model_type = "coreml"
                 print("[SCENE] Core ML model loaded successfully")
                 return True
             except Exception as e:
                 print(f"[SCENE] Warning: Failed to load Core ML model: {e}")
 
-        # 備案：使用 PyTorch + ResNet
+        # 備案：使用 PyTorch + Places365
         if HAS_TORCH:
             try:
                 print(f"[SCENE] Loading PyTorch model on {DEVICE}")
-                # 使用預訓練的 ResNet18
-                self.model = models.resnet18(pretrained=True)
+
+                # 檢查 Places365 模型是否存在
+                if Path(self.places365_model_path).exists():
+                    print(
+                        f"[SCENE] Loading Places365 model: {self.places365_model_path}"
+                    )
+                    checkpoint = torch.load(
+                        self.places365_model_path, map_location=DEVICE
+                    )
+
+                    # 建立 ResNet18 模型 (Places365 有 365 個類別)
+                    self.model = models.resnet18(num_classes=365)
+
+                    # 移除 'module.' prefix (DataParallel training)
+                    state_dict = checkpoint["state_dict"]
+                    new_state_dict = {}
+                    for k, v in state_dict.items():
+                        if k.startswith("module."):
+                            new_state_dict[k[7:]] = v
+                        else:
+                            new_state_dict[k] = v
+
+                    self.model.load_state_dict(new_state_dict)
+                    self.model_type = "places365"
+                    print("[SCENE] Places365 model loaded successfully (365 classes)")
+                else:
+                    print(
+                        f"[SCENE] Places365 model not found, using ImageNet pretrained"
+                    )
+                    self.model = models.resnet18(pretrained=True)
+                    self.model_type = "imagenet"
+
                 self.model.to(DEVICE)
                 self.model.eval()
                 print("[SCENE] PyTorch model loaded successfully")
                 return True
             except Exception as e:
                 print(f"[SCENE] Warning: Failed to load PyTorch model: {e}")
+                import traceback
+
+                traceback.print_exc()
 
         print("[SCENE] Error: No model available")
         return False