dont use predictor over and over

vr180_matting/sam2_wrapper.py

@@ -33,6 +33,8 @@ class SAM2VideoMatting:
         self.device = device
         self.memory_offload = memory_offload
         self.fp16 = fp16
+        self.model_cfg = model_cfg
+        self.checkpoint_path = checkpoint_path
         self.predictor = None
         self.inference_state = None
         self.video_segments = {}
@@ -75,7 +77,8 @@ class SAM2VideoMatting:
     def init_video_state(self, video_frames: List[np.ndarray] = None, video_path: str = None) -> None:
         """Initialize video inference state"""
         if self.predictor is None:
-            raise RuntimeError("SAM2 model not loaded")
+            # Recreate predictor if it was cleaned up
+            self._load_model(self.model_cfg, self.checkpoint_path)
         
         if video_path is not None:
             # Use video path directly (SAM2's preferred method)
@@ -290,11 +293,17 @@ class SAM2VideoMatting:
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
         
+        # Explicitly delete predictor for fresh creation next time
+        if self.predictor is not None:
+            try:
+                del self.predictor
+            except Exception as e:
+                warnings.warn(f"Failed to delete predictor: {e}")
+            finally:
+                self.predictor = None
+        
         # Force garbage collection (critical for memory leak prevention)
         gc.collect()
-        
-        # Clear predictor reference (but don't delete the object itself)
-        self.predictor = None
     
     def __del__(self):
         """Destructor to ensure cleanup"""