debug

vr180_streaming/sam2_streaming.py

@@ -81,14 +81,31 @@ class SAM2StreamingProcessor:
                 vos_optimized=True  # Enable full model compilation for speed
             )
             
-            # Set to eval mode
+            # Set to eval mode and ensure all model components are on GPU
             self.predictor.eval()
             
+            # Force all predictor components to GPU
+            self.predictor = self.predictor.to(self.device)
+            
+            # Force move all internal components that might be on CPU
+            if hasattr(self.predictor, 'image_encoder'):
+                self.predictor.image_encoder = self.predictor.image_encoder.to(self.device)
+            if hasattr(self.predictor, 'memory_attention'):
+                self.predictor.memory_attention = self.predictor.memory_attention.to(self.device)
+            if hasattr(self.predictor, 'memory_encoder'):
+                self.predictor.memory_encoder = self.predictor.memory_encoder.to(self.device)
+            if hasattr(self.predictor, 'sam_mask_decoder'):
+                self.predictor.sam_mask_decoder = self.predictor.sam_mask_decoder.to(self.device)
+            if hasattr(self.predictor, 'sam_prompt_encoder'):
+                self.predictor.sam_prompt_encoder = self.predictor.sam_prompt_encoder.to(self.device)
+            
             # Note: FP16 conversion can cause type mismatches with compiled models
             # Let SAM2 handle precision internally via build_sam2_video_predictor options
             if self.fp16 and self.device.type == 'cuda':
                 print("   FP16 enabled via SAM2 internal settings")
                 
+            print(f"   All SAM2 components moved to {self.device}")
+                
         except Exception as e:
             raise RuntimeError(f"Failed to initialize SAM2 predictor: {e}")
             
@@ -286,11 +303,28 @@ class SAM2StreamingProcessor:
                 print(f"   Error in add_new_points_or_box: {e}")
                 print(f"   Box tensor device: {boxes_tensor.device}")
                 print(f"   Frame tensor device: {frame_tensor.device}")
-                print(f"   State device keys: {[k for k in state.keys() if 'device' in k.lower()]}")
-                # Try to inspect state tensor devices
-                for key, value in state.items():
-                    if isinstance(value, torch.Tensor):
-                        print(f"   State[{key}] device: {value.device}")
+                
+                # Check predictor components
+                print(f"   Checking predictor device placement:")
+                if hasattr(self.predictor, 'image_encoder'):
+                    try:
+                        for name, param in self.predictor.image_encoder.named_parameters():
+                            if param.device.type != 'cuda':
+                                print(f"     image_encoder.{name}: {param.device}")
+                                break
+                    except: pass
+                    
+                if hasattr(self.predictor, 'sam_prompt_encoder'):
+                    try:
+                        for name, param in self.predictor.sam_prompt_encoder.named_parameters():
+                            if param.device.type != 'cuda':
+                                print(f"     sam_prompt_encoder.{name}: {param.device}")
+                                break
+                    except: pass
+                
+                # Check for any CPU tensors in predictor
+                print(f"   Predictor type: {type(self.predictor)}")
+                print(f"   Available predictor attributes: {[attr for attr in dir(self.predictor) if not attr.startswith('_')]}")
                 raise
             
         self.object_ids = object_ids