fix: gpu memory leaks

.gitignore

@@ -5,3 +5,4 @@ __pycache__/
 .claude
 /models/
 /tracked_objects.json
+.trt_cache

bangchak/models/.gitignore

@@ -0,0 +1,2 @@
+*.onnx
+*.engine

debug_trt_output.py

@@ -0,0 +1,136 @@
+"""
+Debug script to capture and compare raw PT vs TRT outputs on problematic frames.
+"""
+
+import torch
+import time
+from services import StreamDecoderFactory, YOLOv8Utils, TensorRTModelRepository
+from ultralytics import YOLO
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+GPU_ID = 0
+MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"
+STREAM_URL = os.getenv('CAMERA_URL_1')
+
+# Load models
+print("Loading models...")
+pt_model = YOLO(MODEL_PATH)
+pt_model.to(f'cuda:{GPU_ID}')
+
+repo = TensorRTModelRepository(gpu_id=GPU_ID)
+trt_path = "./models/trtptcache/trt/cda5e520441e12fe09a97ac2609da29b4cbac969cc2029ef1735f65697579121.trt"
+repo.load_model("detector", trt_path, num_contexts=1)
+
+# Start decoder
+print("Starting decoder...")
+decoder_factory = StreamDecoderFactory(gpu_id=GPU_ID)
+decoder = decoder_factory.create_decoder(STREAM_URL, buffer_size=30)
+decoder.start()
+time.sleep(2)
+
+torch.cuda.set_device(GPU_ID)
+
+print("\nWaiting for frames with TRT false positives...\n")
+
+frame_count = 0
+found_issue = False
+
+while frame_count < 50 and not found_issue:
+    frame = decoder.get_frame()
+    if frame is None:
+        time.sleep(0.01)
+        continue
+
+    frame_count += 1
+
+    # Preprocess
+    preprocessed = YOLOv8Utils.preprocess(frame, input_size=640)
+
+    # Run TRT inference
+    trt_outputs = repo.infer("detector", {"images": preprocessed}, synchronize=True)
+    trt_raw = trt_outputs['output0']  # (1, 5, 8400)
+
+    # Check for the issue - transpose and check channel 4
+    trt_transposed = trt_raw.transpose(1, 2).squeeze(0)  # (8400, 5)
+    conf_channel = trt_transposed[:, 4]  # (8400,)
+
+    num_high_conf = (conf_channel > 0.25).sum().item()
+
+    if num_high_conf > 100:
+        found_issue = True
+        print(f"🔴 FOUND PROBLEMATIC FRAME {frame_count}!")
+        print(f"   TRT detections > 0.25 threshold: {num_high_conf}")
+
+        # Now run PT model on same frame
+        with torch.no_grad():
+            pt_raw = pt_model.model(preprocessed)[0]  # (1, 5, 8400)
+
+        print(f"\n=== RAW OUTPUT COMPARISON ===")
+        print(f"PT  output shape: {pt_raw.shape}")
+        print(f"TRT output shape: {trt_raw.shape}")
+
+        # Compare channel 4 (confidence)
+        pt_conf = pt_raw.transpose(1, 2).squeeze(0)[:, 4]
+        trt_conf = trt_transposed[:, 4]
+
+        print(f"\n--- Confidence Channel (channel 4) ---")
+        print(f"PT  confidence stats:")
+        print(f"  Min:  {pt_conf.min().item():.6e}")
+        print(f"  Max:  {pt_conf.max().item():.6e}")
+        print(f"  Mean: {pt_conf.mean().item():.6e}")
+        print(f"  >0.25: {(pt_conf > 0.25).sum().item()}")
+        print(f"  >0.5:  {(pt_conf > 0.5).sum().item()}")
+
+        print(f"\nTRT confidence stats:")
+        print(f"  Min:  {trt_conf.min().item():.6e}")
+        print(f"  Max:  {trt_conf.max().item():.6e}")
+        print(f"  Mean: {trt_conf.mean().item():.6e}")
+        print(f"  >0.25: {(trt_conf > 0.25).sum().item()}")
+        print(f"  >0.5:  {(trt_conf > 0.5).sum().item()}")
+
+        # Check bbox coordinates too
+        print(f"\n--- BBox Coordinates (channels 0-3) ---")
+        pt_bbox = pt_raw.transpose(1, 2).squeeze(0)[:, :4]
+        trt_bbox = trt_transposed[:, :4]
+
+        print(f"PT  bbox stats:")
+        print(f"  Min:  {pt_bbox.min().item():.3f}")
+        print(f"  Max:  {pt_bbox.max().item():.3f}")
+        print(f"  Mean: {pt_bbox.mean().item():.3f}")
+
+        print(f"\nTRT bbox stats:")
+        print(f"  Min:  {trt_bbox.min().item():.3f}")
+        print(f"  Max:  {trt_bbox.max().item():.3f}")
+        print(f"  Mean: {trt_bbox.mean().item():.3f}")
+
+        # Sample some values
+        print(f"\n--- Sample Values (first 5 anchors) ---")
+        for i in range(5):
+            print(f"\nAnchor {i}:")
+            print(f"  PT  [cx={pt_bbox[i,0]:.1f}, cy={pt_bbox[i,1]:.1f}, w={pt_bbox[i,2]:.1f}, h={pt_bbox[i,3]:.1f}, conf={pt_conf[i]:.6f}]")
+            print(f"  TRT [cx={trt_bbox[i,0]:.1f}, cy={trt_bbox[i,1]:.1f}, w={trt_bbox[i,2]:.1f}, h={trt_bbox[i,3]:.1f}, conf={trt_conf[i]:.6f}]")
+
+        # Find indices with high confidence in TRT
+        high_conf_idx = torch.where(trt_conf > 0.25)[0][:5]
+        print(f"\n--- High Confidence Detections in TRT (first 5) ---")
+        for idx in high_conf_idx:
+            i = idx.item()
+            print(f"\nAnchor {i}:")
+            print(f"  PT  [cx={pt_bbox[i,0]:.1f}, cy={pt_bbox[i,1]:.1f}, w={pt_bbox[i,2]:.1f}, h={pt_bbox[i,3]:.1f}, conf={pt_conf[i]:.6f}]")
+            print(f"  TRT [cx={trt_bbox[i,0]:.1f}, cy={trt_bbox[i,1]:.1f}, w={trt_bbox[i,2]:.1f}, h={trt_bbox[i,3]:.1f}, conf={trt_conf[i]:.6f}]")
+
+        break
+
+    if frame_count % 10 == 0:
+        print(f"Checked {frame_count} frames, no issues yet...")
+
+if not found_issue:
+    print(f"\n⚠️  No problematic frames found in {frame_count} frames")
+
+# Cleanup
+decoder.stop()
+repo.unload_model("detector")
+print("\n✓ Done")

new_buffer_design.txt

@@ -0,0 +1,9 @@
+The Post-Decoded Buffer should just be the ping pong ring buffer
+let's get some relationship in order
+- ping pong ring is per model
+- many camera may use the same model
+- this buffer is filled when we memcpy it from decode buffer
+but I need some more ground rules
+- in the model buffer, one frame per camera may be in the buffer, if older one of the same camera exist, evict it. This is a real time system so buffer should be as fresh as possible.
+- The goal of batching is not to pool up processing for the same camera but to pool up multiple camera.
+- if all camera in the pool already post its frame, flush the buffer too

services/model_controller.py

@@ -117,9 +117,10 @@ class ModelController:
         """
         try:
             metadata = self.model_repository.get_metadata(self.model_id)
-            # Get first input tensor shape
+            # Get first input tensor shape (ModelMetadata has input_shapes, not inputs)
-            first_input = list(metadata.inputs.values())[0]
+            first_input_name = metadata.input_names[0]
-            batch_dim = first_input["shape"][0]
+            input_shape = metadata.input_shapes[first_input_name]
+            batch_dim = input_shape[0]
 
             # batch_dim can be -1 (dynamic), 1 (fixed), or N (fixed batch size)
             if batch_dim == -1:

services/model_repository.py

@@ -1,5 +1,6 @@
 import threading
 import hashlib
+import json
 from typing import Optional, Dict, Any, List, Tuple
 from pathlib import Path
 from queue import Queue
@@ -161,7 +162,7 @@ class TensorRTModelRepository:
         # Result: 1 engine in VRAM, N contexts (e.g., 4), not 100 contexts!
     """
 
-    def __init__(self, gpu_id: int = 0, default_num_contexts: int = 4, enable_pt_conversion: bool = True):
+    def __init__(self, gpu_id: int = 0, default_num_contexts: int = 4, enable_pt_conversion: bool = True, cache_dir: str = ".trt_cache"):
         """
         Initialize the model repository.
 
@@ -169,11 +170,14 @@ class TensorRTModelRepository:
             gpu_id: GPU device ID to use
             default_num_contexts: Default number of execution contexts per unique engine
             enable_pt_conversion: Enable automatic PyTorch to TensorRT conversion
+            cache_dir: Directory for caching stripped TensorRT engines and metadata
         """
         self.gpu_id = gpu_id
         self.device = torch.device(f'cuda:{gpu_id}')
         self.default_num_contexts = default_num_contexts
         self.enable_pt_conversion = enable_pt_conversion
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
 
         # Model ID to engine mapping: model_id -> file_hash
         self._model_to_hash: Dict[str, str] = {}
@@ -192,6 +196,7 @@ class TensorRTModelRepository:
 
         print(f"TensorRT Model Repository initialized on GPU {gpu_id}")
         print(f"Default context pool size: {default_num_contexts} contexts per unique model")
+        print(f"Cache directory: {self.cache_dir}")
         if enable_pt_conversion:
             print(f"PyTorch to TensorRT conversion: enabled")
 
@@ -226,6 +231,14 @@ class TensorRTModelRepository:
         """
         Load TensorRT engine from file.
 
+        Supports both raw TensorRT engines and Ultralytics .engine files
+        (which have embedded JSON metadata at the beginning).
+
+        For Ultralytics engines:
+        - Strips metadata and caches pure TensorRT engine in cache_dir
+        - Saves metadata as separate JSON file
+        - Reuses cached stripped engine on subsequent loads
+
         Args:
             file_path: Path to .trt or .engine file
 
@@ -234,8 +247,68 @@ class TensorRTModelRepository:
         """
         runtime = trt.Runtime(self.trt_logger)
 
-        with open(file_path, 'rb') as f:
+        # Compute hash of original file for cache lookup
+        file_hash = self.compute_file_hash(file_path)
+        cache_engine_path = self.cache_dir / f"{file_hash}.trt"
+        cache_metadata_path = self.cache_dir / f"{file_hash}_metadata.json"
+
+        # Check if stripped engine already cached
+        if cache_engine_path.exists():
+            logger.info(f"Loading cached stripped engine from {cache_engine_path}")
+            with open(cache_engine_path, 'rb') as f:
                 engine_data = f.read()
+        else:
+            # Read and process original file
+            with open(file_path, 'rb') as f:
+                # Try to read Ultralytics metadata header (first 4 bytes = metadata length)
+                try:
+                    meta_len_bytes = f.read(4)
+                    if len(meta_len_bytes) == 4:
+                        meta_len = int.from_bytes(meta_len_bytes, byteorder="little")
+
+                        # Sanity check: metadata length should be reasonable (< 100KB)
+                        if 0 < meta_len < 100000:
+                            try:
+                                metadata_bytes = f.read(meta_len)
+                                metadata = json.loads(metadata_bytes.decode("utf-8"))
+
+                                # This is an Ultralytics engine - read remaining pure TRT data
+                                engine_data = f.read()
+
+                                # Save stripped engine to cache
+                                logger.info(f"Detected Ultralytics engine format")
+                                logger.info(f"Ultralytics metadata: {metadata}")
+                                logger.info(f"Caching stripped engine to {cache_engine_path}")
+
+                                with open(cache_engine_path, 'wb') as cache_f:
+                                    cache_f.write(engine_data)
+
+                                # Save metadata separately
+                                with open(cache_metadata_path, 'w') as meta_f:
+                                    json.dump(metadata, meta_f, indent=2)
+
+                            except (UnicodeDecodeError, json.JSONDecodeError):
+                                # Not Ultralytics format, rewind and read entire file
+                                f.seek(0)
+                                engine_data = f.read()
+                        else:
+                            # Invalid metadata length, rewind and read entire file
+                            f.seek(0)
+                            engine_data = f.read()
+                    else:
+                        # File too small, just use what we read
+                        engine_data = meta_len_bytes
+
+                except Exception as e:
+                    # Any error, rewind and read entire file
+                    logger.warning(f"Error reading engine metadata: {e}, treating as raw TRT engine")
+                    f.seek(0)
+                    engine_data = f.read()
+
+            # Cache the engine data (even if it was already raw TRT)
+            if not cache_engine_path.exists():
+                with open(cache_engine_path, 'wb') as cache_f:
+                    cache_f.write(engine_data)
 
         engine = runtime.deserialize_cuda_engine(engine_data)
         if engine is None:
@@ -494,6 +567,11 @@ class TensorRTModelRepository:
                     device=self.device
                 )
 
+                # NOTE: Don't track these tensors - they're returned to caller and consumed
+                # by postprocessing, then automatically freed by PyTorch's garbage collector.
+                # Tracking them would show false "leaks" since we can't track when the caller
+                # finishes using them and PyTorch deallocates them.
+
                 outputs[name] = output_tensor
                 exec_ctx.context.set_tensor_address(name, output_tensor.data_ptr())
 

services/pt_converter.py

@@ -125,12 +125,19 @@ class PTConverter:
 
         mapping = self.mapping_db[pt_hash]
         trt_hash = mapping["trt_hash"]
+
+        # Check both .engine and .trt extensions (Ultralytics uses .engine, generic uses .trt)
+        engine_key = f"trt/{trt_hash}.engine"
         trt_key = f"trt/{trt_hash}.trt"
 
-        # Verify TRT file still exists in storage
+        # Try .engine first (Ultralytics native format)
-        if not self.storage.exists(trt_key):
+        if self.storage.exists(engine_key):
+            cached_key = engine_key
+        elif self.storage.exists(trt_key):
+            cached_key = trt_key
+        else:
             logger.warning(
-                f"Mapping exists for PT hash {pt_hash[:16]}... but TRT file missing. "
+                f"Mapping exists for PT hash {pt_hash[:16]}... but engine file missing. "
                 f"Will reconvert."
             )
             # Remove stale mapping
@@ -139,16 +146,16 @@ class PTConverter:
             return None
 
         # Get local path
-        trt_path = self.storage.get_local_path(trt_key)
+        cached_path = self.storage.get_local_path(cached_key)
-        if trt_path is None:
+        if cached_path is None:
-            logger.error(f"Could not get local path for TRT file {trt_key}")
+            logger.error(f"Could not get local path for engine file {cached_key}")
             return None
 
         logger.info(
             f"Found cached conversion for PT hash {pt_hash[:16]}... -> "
-            f"TRT hash {trt_hash[:16]}..."
+            f"Engine hash {trt_hash[:16]}... ({cached_key})"
         )
-        return (trt_hash, trt_path)
+        return (trt_hash, cached_path)
 
     def convert(
         self,
@@ -241,24 +248,21 @@ class PTConverter:
         precision: torch.dtype,
     ) -> Tuple[str, str]:
         """
-        Convert ultralytics YOLO model using ONNX → TensorRT pipeline.
+        Convert ultralytics YOLO model using native .engine export.
-        Uses the same approach as scripts/convert_pt_to_tensorrt.py
+        This produces .engine files with embedded metadata (no manual input_shapes needed).
 
         Args:
             pt_path: Path to PT file
             pt_hash: PT file hash
-            input_shapes: Input tensor shapes
+            input_shapes: Input tensor shapes (IGNORED for Ultralytics - auto-detected)
             precision: Target precision
 
         Returns:
-            Tuple of (trt_hash, trt_file_path)
+            Tuple of (engine_hash, engine_file_path)
         """
-        import tensorrt as trt
-        import tempfile
         import os
-        import shutil
 
-        logger.info("Detected ultralytics YOLO model, using ONNX → TensorRT pipeline...")
+        logger.info("Detected ultralytics YOLO model, using native .engine export...")
 
         # Load ultralytics model
         try:
@@ -267,83 +271,48 @@ class PTConverter:
         except ImportError:
             raise ImportError("ultralytics package not found. Install with: pip install ultralytics")
 
-        # Determine input shape
+        # Export to native .engine format with embedded metadata
-        if not input_shapes:
+        logger.info(f"Exporting to native TensorRT .engine (precision: {'FP16' if precision == torch.float16 else 'FP32'})...")
-            raise ValueError("input_shapes required for ultralytics conversion")
 
-        input_key = 'images' if 'images' in input_shapes else list(input_shapes.keys())[0]
+        # Ultralytics export creates .engine file in same directory as .pt
-        input_shape = input_shapes[input_key]
+        engine_path = model.export(
+            format='engine',
+            half=(precision == torch.float16),
+            device=self.gpu_id,
+            batch=1,
+            simplify=True
+        )
 
-        # Export to ONNX first
+        # Convert to string (Ultralytics returns Path object)
-        logger.info(f"Exporting to ONNX (input shape: {input_shape})...")
+        engine_path = str(engine_path)
-        with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmp_onnx:
+        logger.info(f"Native .engine export complete: {engine_path}")
-            onnx_path = tmp_onnx.name
+        logger.info("Metadata embedded in .engine file (stride, imgsz, names, etc.)")
 
-        try:
+        # Read the exported .engine file
-            # Use ultralytics export to ONNX
+        with open(engine_path, 'rb') as f:
-            model.export(format='onnx', imgsz=input_shape[2], batch=input_shape[0])
+            engine_data = f.read()
-            # Ultralytics saves as model_name.onnx in same directory
-            pt_dir = os.path.dirname(pt_path)
-            pt_name = os.path.splitext(os.path.basename(pt_path))[0]
-            onnx_export_path = os.path.join(pt_dir, f"{pt_name}.onnx")
 
-            # Move to our temp location (use shutil.move for cross-device support)
+        # Compute hash of the .engine file
-            if os.path.exists(onnx_export_path):
+        engine_hash = hashlib.sha256(engine_data).hexdigest()
-                shutil.move(onnx_export_path, onnx_path)
+
+        # Store in our cache (as .engine to preserve metadata)
+        engine_key = f"trt/{engine_hash}.engine"
+        self.storage.write(engine_key, engine_data)
+
+        cached_path = self.storage.get_local_path(engine_key)
+        if cached_path is None:
+            raise RuntimeError("Failed to get local path for .engine file")
+
+        # Clean up the original export (we've cached it)
+        # Only delete if it's different from cached path
+        if os.path.exists(engine_path) and os.path.abspath(engine_path) != os.path.abspath(cached_path):
+            logger.info(f"Removing original export (cached): {engine_path}")
+            os.unlink(engine_path)
         else:
-                raise RuntimeError(f"ONNX export failed, file not found: {onnx_export_path}")
+            logger.info(f"Keeping original export at: {engine_path}")
 
-            logger.info(f"ONNX export complete: {onnx_path}")
+        logger.info(f"Cached .engine file: {cached_path}")
-
+        return (engine_hash, cached_path)
-            # Build TensorRT engine from ONNX
-            logger.info("Building TensorRT engine from ONNX...")
-            trt_logger = trt.Logger(trt.Logger.WARNING)
-            builder = trt.Builder(trt_logger)
-            network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
-            parser = trt.OnnxParser(network, trt_logger)
-
-            # Parse ONNX
-            with open(onnx_path, 'rb') as f:
-                if not parser.parse(f.read()):
-                    errors = [parser.get_error(i) for i in range(parser.num_errors)]
-                    raise RuntimeError(f"Failed to parse ONNX: {errors}")
-
-            # Configure builder
-            config = builder.create_builder_config()
-            config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 << 30)  # 4GB
-
-            # Set precision
-            if precision == torch.float16:
-                if builder.platform_has_fast_fp16:
-                    config.set_flag(trt.BuilderFlag.FP16)
-                    logger.info("FP16 mode enabled")
-
-            # Build engine
-            logger.info("Building TensorRT engine (this may take a few minutes)...")
-            serialized_engine = builder.build_serialized_network(network, config)
-
-            if serialized_engine is None:
-                raise RuntimeError("Failed to build TensorRT engine")
-
-            # Convert IHostMemory to bytes
-            engine_bytes = bytes(serialized_engine)
-
-            # Save to storage
-            trt_hash = hashlib.sha256(engine_bytes).hexdigest()
-            trt_key = f"trt/{trt_hash}.trt"
-            self.storage.write(trt_key, engine_bytes)
-
-            trt_path = self.storage.get_local_path(trt_key)
-            if trt_path is None:
-                raise RuntimeError("Failed to get local path for TRT file")
-
-            logger.info(f"TensorRT engine built successfully: {trt_path}")
-            return (trt_hash, trt_path)
-
-        finally:
-            # Cleanup ONNX file
-            if os.path.exists(onnx_path):
-                os.unlink(onnx_path)
 
     def _perform_conversion(
         self,
@@ -387,11 +356,21 @@ class PTConverter:
 
             # Check if this is an ultralytics model
             if self._is_ultralytics_model(model):
-                logger.info("Detected ultralytics model, using ultralytics export API")
+                logger.info("Detected Ultralytics YOLO model, using native .engine export")
+                logger.info("Note: input_shapes parameter is ignored for Ultralytics models (auto-detected)")
                 return self._convert_ultralytics_model(pt_path, pt_hash, input_shapes, precision)
 
             # For non-ultralytics models, use torch_tensorrt
-            logger.info("Using torch_tensorrt for conversion")
+            logger.info("Using torch_tensorrt for conversion (non-Ultralytics model)")
+
+            # Non-Ultralytics models REQUIRE input_shapes
+            if input_shapes is None:
+                raise ValueError(
+                    "input_shapes required for non-Ultralytics PyTorch models. "
+                    "For Ultralytics YOLO models, input_shapes is auto-detected. "
+                    "Example: input_shapes={'images': (1, 3, 640, 640)}"
+                )
+
             model.eval()
 
             # Convert model to target precision to avoid mixed precision issues

services/stream_connection_manager.py

@@ -127,15 +127,17 @@ class StreamConnection:
         self.status = ConnectionStatus.DISCONNECTED
         logger.info(f"Stream {self.stream_id} stopped")
 
-    def _on_frame_decoded(self, frame: torch.Tensor):
+    def _on_frame_decoded(self, frame_ref):
         """
         Event handler called by decoder when a new frame is decoded.
         This is the event-driven replacement for polling.
 
         Args:
-            frame: RGB frame tensor on GPU (3, H, W)
+            frame_ref: FrameReference object containing the RGB frame tensor
         """
         if not self.running:
+            # If not running, free the frame immediately
+            frame_ref.free()
             return
 
         try:
@@ -143,12 +145,14 @@ class StreamConnection:
             self.frame_count += 1
 
             # Submit to model controller for batched inference
+            # Pass the FrameReference in metadata so we can free it later
             self.model_controller.submit_frame(
                 stream_id=self.stream_id,
-                frame=frame,
+                frame=frame_ref.rgb_tensor,
                 metadata={
                     "frame_number": self.frame_count,
-                    "shape": tuple(frame.shape),
+                    "shape": tuple(frame_ref.rgb_tensor.shape),
+                    "frame_ref": frame_ref,  # Store reference for later cleanup
                 }
             )
 
@@ -164,6 +168,8 @@ class StreamConnection:
             logger.error(f"Error processing frame for {self.stream_id}: {e}", exc_info=True)
             self.error_queue.put(e)
             self.status = ConnectionStatus.ERROR
+            # Free the frame on error
+            frame_ref.free()
 
     def _handle_inference_result(self, result: Dict[str, Any]):
         """
@@ -173,12 +179,17 @@ class StreamConnection:
         Args:
             result: Inference result dictionary
         """
+        frame_ref = None
         try:
             # Extract detections
             detections = result["detections"]
 
-            # Run tracking (synchronous)
+            # Get FrameReference from metadata (if present)
-            tracked_objects = self._run_tracking_sync(detections)
+            frame_ref = result["metadata"].get("frame_ref")
+
+            # Run tracking (synchronous) with frame shape for bbox scaling
+            frame_shape = result["metadata"].get("shape")
+            tracked_objects = self._run_tracking_sync(detections, frame_shape)
 
             # Create tracking result
             tracking_result = TrackingResult(
@@ -196,13 +207,18 @@ class StreamConnection:
         except Exception as e:
             logger.error(f"Error handling inference result for {self.stream_id}: {e}", exc_info=True)
             self.error_queue.put(e)
+        finally:
+            # Free the frame reference - this is the last point in the pipeline
+            if frame_ref is not None:
+                frame_ref.free()
 
-    def _run_tracking_sync(self, detections, min_confidence=0.7):
+    def _run_tracking_sync(self, detections, frame_shape=None, min_confidence=0.7):
         """
         Run tracking synchronously (called from executor).
 
         Args:
             detections: Detection tensor (N, 6) [x1, y1, x2, y2, conf, class_id]
+            frame_shape: Original frame shape (C, H, W) for scaling bboxes
             min_confidence: Minimum confidence threshold for detections
 
         Returns:
@@ -226,8 +242,8 @@ class StreamConnection:
                 class_name=f"class_{int(det[5])}" if det.shape[0] > 5 else "unknown"
             ))
 
-        # Update tracker with detections (lightweight, no model dependency!)
+        # Update tracker with detections (will scale bboxes to frame space)
-        return self.tracking_controller.update(detection_list)
+        return self.tracking_controller.update(detection_list, frame_shape=frame_shape)
 
     def tracking_results(self):
         """
@@ -339,15 +355,31 @@ class StreamConnectionManager:
         """
         Initialize the manager with a model.
 
+        Supports transparent loading of .pt (YOLO), .engine, and .trt files.
+        For Ultralytics YOLO models (.pt), metadata is auto-detected - no manual
+        input_shapes or precision needed! Non-YOLO models still require input_shapes.
+
         Args:
-            model_path: Path to TensorRT or PyTorch model file (.trt, .pt, .pth)
+            model_path: Path to model file (.trt, .engine, .pt, .pth)
+                       - .engine: Ultralytics native format (recommended)
+                       - .pt: Auto-converts to .engine (YOLO models only)
+                       - .trt: Raw TensorRT engine
             model_id: Model identifier (default: "detector")
             preprocess_fn: Preprocessing function (e.g., YOLOv8Utils.preprocess)
             postprocess_fn: Postprocessing function (e.g., YOLOv8Utils.postprocess)
             num_contexts: Number of TensorRT execution contexts (default: 4)
-            pt_input_shapes: Required for PT files - dict of input shapes
+            pt_input_shapes: [Optional] Only required for non-YOLO PyTorch models
-            pt_precision: Precision for PT conversion (torch.float16 or torch.float32)
+                           YOLO models auto-detect from embedded metadata
+            pt_precision: [Optional] Precision for PT conversion (auto-detected for YOLO)
             **pt_conversion_kwargs: Additional PT conversion arguments
+
+        Example:
+            # YOLO model - no manual parameters needed:
+            manager.initialize(
+                model_path="model.pt",  # or .engine
+                preprocess_fn=YOLOv8Utils.preprocess,
+                postprocess_fn=YOLOv8Utils.postprocess
+            )
         """
         logger.info(f"Initializing StreamConnectionManager on GPU {self.gpu_id}")
 

services/stream_decoder.py

@@ -12,26 +12,31 @@ from .jpeg_encoder import encode_frame_to_jpeg
 
 class FrameReference:
     """
-    CPU-side reference object for a GPU frame.
+    Reference-counted frame wrapper for zero-copy memory management.
 
-    This object holds a cloned RGB tensor that is independent of PyNvVideoCodec's
+    This allows multiple parts of the pipeline to hold references to the same
-    DecodedFrame lifecycle. We don't keep the DecodedFrame to avoid conflicts
+    cloned frame, and tracks when all references are released so the decoder
-    with PyNvVideoCodec's internal frame pool management.
+    knows when buffer slots can be reused.
     """
     def __init__(self, rgb_tensor: torch.Tensor, buffer_index: int, decoder):
-        self.rgb_tensor = rgb_tensor  # Cloned RGB tensor (independent copy)
+        self.rgb_tensor = rgb_tensor  # Cloned RGB tensor (one clone per frame)
         self.buffer_index = buffer_index
-        self.decoder = decoder  # Reference to decoder for marking as free
+        self.decoder = decoder
         self._freed = False
 
     def free(self):
-        """Mark this frame as no longer in use"""
+        """Mark this reference as freed - called by the last user of the frame"""
         if not self._freed:
             self._freed = True
+
+            # Release GPU memory immediately
+            if self.rgb_tensor is not None:
+                del self.rgb_tensor
+                self.rgb_tensor = None
             self.decoder._mark_frame_free(self.buffer_index)
 
     def is_freed(self) -> bool:
-        """Check if this frame has been freed"""
+        """Check if this reference has been freed"""
         return self._freed
 
     def __del__(self):
@@ -212,13 +217,10 @@ class StreamDecoder:
         self.status = ConnectionStatus.DISCONNECTED
         self._status_lock = threading.Lock()
 
-        # Frame buffer (ring buffer) - stores FrameReference objects
+        # Frame buffer (ring buffer) - stores cloned RGB tensors
         self.frame_buffer = deque(maxlen=buffer_size)
         self._buffer_lock = threading.RLock()
 
-        # Track which buffer slots are in use (list of FrameReference objects)
-        self._in_use_frames = []  # List of FrameReference objects currently held by callbacks
-
         # Decoder and container instances
         self.decoder = None
         self.container = None
@@ -236,6 +238,10 @@ class StreamDecoder:
         self._frame_callbacks = []
         self._callback_lock = threading.Lock()
 
+        # Track frames currently in use (referenced by callbacks/pipeline)
+        self._in_use_frames = []  # List of FrameReference objects
+        self._frame_index_counter = 0  # Monotonically increasing frame index
+
     def register_frame_callback(self, callback: Callable):
         """
         Register a callback to be called when a new frame is decoded.
@@ -396,19 +402,7 @@ class StreamDecoder:
                     # Add frames to ring buffer and fire callbacks
                     with self._buffer_lock:
                         for frame in decoded_frames:
-                            # Check for buffer overflow - discard oldest if needed
+                            # Convert to tensor immediately after NVDEC
-                            if len(self.frame_buffer) >= self.buffer_size:
-                                # Check if oldest frame is still in use
-                                if len(self._in_use_frames) > 0:
-                                    oldest_ref = self.frame_buffer[0] if len(self.frame_buffer) > 0 else None
-                                    if oldest_ref and not oldest_ref.is_freed():
-                                        # Force free the oldest frame to prevent overflow
-                                        print(f"[WARNING] Buffer overflow, force-freeing oldest frame (buffer_index={oldest_ref.buffer_index})")
-                                        oldest_ref.free()
-
-                                # Deque will automatically remove oldest when at maxlen
-
-                            # Convert to tensor
                             try:
                                 # Convert DecodedFrame to PyTorch tensor using DLPack (zero-copy)
                                 nv12_tensor = torch.from_dlpack(frame)
@@ -417,32 +411,32 @@ class StreamDecoder:
                                 if self.frame_height is not None and self.frame_width is not None:
                                     rgb_tensor = nv12_to_rgb_gpu(nv12_tensor, self.frame_height, self.frame_width)
 
-                                    # CRITICAL: Clone the RGB tensor to break CUDA memory dependency
+                                    # CLONE ONCE into our post-decode buffer
-                                    # The nv12_to_rgb_gpu creates a new tensor, but it still references
+                                    # This breaks the dependency on PyNvVideoCodec's DecodedFrame
-                                    # the same CUDA context/stream. We need an independent copy.
+                                    # After this, the tensor is fully ours and can be used throughout the pipeline
-                                    rgb_tensor_cloned = rgb_tensor.clone()
+                                    rgb_cloned = rgb_tensor.clone()
 
-                                    # Create FrameReference object for C++-style memory management
+                                    # Create FrameReference for reference counting
-                                    # We don't keep the DecodedFrame to avoid conflicts with PyNvVideoCodec's
-                                    # internal frame pool - the clone is fully independent
-                                    buffer_index = self.frame_count
                                     frame_ref = FrameReference(
-                                        rgb_tensor=rgb_tensor_cloned,  # Independent cloned tensor
+                                        rgb_tensor=rgb_cloned,
-                                        buffer_index=buffer_index,
+                                        buffer_index=self._frame_index_counter,
                                         decoder=self
                                     )
+                                    self._frame_index_counter += 1
 
-                                    # Add to buffer and in-use tracking
+                                    # Add FrameReference to ring buffer (deque automatically removes oldest when full)
                                     self.frame_buffer.append(frame_ref)
-                                    self._in_use_frames.append(frame_ref)
                                     self.frame_count += 1
 
-                                    # Fire callbacks with the cloned RGB tensor from FrameReference
+                                    # Track this frame as in-use
-                                    # The tensor is now independent of the DecodedFrame lifecycle
+                                    self._in_use_frames.append(frame_ref)
+
+                                    # Fire callbacks with the FrameReference
+                                    # The callback receivers should call .free() when done
                                     with self._callback_lock:
                                         for callback in self._frame_callbacks:
                                             try:
-                                                callback(frame_ref.rgb_tensor)
+                                                callback(frame_ref)
                                             except Exception as e:
                                                 print(f"Error in frame callback: {e}")
                             except Exception as e:

services/tracking_controller.py

@@ -272,12 +272,14 @@ class ObjectTracker:
         for tid in stale_track_ids:
             del self._tracks[tid]
 
-    def update(self, detections: List[Detection]) -> List[TrackedObject]:
+    def update(self, detections: List[Detection], frame_shape: tuple = None, model_input_size: int = 640) -> List[TrackedObject]:
         """
         Update tracker with new detections (decoupled from inference).
 
         Args:
             detections: List of Detection objects from model inference
+            frame_shape: Original frame shape (C, H, W) for scaling bboxes back from model space
+            model_input_size: Model input size (default: 640 for YOLOv8)
 
         Returns:
             List of currently tracked objects
@@ -291,6 +293,22 @@ class ObjectTracker:
                 self._cleanup_stale_tracks()
                 return list(self._tracks.values())
 
+            # Scale detections from model space (640x640) to frame space (H x W)
+            if frame_shape is not None:
+                _, frame_h, frame_w = frame_shape
+                scale_x = frame_w / model_input_size
+                scale_y = frame_h / model_input_size
+
+                # Scale all detection bboxes
+                for det in detections:
+                    x1, y1, x2, y2 = det.bbox
+                    det.bbox = [
+                        x1 * scale_x,
+                        y1 * scale_y,
+                        x2 * scale_x,
+                        y2 * scale_y
+                    ]
+
             # Convert detections to tensor for GPU processing
             det_tensor = torch.tensor(
                 [[*det.bbox, det.confidence, det.class_id] for det in detections],

services/yolo.py

@@ -63,6 +63,10 @@ class YOLOv8Utils:
         # Normalize to [0, 1] (YOLOv8 expects normalized input)
         frame_normalized = frame_resized / 255.0
 
+        # NOTE: Don't track these tensors - they're short-lived inputs to TensorRT
+        # that get automatically freed by PyTorch after inference completes.
+        # Tracking them would show false "leaks" since we can't track when TensorRT consumes them.
+
         return frame_normalized
 
     @staticmethod

test_event_driven.py

@@ -43,8 +43,8 @@ async def example_callback_pattern():
         poll_interval=0.01,  # 100 FPS
     )
 
-    # Initialize with YOLOv8 model
+    # Initialize with YOLOv8 model (transparent loading: .pt, .engine, or .trt)
-    model_path = "models/yolov8n.trt"  # Adjust path as needed
+    model_path = "models/yolov8n.trt"  # Can also use .pt or .engine
     if not os.path.exists(model_path):
         logger.error(f"Model file not found: {model_path}")
         return
@@ -53,7 +53,8 @@ async def example_callback_pattern():
         model_path=model_path,
         model_id="yolo",
         preprocess_fn=YOLOv8Utils.preprocess,
-        postprocess_fn=YOLOv8Utils.postprocess,
+        postprocess_fn=YOLOv8Utils.postprocess
+        # Note: No manual parameters needed for YOLO models
     )
 
     # Define callback for tracking results

test_tracking_realtime.py

@@ -24,7 +24,6 @@ from services import (
 # Load environment variables
 load_dotenv()
 
-
 def main_single_stream():
     """Single stream example with event-driven architecture."""
     print("=" * 80)
@@ -33,7 +32,7 @@ def main_single_stream():
 
     # Configuration
     GPU_ID = 0
-    MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"  # PT file will be auto-converted
+    MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"  # Transparent loading: .pt, .engine, or .trt
     STREAM_URL = os.getenv('CAMERA_URL_1', 'rtsp://localhost:8554/test')
     BATCH_SIZE = 4
     FORCE_TIMEOUT = 0.05
@@ -59,10 +58,10 @@ def main_single_stream():
     )
     print("✓ Manager created")
 
-    # Initialize with PT model (auto-conversion)
+    # Initialize with model (transparent loading - no manual parameters needed)
-    print("\n[2/3] Initializing with PT model...")
+    print("\n[2/3] Initializing model...")
-    print("Note: First load will convert PT to TensorRT (3-5 minutes)")
+    print("Note: YOLO models auto-convert to native TensorRT .engine (first time only)")
-    print("Subsequent loads will use cached TensorRT engine\n")
+    print("Metadata is auto-detected from model - no manual input_shapes needed!\n")
 
     try:
         manager.initialize(
@@ -70,11 +69,10 @@ def main_single_stream():
             model_id="detector",
             preprocess_fn=YOLOv8Utils.preprocess,
             postprocess_fn=YOLOv8Utils.postprocess,
-            num_contexts=4,
+            num_contexts=4
-            pt_input_shapes={"images": (1, 3, 640, 640)},
+            # Note: No pt_input_shapes or pt_precision needed for YOLO models!
-            pt_precision=torch.float16
         )
-        print("✓ Manager initialized (PT converted to TensorRT)")
+        print("✓ Manager initialized")
     except Exception as e:
         print(f"✗ Failed to initialize: {e}")
         import traceback
@@ -176,6 +174,7 @@ def main_single_stream():
                         class_counts[obj.class_name] = class_counts.get(obj.class_name, 0) + 1
                     print(f"  Classes: {class_counts}")
 
+
     except KeyboardInterrupt:
         print(f"\n✓ Interrupted by user")
 
@@ -206,7 +205,7 @@ def main_multi_stream():
 
     # Configuration
     GPU_ID = 0
-    MODEL_PATH = "models/yolov8n.pt"  # PT file will be auto-converted
+    MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"  # Transparent loading: .pt, .engine, or .trt
     BATCH_SIZE = 16
     FORCE_TIMEOUT = 0.05
 
@@ -241,17 +240,16 @@ def main_multi_stream():
     )
     print("✓ Manager created")
 
-    # Initialize with PT model
+    # Initialize model (transparent loading)
-    print("\n[2/3] Initializing with PT model...")
+    print("\n[2/3] Initializing model...")
     try:
         manager.initialize(
             model_path=MODEL_PATH,
             model_id="detector",
             preprocess_fn=YOLOv8Utils.preprocess,
             postprocess_fn=YOLOv8Utils.postprocess,
-            num_contexts=8,
+            num_contexts=8
-            pt_input_shapes={"images": (1, 3, 640, 640)},
+            # Note: No pt_input_shapes or pt_precision needed for YOLO models!
-            pt_precision=torch.float16
         )
         print("✓ Manager initialized")
     except Exception as e:
@@ -312,6 +310,7 @@ def main_multi_stream():
                         s_fps = stats['count'] / s_elapsed if s_elapsed > 0 else 0
                         print(f"  {sid}: {stats['count']} ({s_fps:.1f} FPS)")
 
+
     except KeyboardInterrupt:
         print(f"\n✓ Interrupted")