Auto convert ONNX models to RKNN format (#19674)

* Implement base rknn conversion

* Remove unused

* Formatting

* Add model conversion lock so it doesn't break when multiple detectors are defined

* Ignore unused impor
t

.github/DISCUSSION_TEMPLATE/report-a-bug.yml

@@ -6,7 +6,7 @@ body:
       value: |
         Use this form to submit a reproducible bug in Frigate or Frigate's UI.
 
-        Before submitting your bug report, please [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
+        Before submitting your bug report, please ask the AI with the "Ask AI" button on the [official documentation site][ai] about your issue, [search the discussions][discussions], look at recent open and closed [pull requests][prs], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your bug has already been fixed by the developers or reported by the community.
 
         **If you are unsure if your issue is actually a bug or not, please submit a support request first.**
 
@@ -14,6 +14,7 @@ body:
         [prs]: https://www.github.com/blakeblackshear/frigate/pulls
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai]: https://docs.frigate.video
   - type: checkboxes
     attributes:
       label: Checklist
@@ -26,6 +27,8 @@ body:
         - label: I have tried a different browser to see if it is related to my browser.
           required: true
         - label: I have tried reproducing the issue in [incognito mode](https://www.computerworld.com/article/1719851/how-to-go-incognito-in-chrome-firefox-safari-and-edge.html) to rule out problems with any third party extensions or plugins I have installed.
+        - label: I have asked the AI at https://docs.frigate.video about my issue.
+          required: true
   - type: textarea
     id: description
     attributes:

frigate/detectors/plugins/rknn.py

@@ -12,6 +12,7 @@ from frigate.const import MODEL_CACHE_DIR
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
 from frigate.util.model import post_process_yolo
+from frigate.util.rknn_converter import auto_convert_model
 
 logger = logging.getLogger(__name__)
 
@@ -94,7 +95,31 @@ class Rknn(DetectionApi):
         # user provided models should be a path and contain a "/"
         if "/" in model_path:
             model_props["preset"] = False
-            model_props["path"] = model_path
+
+            # Check if this is an ONNX model or model without extension that needs conversion
+            if model_path.endswith(".onnx") or not os.path.splitext(model_path)[1]:
+                # Try to auto-convert to RKNN format
+                logger.info(
+                    f"Attempting to auto-convert {model_path} to RKNN format..."
+                )
+
+                # Determine model type from config
+                model_type = self.detector_config.model.model_type
+
+                # Auto-convert the model
+                converted_path = auto_convert_model(model_path, model_type.value)
+
+                if converted_path:
+                    model_props["path"] = converted_path
+                    logger.info(f"Successfully converted model to: {converted_path}")
+                else:
+                    # Fall back to original path if conversion fails
+                    logger.warning(
+                        f"Failed to convert {model_path} to RKNN format, using original path"
+                    )
+                    model_props["path"] = model_path
+            else:
+                model_props["path"] = model_path
         else:
             model_props["preset"] = True
 

frigate/record/cleanup.py

@@ -308,7 +308,12 @@ class RecordingCleanup(threading.Thread):
                 now - datetime.timedelta(days=config.record.continuous.days)
             ).timestamp()
             motion_expire_date = (
-                now - datetime.timedelta(days=config.record.motion.days)
+                now
+                - datetime.timedelta(
+                    days=max(
+                        config.record.motion.days, config.record.continuous.days
+                    )  # can't keep motion for less than continuous
+                )
             ).timestamp()
 
             # Get all the reviews to check against

frigate/util/config.py

@@ -363,6 +363,10 @@ def migrate_017_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
         if days:
             if mode == "all":
                 continuous["days"] = days
+
+                # if a user was keeping all for number of days
+                # we need to keep motion and all for that number of days
+                motion["days"] = days
             else:
                 motion["days"] = days
 

frigate/util/rknn_converter.py

@@ -0,0 +1,401 @@
+"""RKNN model conversion utility for Frigate."""
+
+import fcntl
+import logging
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+MODEL_TYPE_CONFIGS = {
+    "yolo-generic": {
+        "mean_values": [[0, 0, 0]],
+        "std_values": [[255, 255, 255]],
+        "target_platform": None,  # Will be set dynamically
+    },
+    "yolonas": {
+        "mean_values": [[0, 0, 0]],
+        "std_values": [[255, 255, 255]],
+        "target_platform": None,  # Will be set dynamically
+    },
+    "yolox": {
+        "mean_values": [[0, 0, 0]],
+        "std_values": [[255, 255, 255]],
+        "target_platform": None,  # Will be set dynamically
+    },
+}
+
+
+def ensure_torch_dependencies() -> bool:
+    """Dynamically install torch dependencies if not available."""
+    try:
+        import torch  # type: ignore
+
+        logger.debug("PyTorch is already available")
+        return True
+    except ImportError:
+        logger.info("PyTorch not found, attempting to install...")
+
+        try:
+            subprocess.check_call(
+                [
+                    sys.executable,
+                    "-m",
+                    "pip",
+                    "install",
+                    "--break-system-packages",
+                    "torch",
+                    "torchvision",
+                ],
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+            )
+
+            import torch  # type: ignore # noqa: F401
+
+            logger.info("PyTorch installed successfully")
+            return True
+        except (subprocess.CalledProcessError, ImportError) as e:
+            logger.error(f"Failed to install PyTorch: {e}")
+            return False
+
+
+def ensure_rknn_toolkit() -> bool:
+    """Ensure RKNN toolkit is available."""
+    try:
+        import rknn  # type: ignore  # noqa: F401
+        from rknn.api import RKNN  # type: ignore # noqa: F401
+
+        logger.debug("RKNN toolkit is already available")
+        return True
+    except ImportError:
+        logger.error("RKNN toolkit not found. Please ensure it's installed.")
+        return False
+
+
+def get_soc_type() -> Optional[str]:
+    """Get the SoC type from device tree."""
+    try:
+        with open("/proc/device-tree/compatible") as file:
+            soc = file.read().split(",")[-1].strip("\x00")
+            return soc
+    except FileNotFoundError:
+        logger.warning("Could not determine SoC type from device tree")
+        return None
+
+
+def convert_onnx_to_rknn(
+    onnx_path: str,
+    output_path: str,
+    model_type: str,
+    quantization: bool = False,
+    soc: Optional[str] = None,
+) -> bool:
+    """
+    Convert ONNX model to RKNN format.
+
+    Args:
+        onnx_path: Path to input ONNX model
+        output_path: Path for output RKNN model
+        model_type: Type of model (yolo-generic, yolonas, yolox, ssd)
+        quantization: Whether to use 8-bit quantization (i8) or 16-bit float (fp16)
+        soc: Target SoC platform (auto-detected if None)
+
+    Returns:
+        True if conversion successful, False otherwise
+    """
+    if not ensure_torch_dependencies():
+        logger.error("PyTorch dependencies not available")
+        return False
+
+    if not ensure_rknn_toolkit():
+        logger.error("RKNN toolkit not available")
+        return False
+
+    # Get SoC type if not provided
+    if soc is None:
+        soc = get_soc_type()
+        if soc is None:
+            logger.error("Could not determine SoC type")
+            return False
+
+    # Get model config for the specified type
+    if model_type not in MODEL_TYPE_CONFIGS:
+        logger.error(f"Unsupported model type: {model_type}")
+        return False
+
+    config = MODEL_TYPE_CONFIGS[model_type].copy()
+    config["target_platform"] = soc
+
+    try:
+        from rknn.api import RKNN  # type: ignore
+
+        logger.info(f"Converting {onnx_path} to RKNN format for {soc}")
+        rknn = RKNN(verbose=True)
+        rknn.config(**config)
+
+        if rknn.load_onnx(model=onnx_path) != 0:
+            logger.error("Failed to load ONNX model")
+            return False
+
+        if rknn.build(do_quantization=quantization) != 0:
+            logger.error("Failed to build RKNN model")
+            return False
+
+        if rknn.export_rknn(output_path) != 0:
+            logger.error("Failed to export RKNN model")
+            return False
+
+        logger.info(f"Successfully converted model to {output_path}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Error during RKNN conversion: {e}")
+        return False
+
+
+def cleanup_stale_lock(lock_file_path: Path) -> bool:
+    """
+    Clean up a stale lock file if it exists and is old.
+
+    Args:
+        lock_file_path: Path to the lock file
+
+    Returns:
+        True if lock was cleaned up, False otherwise
+    """
+    try:
+        if lock_file_path.exists():
+            # Check if lock file is older than 10 minutes (stale)
+            lock_age = time.time() - lock_file_path.stat().st_mtime
+            if lock_age > 600:  # 10 minutes
+                logger.warning(
+                    f"Removing stale lock file: {lock_file_path} (age: {lock_age:.1f}s)"
+                )
+                lock_file_path.unlink()
+                return True
+    except Exception as e:
+        logger.error(f"Error cleaning up stale lock: {e}")
+
+    return False
+
+
+def acquire_conversion_lock(lock_file_path: Path, timeout: int = 300) -> bool:
+    """
+    Acquire a file-based lock for model conversion.
+
+    Args:
+        lock_file_path: Path to the lock file
+        timeout: Maximum time to wait for lock in seconds
+
+    Returns:
+        True if lock acquired, False if timeout or error
+    """
+    try:
+        lock_file_path.parent.mkdir(parents=True, exist_ok=True)
+        cleanup_stale_lock(lock_file_path)
+        lock_fd = os.open(lock_file_path, os.O_CREAT | os.O_RDWR)
+
+        # Try to acquire exclusive lock
+        start_time = time.time()
+        while time.time() - start_time < timeout:
+            try:
+                fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+                # Lock acquired successfully
+                logger.debug(f"Acquired conversion lock: {lock_file_path}")
+                return True
+            except (OSError, IOError):
+                # Lock is held by another process, wait and retry
+                if time.time() - start_time >= timeout:
+                    logger.warning(
+                        f"Timeout waiting for conversion lock: {lock_file_path}"
+                    )
+                    os.close(lock_fd)
+                    return False
+
+                logger.debug("Waiting for conversion lock to be released...")
+                time.sleep(1)
+
+        os.close(lock_fd)
+        return False
+
+    except Exception as e:
+        logger.error(f"Error acquiring conversion lock: {e}")
+        return False
+
+
+def release_conversion_lock(lock_file_path: Path) -> None:
+    """
+    Release the conversion lock.
+
+    Args:
+        lock_file_path: Path to the lock file
+    """
+    try:
+        if lock_file_path.exists():
+            lock_file_path.unlink()
+            logger.debug(f"Released conversion lock: {lock_file_path}")
+    except Exception as e:
+        logger.error(f"Error releasing conversion lock: {e}")
+
+
+def is_lock_stale(lock_file_path: Path, max_age: int = 600) -> bool:
+    """
+    Check if a lock file is stale (older than max_age seconds).
+
+    Args:
+        lock_file_path: Path to the lock file
+        max_age: Maximum age in seconds before considering lock stale
+
+    Returns:
+        True if lock is stale, False otherwise
+    """
+    try:
+        if lock_file_path.exists():
+            lock_age = time.time() - lock_file_path.stat().st_mtime
+            return lock_age > max_age
+    except Exception:
+        pass
+
+    return False
+
+
+def wait_for_conversion_completion(
+    rknn_path: Path, lock_file_path: Path, timeout: int = 300
+) -> bool:
+    """
+    Wait for another process to complete the conversion.
+
+    Args:
+        rknn_path: Path to the expected RKNN model
+        lock_file_path: Path to the lock file to monitor
+        timeout: Maximum time to wait in seconds
+
+    Returns:
+        True if RKNN model appears, False if timeout
+    """
+    start_time = time.time()
+    while time.time() - start_time < timeout:
+        # Check if RKNN model appeared
+        if rknn_path.exists():
+            logger.info(f"RKNN model appeared: {rknn_path}")
+            return True
+
+        # Check if lock file is gone (conversion completed or failed)
+        if not lock_file_path.exists():
+            logger.info("Lock file removed, checking for RKNN model...")
+            if rknn_path.exists():
+                logger.info(f"RKNN model found after lock removal: {rknn_path}")
+                return True
+            else:
+                logger.warning(
+                    "Lock file removed but RKNN model not found, conversion may have failed"
+                )
+                return False
+
+        # Check if lock is stale
+        if is_lock_stale(lock_file_path):
+            logger.warning("Lock file is stale, attempting to clean up and retry...")
+            cleanup_stale_lock(lock_file_path)
+            # Try to acquire lock again
+            if acquire_conversion_lock(lock_file_path, timeout=60):
+                try:
+                    # Check if RKNN file appeared while waiting
+                    if rknn_path.exists():
+                        logger.info(f"RKNN model appeared while waiting: {rknn_path}")
+                        return str(rknn_path)
+
+                    # Convert ONNX to RKNN
+                    logger.info(
+                        f"Retrying conversion of {rknn_path} after stale lock cleanup..."
+                    )
+
+                    # Get the original model path from rknn_path
+                    base_path = rknn_path.parent / rknn_path.stem
+                    onnx_path = base_path.with_suffix(".onnx")
+
+                    if onnx_path.exists():
+                        if convert_onnx_to_rknn(
+                            str(onnx_path), str(rknn_path), "yolo-generic", False
+                        ):
+                            return str(rknn_path)
+
+                    logger.error("Failed to convert model after stale lock cleanup")
+                    return None
+
+                finally:
+                    release_conversion_lock(lock_file_path)
+
+        logger.debug("Waiting for RKNN model to appear...")
+        time.sleep(1)
+
+    logger.warning(f"Timeout waiting for RKNN model: {rknn_path}")
+    return False
+
+
+def auto_convert_model(
+    model_path: str, model_type: str, quantization: bool = False
+) -> Optional[str]:
+    """
+    Automatically convert a model to RKNN format if needed.
+
+    Args:
+        model_path: Path to the model file
+        model_type: Type of the model
+        quantization: Whether to use quantization
+
+    Returns:
+        Path to the RKNN model if successful, None otherwise
+    """
+    if model_path.endswith(".rknn"):
+        return model_path
+
+    # Check if equivalent .rknn file exists
+    base_path = Path(model_path)
+    if base_path.suffix.lower() in [".onnx", ""]:
+        base_name = base_path.stem if base_path.suffix else base_path.name
+        rknn_path = base_path.parent / f"{base_name}.rknn"
+
+        if rknn_path.exists():
+            logger.info(f"Found existing RKNN model: {rknn_path}")
+            return str(rknn_path)
+
+        lock_file_path = base_path.parent / f"{base_name}.conversion.lock"
+
+        if acquire_conversion_lock(lock_file_path):
+            try:
+                if rknn_path.exists():
+                    logger.info(
+                        f"RKNN model appeared while waiting for lock: {rknn_path}"
+                    )
+                    return str(rknn_path)
+
+                logger.info(f"Converting {model_path} to RKNN format...")
+                rknn_path.parent.mkdir(parents=True, exist_ok=True)
+
+                if convert_onnx_to_rknn(
+                    str(base_path), str(rknn_path), model_type, quantization
+                ):
+                    return str(rknn_path)
+                else:
+                    logger.error(f"Failed to convert {model_path} to RKNN format")
+                    return None
+
+            finally:
+                release_conversion_lock(lock_file_path)
+        else:
+            logger.info(
+                f"Another process is converting {model_path}, waiting for completion..."
+            )
+
+            if wait_for_conversion_completion(rknn_path, lock_file_path):
+                return str(rknn_path)
+            else:
+                logger.error(f"Timeout waiting for conversion of {model_path}")
+                return None
+
+    return None

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -19,8 +19,8 @@
       },
       "outputs": [],
       "source": [
-        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.11/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/pretrained_models.py\n",
-        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.11/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
       ]
     },
     {