mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-09-14 17:52:10 +02:00
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
This commit is contained in:
Nicolas Mowen
GitHub
parent
6e3b40eaee
commit
2236ecf23f
@ -12,6 +12,7 @@ from frigate.const import MODEL_CACHE_DIR
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
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from frigate.util.model import post_process_yolo
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from frigate.util.rknn_converter import auto_convert_model
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logger = logging.getLogger(__name__)
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@ -94,6 +95,30 @@ class Rknn(DetectionApi):
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# user provided models should be a path and contain a "/"
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if "/" in model_path:
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model_props["preset"] = False
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# Check if this is an ONNX model or model without extension that needs conversion
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if model_path.endswith(".onnx") or not os.path.splitext(model_path)[1]:
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# Try to auto-convert to RKNN format
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logger.info(
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f"Attempting to auto-convert {model_path} to RKNN format..."
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)
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# Determine model type from config
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model_type = self.detector_config.model.model_type
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# Auto-convert the model
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converted_path = auto_convert_model(model_path, model_type.value)
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if converted_path:
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model_props["path"] = converted_path
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logger.info(f"Successfully converted model to: {converted_path}")
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else:
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# Fall back to original path if conversion fails
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logger.warning(
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f"Failed to convert {model_path} to RKNN format, using original path"
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)
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model_props["path"] = model_path
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else:
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model_props["path"] = model_path
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else:
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model_props["preset"] = True
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401
frigate/util/rknn_converter.py
Normal file
401
frigate/util/rknn_converter.py
Normal file
@ -0,0 +1,401 @@
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"""RKNN model conversion utility for Frigate."""
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import fcntl
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import logging
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import os
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import subprocess
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import sys
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import time
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from pathlib import Path
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from typing import Optional
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logger = logging.getLogger(__name__)
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MODEL_TYPE_CONFIGS = {
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"yolo-generic": {
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"mean_values": [[0, 0, 0]],
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"std_values": [[255, 255, 255]],
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"target_platform": None, # Will be set dynamically
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},
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"yolonas": {
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"mean_values": [[0, 0, 0]],
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"std_values": [[255, 255, 255]],
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"target_platform": None, # Will be set dynamically
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},
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"yolox": {
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"mean_values": [[0, 0, 0]],
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"std_values": [[255, 255, 255]],
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"target_platform": None, # Will be set dynamically
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},
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}
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def ensure_torch_dependencies() -> bool:
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"""Dynamically install torch dependencies if not available."""
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try:
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import torch # type: ignore
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logger.debug("PyTorch is already available")
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return True
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except ImportError:
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logger.info("PyTorch not found, attempting to install...")
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try:
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subprocess.check_call(
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[
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sys.executable,
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"-m",
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"pip",
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"install",
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"--break-system-packages",
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"torch",
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"torchvision",
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],
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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)
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import torch # type: ignore # noqa: F401
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logger.info("PyTorch installed successfully")
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return True
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except (subprocess.CalledProcessError, ImportError) as e:
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logger.error(f"Failed to install PyTorch: {e}")
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return False
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def ensure_rknn_toolkit() -> bool:
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"""Ensure RKNN toolkit is available."""
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try:
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import rknn # type: ignore # noqa: F401
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from rknn.api import RKNN # type: ignore # noqa: F401
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logger.debug("RKNN toolkit is already available")
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return True
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except ImportError:
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logger.error("RKNN toolkit not found. Please ensure it's installed.")
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return False
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def get_soc_type() -> Optional[str]:
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"""Get the SoC type from device tree."""
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try:
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with open("/proc/device-tree/compatible") as file:
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soc = file.read().split(",")[-1].strip("\x00")
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return soc
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except FileNotFoundError:
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logger.warning("Could not determine SoC type from device tree")
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return None
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def convert_onnx_to_rknn(
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onnx_path: str,
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output_path: str,
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model_type: str,
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quantization: bool = False,
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soc: Optional[str] = None,
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) -> bool:
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"""
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Convert ONNX model to RKNN format.
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Args:
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onnx_path: Path to input ONNX model
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output_path: Path for output RKNN model
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model_type: Type of model (yolo-generic, yolonas, yolox, ssd)
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quantization: Whether to use 8-bit quantization (i8) or 16-bit float (fp16)
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soc: Target SoC platform (auto-detected if None)
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Returns:
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True if conversion successful, False otherwise
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"""
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if not ensure_torch_dependencies():
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logger.error("PyTorch dependencies not available")
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return False
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if not ensure_rknn_toolkit():
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logger.error("RKNN toolkit not available")
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return False
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# Get SoC type if not provided
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if soc is None:
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soc = get_soc_type()
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if soc is None:
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logger.error("Could not determine SoC type")
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return False
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# Get model config for the specified type
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if model_type not in MODEL_TYPE_CONFIGS:
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logger.error(f"Unsupported model type: {model_type}")
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return False
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config = MODEL_TYPE_CONFIGS[model_type].copy()
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config["target_platform"] = soc
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try:
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from rknn.api import RKNN # type: ignore
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logger.info(f"Converting {onnx_path} to RKNN format for {soc}")
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rknn = RKNN(verbose=True)
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rknn.config(**config)
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if rknn.load_onnx(model=onnx_path) != 0:
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logger.error("Failed to load ONNX model")
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return False
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if rknn.build(do_quantization=quantization) != 0:
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logger.error("Failed to build RKNN model")
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return False
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if rknn.export_rknn(output_path) != 0:
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logger.error("Failed to export RKNN model")
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return False
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logger.info(f"Successfully converted model to {output_path}")
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return True
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except Exception as e:
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logger.error(f"Error during RKNN conversion: {e}")
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return False
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def cleanup_stale_lock(lock_file_path: Path) -> bool:
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"""
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Clean up a stale lock file if it exists and is old.
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Args:
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lock_file_path: Path to the lock file
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Returns:
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True if lock was cleaned up, False otherwise
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"""
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try:
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if lock_file_path.exists():
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# Check if lock file is older than 10 minutes (stale)
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lock_age = time.time() - lock_file_path.stat().st_mtime
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if lock_age > 600: # 10 minutes
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logger.warning(
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f"Removing stale lock file: {lock_file_path} (age: {lock_age:.1f}s)"
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)
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lock_file_path.unlink()
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return True
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except Exception as e:
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logger.error(f"Error cleaning up stale lock: {e}")
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return False
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def acquire_conversion_lock(lock_file_path: Path, timeout: int = 300) -> bool:
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"""
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Acquire a file-based lock for model conversion.
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Args:
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lock_file_path: Path to the lock file
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timeout: Maximum time to wait for lock in seconds
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Returns:
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True if lock acquired, False if timeout or error
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"""
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try:
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lock_file_path.parent.mkdir(parents=True, exist_ok=True)
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cleanup_stale_lock(lock_file_path)
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lock_fd = os.open(lock_file_path, os.O_CREAT | os.O_RDWR)
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# Try to acquire exclusive lock
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start_time = time.time()
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while time.time() - start_time < timeout:
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try:
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fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
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# Lock acquired successfully
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logger.debug(f"Acquired conversion lock: {lock_file_path}")
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return True
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except (OSError, IOError):
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# Lock is held by another process, wait and retry
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if time.time() - start_time >= timeout:
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logger.warning(
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f"Timeout waiting for conversion lock: {lock_file_path}"
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)
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os.close(lock_fd)
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return False
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logger.debug("Waiting for conversion lock to be released...")
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time.sleep(1)
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os.close(lock_fd)
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return False
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except Exception as e:
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logger.error(f"Error acquiring conversion lock: {e}")
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return False
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def release_conversion_lock(lock_file_path: Path) -> None:
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"""
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Release the conversion lock.
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Args:
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lock_file_path: Path to the lock file
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"""
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try:
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if lock_file_path.exists():
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lock_file_path.unlink()
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logger.debug(f"Released conversion lock: {lock_file_path}")
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except Exception as e:
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logger.error(f"Error releasing conversion lock: {e}")
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def is_lock_stale(lock_file_path: Path, max_age: int = 600) -> bool:
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"""
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Check if a lock file is stale (older than max_age seconds).
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Args:
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lock_file_path: Path to the lock file
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max_age: Maximum age in seconds before considering lock stale
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Returns:
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True if lock is stale, False otherwise
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"""
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try:
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if lock_file_path.exists():
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lock_age = time.time() - lock_file_path.stat().st_mtime
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return lock_age > max_age
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except Exception:
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pass
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return False
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def wait_for_conversion_completion(
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rknn_path: Path, lock_file_path: Path, timeout: int = 300
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) -> bool:
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"""
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Wait for another process to complete the conversion.
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Args:
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rknn_path: Path to the expected RKNN model
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lock_file_path: Path to the lock file to monitor
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timeout: Maximum time to wait in seconds
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Returns:
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True if RKNN model appears, False if timeout
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"""
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start_time = time.time()
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while time.time() - start_time < timeout:
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# Check if RKNN model appeared
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if rknn_path.exists():
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logger.info(f"RKNN model appeared: {rknn_path}")
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return True
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# Check if lock file is gone (conversion completed or failed)
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if not lock_file_path.exists():
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logger.info("Lock file removed, checking for RKNN model...")
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if rknn_path.exists():
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logger.info(f"RKNN model found after lock removal: {rknn_path}")
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return True
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else:
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logger.warning(
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"Lock file removed but RKNN model not found, conversion may have failed"
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)
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return False
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# Check if lock is stale
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if is_lock_stale(lock_file_path):
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logger.warning("Lock file is stale, attempting to clean up and retry...")
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cleanup_stale_lock(lock_file_path)
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# Try to acquire lock again
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if acquire_conversion_lock(lock_file_path, timeout=60):
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try:
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# Check if RKNN file appeared while waiting
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if rknn_path.exists():
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logger.info(f"RKNN model appeared while waiting: {rknn_path}")
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return str(rknn_path)
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# Convert ONNX to RKNN
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logger.info(
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f"Retrying conversion of {rknn_path} after stale lock cleanup..."
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)
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# Get the original model path from rknn_path
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base_path = rknn_path.parent / rknn_path.stem
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onnx_path = base_path.with_suffix(".onnx")
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if onnx_path.exists():
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if convert_onnx_to_rknn(
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str(onnx_path), str(rknn_path), "yolo-generic", False
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):
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return str(rknn_path)
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logger.error("Failed to convert model after stale lock cleanup")
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return None
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finally:
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release_conversion_lock(lock_file_path)
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logger.debug("Waiting for RKNN model to appear...")
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time.sleep(1)
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logger.warning(f"Timeout waiting for RKNN model: {rknn_path}")
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return False
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def auto_convert_model(
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model_path: str, model_type: str, quantization: bool = False
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) -> Optional[str]:
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"""
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Automatically convert a model to RKNN format if needed.
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Args:
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model_path: Path to the model file
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model_type: Type of the model
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quantization: Whether to use quantization
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Returns:
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Path to the RKNN model if successful, None otherwise
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"""
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if model_path.endswith(".rknn"):
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return model_path
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# Check if equivalent .rknn file exists
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base_path = Path(model_path)
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if base_path.suffix.lower() in [".onnx", ""]:
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base_name = base_path.stem if base_path.suffix else base_path.name
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rknn_path = base_path.parent / f"{base_name}.rknn"
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if rknn_path.exists():
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logger.info(f"Found existing RKNN model: {rknn_path}")
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return str(rknn_path)
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lock_file_path = base_path.parent / f"{base_name}.conversion.lock"
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if acquire_conversion_lock(lock_file_path):
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try:
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if rknn_path.exists():
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logger.info(
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f"RKNN model appeared while waiting for lock: {rknn_path}"
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)
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return str(rknn_path)
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logger.info(f"Converting {model_path} to RKNN format...")
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rknn_path.parent.mkdir(parents=True, exist_ok=True)
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if convert_onnx_to_rknn(
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str(base_path), str(rknn_path), model_type, quantization
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):
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return str(rknn_path)
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else:
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logger.error(f"Failed to convert {model_path} to RKNN format")
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return None
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finally:
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release_conversion_lock(lock_file_path)
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else:
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logger.info(
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f"Another process is converting {model_path}, waiting for completion..."
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)
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if wait_for_conversion_completion(rknn_path, lock_file_path):
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return str(rknn_path)
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else:
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logger.error(f"Timeout waiting for conversion of {model_path}")
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return None
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return None
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