mirror of
https://github.com/blakeblackshear/frigate.git
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Final Async Update
This commit is contained in:
OmriAx
parent
eadd55eec6
commit
7813476500
@ -1,322 +1,192 @@
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import logging
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import os
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import queue
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import threading
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import subprocess
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import urllib.request
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from typing import Optional
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import numpy as np
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try:
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from hailo_platform import (
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HEF,
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ConfigureParams,
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FormatType,
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HailoRTException,
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HailoStreamInterface,
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InferVStreams,
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InputVStreamParams,
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OutputVStreamParams,
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VDevice,
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)
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except ModuleNotFoundError:
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pass
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from pydantic import BaseModel, Field
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from typing_extensions import Literal
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from hailo_platform import (
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HEF,
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ConfigureParams,
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FormatType,
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HailoRTException,
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HailoStreamInterface,
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VDevice,
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HailoSchedulingAlgorithm,
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InferVStreams,
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InputVStreamParams,
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OutputVStreamParams
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)
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from pydantic import BaseModel, Field
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from typing_extensions import Literal
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from typing import Optional
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# Set up logging
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logger = logging.getLogger(__name__)
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# Define the detector key for Hailo
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DETECTOR_KEY = "hailo8l"
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# Configuration class for model settings
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def get_device_architecture():
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"""Get the device architecture from hailortcli."""
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try:
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result = subprocess.run(['hailortcli', 'fw-control', 'identify'], capture_output=True, text=True)
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for line in result.stdout.split('\n'):
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if "Device Architecture" in line:
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return line.split(':')[1].strip().lower()
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except Exception:
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return "unknown"
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class ModelConfig(BaseModel):
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path: Optional[str] = Field(default=None, title="Model Path")
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type: str = Field(default="ssd_mobilenet_v1", title="Model Type")
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url: str = Field(default="", title="Model URL")
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width: int = Field(default=300, title="Model Width")
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height: int = Field(default=300, title="Model Height")
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type: str = Field(default="yolov8s", title="Model Type")
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width: int = Field(default=640, title="Model Width")
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height: int = Field(default=640, title="Model Height")
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score_threshold: float = Field(default=0.3, title="Score Threshold")
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max_detections: int = Field(default=30, title="Maximum Detections")
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input_tensor: str = Field(default="input_tensor", title="Input Tensor Name")
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input_pixel_format: str = Field(default="RGB", title="Input Pixel Format")
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# Configuration class for Hailo detector
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class HailoDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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device: str = Field(default="PCIe", title="Device Type")
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model: ModelConfig
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# Hailo detector class implementation
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class HailoAsyncInference:
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def __init__(self, config: HailoDetectorConfig):
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self.config = config
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self.input_queue = queue.Queue()
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self.output_queue = queue.Queue()
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params = VDevice.create_params()
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params.scheduling_algorithm = HailoSchedulingAlgorithm.ROUND_ROBIN
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self.target = VDevice(params)
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self.hef = HEF(self.config.model.path)
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self.infer_model = self.target.create_infer_model(self.config.model.path)
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self.infer_model.set_batch_size(1)
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def infer(self):
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while True:
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batch_data = self.input_queue.get()
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if batch_data is None:
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break
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bindings = []
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for frame in batch_data:
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binding = self.infer_model.create_bindings()
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binding.input().set_buffer(frame)
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bindings.append(binding)
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self.infer_model.run_async(bindings, self._callback, batch_data)
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def _callback(self, completion_info, bindings_list, batch_data):
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if completion_info.exception:
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logger.error(f"Inference error: {completion_info.exception}")
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else:
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results = [binding.output().get_buffer() for binding in bindings_list]
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self.output_queue.put((batch_data, results))
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def stop(self):
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self.input_queue.put(None)
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class HailoDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: HailoDetectorConfig):
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# Initialize base configuration
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self.h8l_device_type = detector_config.device
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self.h8l_model_path = detector_config.model.path
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self.h8l_model_height = detector_config.model.height
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self.h8l_model_width = detector_config.model.width
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self.h8l_model_type = detector_config.model.type
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self.h8l_tensor_format = detector_config.model.input_tensor
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self.h8l_pixel_format = detector_config.model.input_pixel_format
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self.model_url = detector_config.model.url
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self.score_threshold = detector_config.model.score_threshold
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self.max_detections = detector_config.model.max_detections
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self.cache_dir = "/config/model_cache/h8l_cache"
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def __init__(self, config: HailoDetectorConfig):
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super().__init__()
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self.async_inference = HailoAsyncInference(config)
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self.worker_thread = threading.Thread(target=self.async_inference.infer)
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self.worker_thread.start()
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logger.info(f"Initializing Hailo device as {self.h8l_device_type}")
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# Determine device architecture
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self.device_architecture = get_device_architecture()
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if self.device_architecture not in ["hailo8", "hailo8l"]:
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raise RuntimeError(f"Unsupported device architecture: {self.device_architecture}")
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logger.info(f"Device architecture detected: {self.device_architecture}")
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# Ensure the model is available
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self.cache_dir = "/config/model_cache/h8l_cache"
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self.expected_model_filename = f"{config.model.type}.hef"
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self.check_and_prepare_model()
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try:
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# Validate device type
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if self.h8l_device_type not in ["PCIe", "M.2"]:
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raise ValueError(f"Unsupported device type: {self.h8l_device_type}")
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# Initialize the Hailo device
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self.target = VDevice()
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# Load the HEF (Hailo's binary format for neural networks)
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self.hef = HEF(self.h8l_model_path)
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# Create configuration parameters from the HEF
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self.configure_params = ConfigureParams.create_from_hef(
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hef=self.hef, interface=HailoStreamInterface.PCIe
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)
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# Configure the device with the HEF
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self.network_groups = self.target.configure(self.hef, self.configure_params)
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self.network_group = self.network_groups[0]
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self.network_group_params = self.network_group.create_params()
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# Create input and output virtual stream parameters
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self.input_vstream_params = InputVStreamParams.make(
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self.network_group,
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format_type=self.hef.get_input_vstream_infos()[0].format.type,
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)
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self.output_vstream_params = OutputVStreamParams.make(
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self.network_group,
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format_type=FormatType.FLOAT32
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)
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# Get input and output stream information from the HEF
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self.input_vstream_info = self.hef.get_input_vstream_infos()
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self.output_vstream_info = self.hef.get_output_vstream_infos()
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logger.info("Hailo device initialized successfully")
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logger.debug(f"[__init__] Model Path: {self.h8l_model_path}")
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logger.debug(f"[__init__] Input Tensor Format: {self.h8l_tensor_format}")
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logger.debug(f"[__init__] Input Pixel Format: {self.h8l_pixel_format}")
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logger.debug(f"[__init__] Input VStream Info: {self.input_vstream_info[0]}")
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logger.debug(f"[__init__] Output VStream Info: {self.output_vstream_info[0]}")
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except HailoRTException as e:
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logger.error(f"HailoRTException during initialization: {e}")
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raise
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except Exception as e:
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logger.error(f"Failed to initialize Hailo device: {e}")
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raise
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def check_and_prepare_model(self):
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"""Download and prepare the model if necessary"""
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# Ensure cache directory exists
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if not os.path.exists(self.cache_dir):
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os.makedirs(self.cache_dir)
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model_filename = f"{self.h8l_model_type}.hef"
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model_file_path = os.path.join(self.cache_dir, model_filename)
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self.h8l_model_path = model_file_path
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# Check for the expected model file
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model_file_path = os.path.join(self.cache_dir, self.expected_model_filename)
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self.async_inference.config.model.path = model_file_path
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if not os.path.isfile(model_file_path):
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logger.info(
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f"A model file was not found at {model_file_path}, Downloading one from {self.model_url}."
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)
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urllib.request.urlretrieve(self.model_url, model_file_path)
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logger.info(f"A model file was downloaded to {model_file_path}.")
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if self.async_inference.config.model.path:
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logger.info(
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f"A model file was not found at {model_file_path}, Downloading one from the provided URL."
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)
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urllib.request.urlretrieve(self.async_inference.config.model.path, model_file_path)
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logger.info(f"A model file was downloaded to {model_file_path}.")
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else:
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raise RuntimeError("Model file path is missing and no URL is provided.")
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else:
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logger.info(
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f"A model file already exists at {model_file_path} not downloading one."
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)
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def detect_raw(self, tensor_input):
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logger.debug("[detect_raw] Entering function")
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logger.debug(
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f"[detect_raw] The `tensor_input` = {tensor_input} tensor_input shape = {tensor_input.shape}"
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)
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if tensor_input is None:
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raise ValueError("[detect_raw] The 'tensor_input' argument must be provided")
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# Ensure tensor_input is a numpy array
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if isinstance(tensor_input, list):
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tensor_input = np.array(tensor_input)
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logger.debug(
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f"[detect_raw] Converted tensor_input to numpy array: shape {tensor_input.shape}"
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)
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input_data = tensor_input
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logger.debug(
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f"[detect_raw] Input data for inference shape: {tensor_input.shape}, dtype: {tensor_input.dtype}"
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)
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"""
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Perform inference and return raw detection results.
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"""
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preprocessed_input = self.preprocess(tensor_input)
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self.async_inference.input_queue.put([preprocessed_input])
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try:
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with InferVStreams(
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self.network_group,
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self.input_vstream_params,
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self.output_vstream_params,
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) as infer_pipeline:
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input_dict = {}
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if isinstance(input_data, dict):
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input_dict = input_data
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logger.debug("[detect_raw] it a dictionary.")
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elif isinstance(input_data, (list, tuple)):
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for idx, layer_info in enumerate(self.input_vstream_info):
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input_dict[layer_info.name] = input_data[idx]
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logger.debug("[detect_raw] converted from list/tuple.")
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else:
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if len(input_data.shape) == 3:
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input_data = np.expand_dims(input_data, axis=0)
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logger.debug("[detect_raw] converted from an array.")
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input_dict[self.input_vstream_info[0].name] = input_data
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batch_data, raw_results = self.async_inference.output_queue.get(timeout=5)
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return self.postprocess(raw_results)
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except queue.Empty:
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logger.warning("Inference timed out")
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return np.zeros((20, 6), np.float32)
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logger.debug(
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f"[detect_raw] Input dictionary for inference keys: {input_dict.keys()}"
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)
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def preprocess(self, frame):
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input_shape = (self.async_inference.hef.get_input_vstream_infos()[0].shape)
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resized_frame = np.resize(frame, input_shape)
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return resized_frame / 255.0
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with self.network_group.activate(self.network_group_params):
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raw_output = infer_pipeline.infer(input_dict)
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logger.debug(f"[detect_raw] Raw inference output: {raw_output}")
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if self.output_vstream_info[0].name not in raw_output:
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logger.error(
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f"[detect_raw] Missing output stream {self.output_vstream_info[0].name} in inference results"
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)
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return np.zeros((self.max_detections, 6), np.float32)
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raw_output = raw_output[self.output_vstream_info[0].name][0]
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logger.debug(
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f"[detect_raw] Raw output for stream {self.output_vstream_info[0].name}: {raw_output}"
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)
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# Process the raw output based on model type
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detections = self.process_detections(raw_output)
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if len(detections) == 0:
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logger.debug(
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"[detect_raw] No detections found after processing. Setting default values."
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)
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return np.zeros((self.max_detections, 6), np.float32)
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else:
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return detections
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except HailoRTException as e:
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logger.error(f"[detect_raw] HailoRTException during inference: {e}")
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return np.zeros((self.max_detections, 6), np.float32)
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except Exception as e:
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logger.error(f"[detect_raw] Exception during inference: {e}")
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return np.zeros((self.max_detections, 6), np.float32)
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finally:
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logger.debug("[detect_raw] Exiting function")
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def process_detections(self, raw_detections, threshold=None):
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"""Process detections based on model type"""
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if threshold is None:
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threshold = self.score_threshold
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if self.h8l_model_type == "ssd_mobilenet_v1":
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return self._process_ssd_detections(raw_detections, threshold)
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elif self.h8l_model_type == "yolov8s":
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return self._process_yolo_detections(raw_detections, threshold, version=8)
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elif self.h8l_model_type == "yolov6n":
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return self._process_yolo_detections(raw_detections, threshold, version=6)
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def postprocess(self, raw_output):
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model_type = self.async_inference.config.model.type
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if model_type == "ssd_mobilenet_v1":
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return self._process_ssd(raw_output)
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elif model_type in ["yolov8s", "yolov8m", "yolov6n"]:
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return self._process_yolo(raw_output, version=model_type[-1])
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else:
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logger.error(f"Unsupported model type: {self.h8l_model_type}")
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return np.zeros((self.max_detections, 6), np.float32)
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logger.error(f"Unsupported model type: {model_type}")
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return []
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def _process_ssd_detections(self, raw_detections, threshold):
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"""Process SSD MobileNet detections"""
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boxes, scores, classes = [], [], []
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num_detections = 0
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try:
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for detection_set in raw_detections:
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if not isinstance(detection_set, np.ndarray) or detection_set.size == 0:
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continue
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for detection in detection_set:
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if detection.shape[0] == 0:
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continue
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ymin, xmin, ymax, xmax = detection[:4]
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score = np.clip(detection[4], 0, 1)
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if score < threshold:
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continue
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boxes.append([ymin, xmin, ymax, xmax])
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scores.append(score)
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classes.append(int(detection[5]))
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num_detections += 1
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return self._format_output(boxes, scores, classes)
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except Exception as e:
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logger.error(f"Error processing SSD detections: {e}")
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return np.zeros((self.max_detections, 6), np.float32)
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def _process_yolo_detections(self, raw_detections, threshold, version):
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"""Process YOLO detections (v6 and v8)"""
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boxes, scores, classes = [], [], []
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try:
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detections = raw_detections[0]
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for detection in detections:
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if version == 8:
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confidence = detection[4]
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if confidence < threshold:
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continue
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class_scores = detection[5:]
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else: # YOLOv6
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class_scores = detection[4:]
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confidence = np.max(class_scores)
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if confidence < threshold:
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continue
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def _process_ssd(self, raw_output):
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detections = []
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for detection in raw_output[1]:
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score = detection[4]
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if score >= self.async_inference.config.model.score_threshold:
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ymin, xmin, ymax, xmax = detection[:4]
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detections.append({
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"bounding_box": [xmin, ymin, xmax, ymax],
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"score": score,
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"class": int(detection[5])
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})
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return detections
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def _process_yolo(self, raw_output, version):
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detections = []
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for detection in raw_output[1]:
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confidence = detection[4] if version == "8" else np.max(detection[5:])
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if confidence >= self.async_inference.config.model.score_threshold:
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x, y, w, h = detection[:4]
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# Convert to corner format
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ymin = y - h/2
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xmin = x - w/2
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ymax = y + h/2
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xmax = x + w/2
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class_id = np.argmax(class_scores)
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boxes.append([ymin, xmin, ymax, xmax])
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scores.append(confidence)
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classes.append(class_id)
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ymin, xmin, ymax, xmax = y - h / 2, x - w / 2, y + h / 2, x + w / 2
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class_id = np.argmax(detection[5:])
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detections.append({
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"bounding_box": [xmin, ymin, xmax, ymax],
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"score": confidence,
|
||||
"class": class_id
|
||||
})
|
||||
return detections
|
||||
|
||||
return self._format_output(boxes, scores, classes)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing YOLO detections: {e}")
|
||||
return np.zeros((self.max_detections, 6), np.float32)
|
||||
|
||||
def _format_output(self, boxes, scores, classes):
|
||||
"""Format detections to standard output format"""
|
||||
if not boxes:
|
||||
return np.zeros((self.max_detections, 6), np.float32)
|
||||
|
||||
combined = np.hstack((
|
||||
np.array(classes)[:, np.newaxis],
|
||||
np.array(scores)[:, np.newaxis],
|
||||
np.array(boxes)
|
||||
))
|
||||
|
||||
if combined.shape[0] < self.max_detections:
|
||||
padding = np.zeros((self.max_detections - combined.shape[0], 6), dtype=np.float32)
|
||||
combined = np.vstack((combined, padding))
|
||||
else:
|
||||
combined = combined[:self.max_detections]
|
||||
|
||||
return combined
|
||||
def stop(self):
|
||||
self.async_inference.stop()
|
||||
self.worker_thread.join()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user