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https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
add num_threads fixes #322
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@ -200,6 +200,9 @@ detectors:
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type: edgetpu
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# Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
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device: usb
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# Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
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# This value is only used for CPU types
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num_threads: 3
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# Optional: model configuration
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model:
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@ -125,9 +125,9 @@ class FrigateApp():
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for name, detector in self.config.detectors.items():
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if detector.type == 'cpu':
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self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, tf_device='cpu')
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self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, 'cpu', detector.num_threads)
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if detector.type == 'edgetpu':
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self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, tf_device=detector.device)
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self.detectors[name] = EdgeTPUProcess(name, self.detection_queue, self.detection_out_events, model_shape, detector.device, detector.num_threads)
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def start_detected_frames_processor(self):
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self.detected_frames_processor = TrackedObjectProcessor(self.config, self.mqtt_client, self.config.mqtt.topic_prefix,
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@ -15,7 +15,8 @@ DETECTORS_SCHEMA = vol.Schema(
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{
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vol.Required(str): {
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vol.Required('type', default='edgetpu'): vol.In(['cpu', 'edgetpu']),
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vol.Optional('device', default='usb'): str
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vol.Optional('device', default='usb'): str,
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vol.Optional('num_threads', default=3): int
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}
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}
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)
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@ -264,6 +265,7 @@ class DetectorConfig():
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def __init__(self, config):
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self._type = config['type']
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self._device = config['device']
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self._num_threads = config['num_threads']
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@property
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def type(self):
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@ -273,10 +275,15 @@ class DetectorConfig():
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def device(self):
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return self._device
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@property
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def num_threads(self):
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return self._num_threads
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def to_dict(self):
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return {
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'type': self.type,
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'device': self.device
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'device': self.device,
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'num_threads': self.num_threads
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}
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class LoggerConfig():
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@ -43,7 +43,7 @@ class ObjectDetector(ABC):
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pass
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class LocalObjectDetector(ObjectDetector):
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def __init__(self, tf_device=None, labels=None):
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def __init__(self, tf_device=None, num_threads=3, labels=None):
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self.fps = EventsPerSecond()
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if labels is None:
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self.labels = {}
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@ -66,7 +66,7 @@ class LocalObjectDetector(ObjectDetector):
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if edge_tpu_delegate is None:
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self.interpreter = tflite.Interpreter(
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model_path='/cpu_model.tflite')
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model_path='/cpu_model.tflite', num_threads=num_threads)
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else:
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self.interpreter = tflite.Interpreter(
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model_path='/edgetpu_model.tflite',
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@ -106,7 +106,7 @@ class LocalObjectDetector(ObjectDetector):
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return detections
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def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, model_shape, tf_device):
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def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.Event], avg_speed, start, model_shape, tf_device, num_threads):
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threading.current_thread().name = f"detector:{name}"
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logger = logging.getLogger(f"detector.{name}")
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logger.info(f"Starting detection process: {os.getpid()}")
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@ -120,7 +120,7 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
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signal.signal(signal.SIGINT, receiveSignal)
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frame_manager = SharedMemoryFrameManager()
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object_detector = LocalObjectDetector(tf_device=tf_device)
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object_detector = LocalObjectDetector(tf_device=tf_device, num_threads=num_threads)
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outputs = {}
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for name in out_events.keys():
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@ -155,7 +155,7 @@ def run_detector(name: str, detection_queue: mp.Queue, out_events: Dict[str, mp.
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avg_speed.value = (avg_speed.value*9 + duration)/10
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class EdgeTPUProcess():
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def __init__(self, name, detection_queue, out_events, model_shape, tf_device=None):
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def __init__(self, name, detection_queue, out_events, model_shape, tf_device=None, num_threads=3):
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self.name = name
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self.out_events = out_events
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self.detection_queue = detection_queue
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@ -164,6 +164,7 @@ class EdgeTPUProcess():
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self.detect_process = None
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self.model_shape = model_shape
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self.tf_device = tf_device
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self.num_threads = num_threads
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self.start_or_restart()
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def stop(self):
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@ -179,7 +180,7 @@ class EdgeTPUProcess():
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self.detection_start.value = 0.0
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if (not self.detect_process is None) and self.detect_process.is_alive():
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self.stop()
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self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.model_shape, self.tf_device))
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self.detect_process = mp.Process(target=run_detector, name=f"detector:{self.name}", args=(self.name, self.detection_queue, self.out_events, self.avg_inference_speed, self.detection_start, self.model_shape, self.tf_device, self.num_threads))
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self.detect_process.daemon = True
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self.detect_process.start()
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