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| """ | |
| This module contains component intended to use in combination with `InferencePipeline` to ensure | |
| observability. Please consider them internal details of implementation. | |
| """ | |
| from abc import ABC, abstractmethod | |
| from collections import deque | |
| from datetime import datetime | |
| from typing import Any, Deque, Iterable, List, Optional, TypeVar | |
| import supervision as sv | |
| from inference.core.interfaces.camera.entities import StatusUpdate, UpdateSeverity | |
| from inference.core.interfaces.camera.video_source import VideoSource | |
| from inference.core.interfaces.stream.entities import ( | |
| LatencyMonitorReport, | |
| ModelActivityEvent, | |
| PipelineStateReport, | |
| ) | |
| T = TypeVar("T") | |
| MAX_LATENCY_CONTEXT = 64 | |
| MAX_UPDATES_CONTEXT = 512 | |
| class PipelineWatchDog(ABC): | |
| def __init__(self): | |
| pass | |
| def register_video_source(self, video_source: VideoSource) -> None: | |
| pass | |
| def on_status_update(self, status_update: StatusUpdate) -> None: | |
| pass | |
| def on_model_preprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_inference_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_postprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_prediction_ready( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def get_report(self) -> Optional[PipelineStateReport]: | |
| pass | |
| class NullPipelineWatchdog(PipelineWatchDog): | |
| def register_video_source(self, video_source: VideoSource) -> None: | |
| pass | |
| def on_status_update(self, status_update: StatusUpdate) -> None: | |
| pass | |
| def on_model_preprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_inference_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_postprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def on_model_prediction_ready( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| pass | |
| def get_report(self) -> Optional[PipelineStateReport]: | |
| return None | |
| class LatencyMonitor: | |
| def __init__(self): | |
| self._preprocessing_start_event: Optional[ModelActivityEvent] = None | |
| self._inference_start_event: Optional[ModelActivityEvent] = None | |
| self._postprocessing_start_event: Optional[ModelActivityEvent] = None | |
| self._prediction_ready_event: Optional[ModelActivityEvent] = None | |
| self._reports: Deque[LatencyMonitorReport] = deque(maxlen=MAX_LATENCY_CONTEXT) | |
| def register_preprocessing_start( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._preprocessing_start_event = ModelActivityEvent( | |
| event_timestamp=datetime.now(), | |
| frame_id=frame_id, | |
| frame_decoding_timestamp=frame_timestamp, | |
| ) | |
| def register_inference_start( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._inference_start_event = ModelActivityEvent( | |
| event_timestamp=datetime.now(), | |
| frame_id=frame_id, | |
| frame_decoding_timestamp=frame_timestamp, | |
| ) | |
| def register_postprocessing_start( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._postprocessing_start_event = ModelActivityEvent( | |
| event_timestamp=datetime.now(), | |
| frame_id=frame_id, | |
| frame_decoding_timestamp=frame_timestamp, | |
| ) | |
| def register_prediction_ready( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._prediction_ready_event = ModelActivityEvent( | |
| event_timestamp=datetime.now(), | |
| frame_id=frame_id, | |
| frame_decoding_timestamp=frame_timestamp, | |
| ) | |
| self._generate_report() | |
| def summarise_reports(self) -> LatencyMonitorReport: | |
| avg_frame_decoding_latency = average_property_values( | |
| examined_objects=self._reports, property_name="frame_decoding_latency" | |
| ) | |
| avg_pre_processing_latency = average_property_values( | |
| examined_objects=self._reports, property_name="pre_processing_latency" | |
| ) | |
| avg_inference_latency = average_property_values( | |
| examined_objects=self._reports, property_name="inference_latency" | |
| ) | |
| avg_pos_processing_latency = average_property_values( | |
| examined_objects=self._reports, property_name="post_processing_latency" | |
| ) | |
| avg_model_latency = average_property_values( | |
| examined_objects=self._reports, property_name="model_latency" | |
| ) | |
| avg_e2e_latency = average_property_values( | |
| examined_objects=self._reports, property_name="e2e_latency" | |
| ) | |
| return LatencyMonitorReport( | |
| frame_decoding_latency=avg_frame_decoding_latency, | |
| pre_processing_latency=avg_pre_processing_latency, | |
| inference_latency=avg_inference_latency, | |
| post_processing_latency=avg_pos_processing_latency, | |
| model_latency=avg_model_latency, | |
| e2e_latency=avg_e2e_latency, | |
| ) | |
| def _generate_report(self) -> None: | |
| frame_decoding_latency = None | |
| if self._preprocessing_start_event is not None: | |
| frame_decoding_latency = ( | |
| self._preprocessing_start_event.event_timestamp | |
| - self._preprocessing_start_event.frame_decoding_timestamp | |
| ).total_seconds() | |
| event_pairs = [ | |
| (self._preprocessing_start_event, self._inference_start_event), | |
| (self._inference_start_event, self._postprocessing_start_event), | |
| (self._postprocessing_start_event, self._prediction_ready_event), | |
| (self._preprocessing_start_event, self._prediction_ready_event), | |
| ] | |
| event_pairs_results = [] | |
| for earlier_event, later_event in event_pairs: | |
| latency = compute_events_latency( | |
| earlier_event=earlier_event, | |
| later_event=later_event, | |
| ) | |
| event_pairs_results.append(latency) | |
| ( | |
| pre_processing_latency, | |
| inference_latency, | |
| post_processing_latency, | |
| model_latency, | |
| ) = event_pairs_results | |
| e2e_latency = None | |
| if self._prediction_ready_event is not None: | |
| e2e_latency = ( | |
| self._prediction_ready_event.event_timestamp | |
| - self._prediction_ready_event.frame_decoding_timestamp | |
| ).total_seconds() | |
| self._reports.append( | |
| LatencyMonitorReport( | |
| frame_decoding_latency=frame_decoding_latency, | |
| pre_processing_latency=pre_processing_latency, | |
| inference_latency=inference_latency, | |
| post_processing_latency=post_processing_latency, | |
| model_latency=model_latency, | |
| e2e_latency=e2e_latency, | |
| ) | |
| ) | |
| def average_property_values( | |
| examined_objects: Iterable, property_name: str | |
| ) -> Optional[float]: | |
| values = get_not_empty_properties( | |
| examined_objects=examined_objects, property_name=property_name | |
| ) | |
| return safe_average(values=values) | |
| def get_not_empty_properties( | |
| examined_objects: Iterable, property_name: str | |
| ) -> List[Any]: | |
| results = [ | |
| getattr(examined_object, property_name, None) | |
| for examined_object in examined_objects | |
| ] | |
| return [e for e in results if e is not None] | |
| def safe_average(values: List[float]) -> Optional[float]: | |
| if len(values) == 0: | |
| return None | |
| return sum(values) / len(values) | |
| def compute_events_latency( | |
| earlier_event: Optional[ModelActivityEvent], | |
| later_event: Optional[ModelActivityEvent], | |
| ) -> Optional[float]: | |
| if not are_events_compatible(events=[earlier_event, later_event]): | |
| return None | |
| return (later_event.event_timestamp - earlier_event.event_timestamp).total_seconds() | |
| def are_events_compatible(events: List[Optional[ModelActivityEvent]]) -> bool: | |
| if any(e is None for e in events): | |
| return False | |
| if len(events) == 0: | |
| return False | |
| frame_ids = [e.frame_id for e in events] | |
| return all(e == frame_ids[0] for e in frame_ids) | |
| class BasePipelineWatchDog(PipelineWatchDog): | |
| """ | |
| Implementation to be used from single inference thread, as it keeps | |
| state assumed to represent status of consecutive stage of prediction process | |
| in latency monitor. | |
| """ | |
| def __init__(self): | |
| super().__init__() | |
| self._video_source: Optional[VideoSource] = None | |
| self._inference_throughput_monitor = sv.FPSMonitor() | |
| self._latency_monitor = LatencyMonitor() | |
| self._stream_updates = deque(maxlen=MAX_UPDATES_CONTEXT) | |
| def register_video_source(self, video_source: VideoSource) -> None: | |
| self._video_source = video_source | |
| def on_status_update(self, status_update: StatusUpdate) -> None: | |
| if status_update.severity.value <= UpdateSeverity.DEBUG.value: | |
| return None | |
| self._stream_updates.append(status_update) | |
| def on_model_preprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._latency_monitor.register_preprocessing_start( | |
| frame_timestamp=frame_timestamp, frame_id=frame_id | |
| ) | |
| def on_model_inference_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._latency_monitor.register_inference_start( | |
| frame_timestamp=frame_timestamp, frame_id=frame_id | |
| ) | |
| def on_model_postprocessing_started( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._latency_monitor.register_postprocessing_start( | |
| frame_timestamp=frame_timestamp, frame_id=frame_id | |
| ) | |
| def on_model_prediction_ready( | |
| self, frame_timestamp: datetime, frame_id: int | |
| ) -> None: | |
| self._latency_monitor.register_prediction_ready( | |
| frame_timestamp=frame_timestamp, frame_id=frame_id | |
| ) | |
| self._inference_throughput_monitor.tick() | |
| def get_report(self) -> PipelineStateReport: | |
| source_metadata = None | |
| if self._video_source is not None: | |
| source_metadata = self._video_source.describe_source() | |
| return PipelineStateReport( | |
| video_source_status_updates=list(self._stream_updates), | |
| latency_report=self._latency_monitor.summarise_reports(), | |
| inference_throughput=self._inference_throughput_monitor(), | |
| source_metadata=source_metadata, | |
| ) | |