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Running
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Zero
File size: 11,131 Bytes
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import os
import signal
from dataclasses import asdict
from multiprocessing import Process, Queue
from types import FrameType
from typing import Callable, Optional, Tuple
from inference.core import logger
from inference.core.exceptions import (
MissingApiKeyError,
RoboflowAPINotAuthorizedError,
RoboflowAPINotNotFoundError,
)
from inference.core.interfaces.camera.entities import VideoFrame
from inference.core.interfaces.camera.exceptions import StreamOperationNotAllowedError
from inference.core.interfaces.camera.video_source import (
BufferConsumptionStrategy,
BufferFillingStrategy,
)
from inference.core.interfaces.stream.entities import ObjectDetectionPrediction
from inference.core.interfaces.stream.inference_pipeline import InferencePipeline
from inference.core.interfaces.stream.sinks import UDPSink
from inference.core.interfaces.stream.watchdog import (
BasePipelineWatchDog,
PipelineWatchDog,
)
from inference.enterprise.stream_management.manager.entities import (
STATUS_KEY,
TYPE_KEY,
CommandType,
ErrorType,
OperationStatus,
)
from inference.enterprise.stream_management.manager.serialisation import describe_error
def ignore_signal(signal_number: int, frame: FrameType) -> None:
pid = os.getpid()
logger.info(
f"Ignoring signal {signal_number} in InferencePipelineManager in process:{pid}"
)
class InferencePipelineManager(Process):
@classmethod
def init(
cls, command_queue: Queue, responses_queue: Queue
) -> "InferencePipelineManager":
return cls(command_queue=command_queue, responses_queue=responses_queue)
def __init__(self, command_queue: Queue, responses_queue: Queue):
super().__init__()
self._command_queue = command_queue
self._responses_queue = responses_queue
self._inference_pipeline: Optional[InferencePipeline] = None
self._watchdog: Optional[PipelineWatchDog] = None
self._stop = False
def run(self) -> None:
signal.signal(signal.SIGINT, ignore_signal)
signal.signal(signal.SIGTERM, self._handle_termination_signal)
while not self._stop:
command: Optional[Tuple[str, dict]] = self._command_queue.get()
if command is None:
break
request_id, payload = command
self._handle_command(request_id=request_id, payload=payload)
def _handle_command(self, request_id: str, payload: dict) -> None:
try:
logger.info(f"Processing request={request_id}...")
command_type = payload[TYPE_KEY]
if command_type is CommandType.INIT:
return self._initialise_pipeline(request_id=request_id, payload=payload)
if command_type is CommandType.TERMINATE:
return self._terminate_pipeline(request_id=request_id)
if command_type is CommandType.MUTE:
return self._mute_pipeline(request_id=request_id)
if command_type is CommandType.RESUME:
return self._resume_pipeline(request_id=request_id)
if command_type is CommandType.STATUS:
return self._get_pipeline_status(request_id=request_id)
raise NotImplementedError(
f"Command type `{command_type}` cannot be handled"
)
except (KeyError, NotImplementedError) as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.INVALID_PAYLOAD
)
except Exception as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.INTERNAL_ERROR
)
def _initialise_pipeline(self, request_id: str, payload: dict) -> None:
try:
watchdog = BasePipelineWatchDog()
sink = assembly_pipeline_sink(sink_config=payload["sink_configuration"])
source_buffer_filling_strategy, source_buffer_consumption_strategy = (
None,
None,
)
if "source_buffer_filling_strategy" in payload:
source_buffer_filling_strategy = BufferFillingStrategy(
payload["source_buffer_filling_strategy"].upper()
)
if "source_buffer_consumption_strategy" in payload:
source_buffer_consumption_strategy = BufferConsumptionStrategy(
payload["source_buffer_consumption_strategy"].upper()
)
model_configuration = payload["model_configuration"]
if model_configuration["type"] != "object-detection":
raise NotImplementedError("Only object-detection models are supported")
self._inference_pipeline = InferencePipeline.init(
model_id=payload["model_id"],
video_reference=payload["video_reference"],
on_prediction=sink,
api_key=payload.get("api_key"),
max_fps=payload.get("max_fps"),
watchdog=watchdog,
source_buffer_filling_strategy=source_buffer_filling_strategy,
source_buffer_consumption_strategy=source_buffer_consumption_strategy,
class_agnostic_nms=model_configuration.get("class_agnostic_nms"),
confidence=model_configuration.get("confidence"),
iou_threshold=model_configuration.get("iou_threshold"),
max_candidates=model_configuration.get("max_candidates"),
max_detections=model_configuration.get("max_detections"),
active_learning_enabled=payload.get("active_learning_enabled"),
)
self._watchdog = watchdog
self._inference_pipeline.start(use_main_thread=False)
self._responses_queue.put(
(request_id, {STATUS_KEY: OperationStatus.SUCCESS})
)
logger.info(f"Pipeline initialised. request_id={request_id}...")
except (MissingApiKeyError, KeyError, NotImplementedError) as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.INVALID_PAYLOAD
)
except RoboflowAPINotAuthorizedError as error:
self._handle_error(
request_id=request_id,
error=error,
error_type=ErrorType.AUTHORISATION_ERROR,
)
except RoboflowAPINotNotFoundError as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.NOT_FOUND
)
def _terminate_pipeline(self, request_id: str) -> None:
if self._inference_pipeline is None:
self._responses_queue.put(
(request_id, {STATUS_KEY: OperationStatus.SUCCESS})
)
self._stop = True
return None
try:
self._execute_termination()
logger.info(f"Pipeline terminated. request_id={request_id}...")
self._responses_queue.put(
(request_id, {STATUS_KEY: OperationStatus.SUCCESS})
)
except StreamOperationNotAllowedError as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.OPERATION_ERROR
)
def _handle_termination_signal(self, signal_number: int, frame: FrameType) -> None:
try:
pid = os.getpid()
logger.info(f"Terminating pipeline in process:{pid}...")
if self._inference_pipeline is not None:
self._execute_termination()
self._command_queue.put(None)
logger.info(f"Termination successful in process:{pid}...")
except Exception as error:
logger.warning(f"Could not terminate pipeline gracefully. Error: {error}")
def _execute_termination(self) -> None:
self._inference_pipeline.terminate()
self._inference_pipeline.join()
self._stop = True
def _mute_pipeline(self, request_id: str) -> None:
if self._inference_pipeline is None:
return self._handle_error(
request_id=request_id, error_type=ErrorType.OPERATION_ERROR
)
try:
self._inference_pipeline.mute_stream()
logger.info(f"Pipeline muted. request_id={request_id}...")
self._responses_queue.put(
(request_id, {STATUS_KEY: OperationStatus.SUCCESS})
)
except StreamOperationNotAllowedError as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.OPERATION_ERROR
)
def _resume_pipeline(self, request_id: str) -> None:
if self._inference_pipeline is None:
return self._handle_error(
request_id=request_id, error_type=ErrorType.OPERATION_ERROR
)
try:
self._inference_pipeline.resume_stream()
logger.info(f"Pipeline resumed. request_id={request_id}...")
self._responses_queue.put(
(request_id, {STATUS_KEY: OperationStatus.SUCCESS})
)
except StreamOperationNotAllowedError as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.OPERATION_ERROR
)
def _get_pipeline_status(self, request_id: str) -> None:
if self._watchdog is None:
return self._handle_error(
request_id=request_id, error_type=ErrorType.OPERATION_ERROR
)
try:
report = self._watchdog.get_report()
if report is None:
return self._handle_error(
request_id=request_id, error_type=ErrorType.OPERATION_ERROR
)
response_payload = {
STATUS_KEY: OperationStatus.SUCCESS,
"report": asdict(report),
}
self._responses_queue.put((request_id, response_payload))
logger.info(f"Pipeline status returned. request_id={request_id}...")
except StreamOperationNotAllowedError as error:
self._handle_error(
request_id=request_id, error=error, error_type=ErrorType.OPERATION_ERROR
)
def _handle_error(
self,
request_id: str,
error: Optional[Exception] = None,
error_type: ErrorType = ErrorType.INTERNAL_ERROR,
):
logger.error(
f"Could not handle Command. request_id={request_id}, error={error}, error_type={error_type}"
)
response_payload = describe_error(error, error_type=error_type)
self._responses_queue.put((request_id, response_payload))
def assembly_pipeline_sink(
sink_config: dict,
) -> Callable[[ObjectDetectionPrediction, VideoFrame], None]:
if sink_config["type"] != "udp_sink":
raise NotImplementedError("Only `udp_socket` sink type is supported")
sink = UDPSink.init(ip_address=sink_config["host"], port=sink_config["port"])
return sink.send_predictions
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