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from dataclasses import dataclass |
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from datetime import datetime |
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from typing import Dict, List, Optional, Union |
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from inference.core.env import ( |
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CLASS_AGNOSTIC_NMS_ENV, |
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DEFAULT_CLASS_AGNOSTIC_NMS, |
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DEFAULT_CONFIDENCE, |
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DEFAULT_IOU_THRESHOLD, |
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DEFAULT_MAX_CANDIDATES, |
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DEFAULT_MAX_DETECTIONS, |
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IOU_THRESHOLD_ENV, |
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MAX_CANDIDATES_ENV, |
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MAX_DETECTIONS_ENV, |
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) |
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from inference.core.interfaces.camera.entities import StatusUpdate |
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from inference.core.interfaces.camera.video_source import SourceMetadata |
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from inference.core.utils.environment import safe_env_to_type, str2bool |
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ObjectDetectionPrediction = dict |
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@dataclass(frozen=True) |
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class ModelConfig: |
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class_agnostic_nms: Optional[bool] |
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confidence: Optional[float] |
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iou_threshold: Optional[float] |
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max_candidates: Optional[int] |
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max_detections: Optional[int] |
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mask_decode_mode: Optional[str] |
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tradeoff_factor: Optional[float] |
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@classmethod |
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def init( |
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cls, |
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class_agnostic_nms: Optional[bool] = None, |
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confidence: Optional[float] = None, |
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iou_threshold: Optional[float] = None, |
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max_candidates: Optional[int] = None, |
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max_detections: Optional[int] = None, |
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mask_decode_mode: Optional[str] = None, |
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tradeoff_factor: Optional[float] = None, |
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) -> "ModelConfig": |
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if class_agnostic_nms is None: |
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class_agnostic_nms = safe_env_to_type( |
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variable_name=CLASS_AGNOSTIC_NMS_ENV, |
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default_value=DEFAULT_CLASS_AGNOSTIC_NMS, |
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type_constructor=str2bool, |
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) |
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if confidence is None: |
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confidence = safe_env_to_type( |
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variable_name=CLASS_AGNOSTIC_NMS_ENV, |
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default_value=DEFAULT_CONFIDENCE, |
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type_constructor=float, |
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) |
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if iou_threshold is None: |
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iou_threshold = safe_env_to_type( |
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variable_name=IOU_THRESHOLD_ENV, |
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default_value=DEFAULT_IOU_THRESHOLD, |
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type_constructor=float, |
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) |
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if max_candidates is None: |
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max_candidates = safe_env_to_type( |
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variable_name=MAX_CANDIDATES_ENV, |
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default_value=DEFAULT_MAX_CANDIDATES, |
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type_constructor=int, |
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) |
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if max_detections is None: |
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max_detections = safe_env_to_type( |
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variable_name=MAX_DETECTIONS_ENV, |
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default_value=DEFAULT_MAX_DETECTIONS, |
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type_constructor=int, |
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) |
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return ModelConfig( |
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class_agnostic_nms=class_agnostic_nms, |
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confidence=confidence, |
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iou_threshold=iou_threshold, |
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max_candidates=max_candidates, |
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max_detections=max_detections, |
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mask_decode_mode=mask_decode_mode, |
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tradeoff_factor=tradeoff_factor, |
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) |
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def to_postprocessing_params(self) -> Dict[str, Union[bool, float, int]]: |
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result = {} |
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for field in [ |
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"class_agnostic_nms", |
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"confidence", |
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"iou_threshold", |
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"max_candidates", |
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"max_detections", |
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"mask_decode_mode", |
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"tradeoff_factor", |
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]: |
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result[field] = getattr(self, field, None) |
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return {name: value for name, value in result.items() if value is not None} |
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@dataclass(frozen=True) |
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class ModelActivityEvent: |
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frame_decoding_timestamp: datetime |
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event_timestamp: datetime |
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frame_id: int |
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@dataclass(frozen=True) |
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class LatencyMonitorReport: |
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frame_decoding_latency: Optional[float] = None |
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pre_processing_latency: Optional[float] = None |
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inference_latency: Optional[float] = None |
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post_processing_latency: Optional[float] = None |
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model_latency: Optional[float] = None |
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e2e_latency: Optional[float] = None |
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@dataclass(frozen=True) |
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class PipelineStateReport: |
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video_source_status_updates: List[StatusUpdate] |
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latency_report: LatencyMonitorReport |
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inference_throughput: float |
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source_metadata: Optional[SourceMetadata] |
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