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472 lines
No EOL
15 KiB
Python
472 lines
No EOL
15 KiB
Python
"""
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YOLO Model Inference Wrapper - Handles model loading and inference optimization
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"""
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import logging
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import torch
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import numpy as np
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from pathlib import Path
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from typing import Dict, List, Optional, Any, Tuple, Union
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from threading import Lock
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from dataclasses import dataclass
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import cv2
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logger = logging.getLogger(__name__)
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@dataclass
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class Detection:
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"""Represents a single detection result"""
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bbox: List[float] # [x1, y1, x2, y2]
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confidence: float
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class_id: int
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class_name: str
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track_id: Optional[int] = None
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@dataclass
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class InferenceResult:
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"""Result from model inference"""
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detections: List[Detection]
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image_shape: Tuple[int, int] # (height, width)
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inference_time: float
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model_id: str
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class YOLOWrapper:
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"""Wrapper for YOLO models with per-instance isolation (no shared cache)"""
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def __init__(self, model_path: Path, model_id: str, device: Optional[str] = None):
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"""
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Initialize YOLO wrapper
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Args:
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model_path: Path to the .pt model file
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model_id: Unique identifier for the model
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device: Device to run inference on ('cuda', 'cpu', or None for auto)
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"""
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self.model_path = model_path
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self.model_id = model_id
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# Auto-detect device if not specified
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if device is None:
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self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
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else:
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self.device = device
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self.model = None
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self._class_names = []
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self._load_model()
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logger.info(f"Initialized YOLO wrapper for {model_id} on {self.device}")
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def _load_model(self) -> None:
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"""Load the YOLO model in isolation (no shared cache)"""
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try:
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from ultralytics import YOLO
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logger.debug(f"Loading YOLO model {self.model_id} from {self.model_path} (ISOLATED)")
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# Load model directly without any caching
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self.model = YOLO(str(self.model_path))
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# Determine if this is a classification model based on filename or model structure
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# Classification models typically have 'cls' in filename
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is_classification = 'cls' in str(self.model_path).lower()
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# For classification models, create a separate instance with task parameter
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if is_classification:
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try:
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# Reload with classification task (like ML engineer's approach)
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self.model = YOLO(str(self.model_path), task="classify")
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logger.info(f"Loaded classification model {self.model_id} with task='classify' (ISOLATED)")
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except Exception as e:
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logger.warning(f"Failed to load with task='classify', using default: {e}")
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# Fall back to regular loading
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self.model = YOLO(str(self.model_path))
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logger.info(f"Loaded model {self.model_id} with default task (ISOLATED)")
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else:
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logger.info(f"Loaded detection model {self.model_id} (ISOLATED)")
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# Move model to device
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if self.device == 'cuda' and torch.cuda.is_available():
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self.model.to('cuda')
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logger.info(f"Model {self.model_id} moved to GPU (ISOLATED)")
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self._extract_class_names()
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logger.debug(f"Successfully loaded model {self.model_id} in isolation - no shared cache!")
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except ImportError:
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logger.error("Ultralytics YOLO not installed. Install with: pip install ultralytics")
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raise
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except Exception as e:
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logger.error(f"Failed to load YOLO model {self.model_id}: {str(e)}", exc_info=True)
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raise
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def _extract_class_names(self) -> None:
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"""Extract class names from the model"""
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try:
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if hasattr(self.model, 'names'):
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self._class_names = self.model.names
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elif hasattr(self.model, 'model') and hasattr(self.model.model, 'names'):
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self._class_names = self.model.model.names
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else:
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logger.warning(f"Could not extract class names from model {self.model_id}")
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self._class_names = {}
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except Exception as e:
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logger.error(f"Failed to extract class names: {str(e)}")
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self._class_names = {}
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def infer(
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self,
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image: np.ndarray,
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confidence_threshold: float = 0.5,
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trigger_classes: Optional[List[str]] = None,
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iou_threshold: float = 0.45
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) -> InferenceResult:
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"""
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Run inference on an image
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Args:
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image: Input image as numpy array (BGR format)
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confidence_threshold: Minimum confidence for detections
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trigger_classes: List of class names to filter (None = all classes)
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iou_threshold: IoU threshold for NMS
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Returns:
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InferenceResult containing detections
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"""
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if self.model is None:
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raise RuntimeError(f"Model {self.model_id} not loaded")
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try:
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import time
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start_time = time.time()
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# Run inference using direct model call (like ML engineer's approach)
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results = self.model(
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image,
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conf=confidence_threshold,
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iou=iou_threshold,
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verbose=False
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)
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inference_time = time.time() - start_time
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# Parse results
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detections = self._parse_results(results[0], trigger_classes)
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return InferenceResult(
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detections=detections,
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image_shape=(image.shape[0], image.shape[1]),
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inference_time=inference_time,
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model_id=self.model_id
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)
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except Exception as e:
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logger.error(f"Inference failed for model {self.model_id}: {str(e)}", exc_info=True)
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raise
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def _parse_results(
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self,
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result: Any,
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trigger_classes: Optional[List[str]] = None
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) -> List[Detection]:
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"""
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Parse YOLO results into Detection objects
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Args:
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result: YOLO result object
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trigger_classes: Optional list of class names to filter
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Returns:
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List of Detection objects
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"""
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detections = []
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try:
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if result.boxes is None:
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return detections
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boxes = result.boxes
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for i in range(len(boxes)):
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# Get box coordinates
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box = boxes.xyxy[i].cpu().numpy()
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x1, y1, x2, y2 = box
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# Get confidence and class
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conf = float(boxes.conf[i])
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cls_id = int(boxes.cls[i])
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# Get class name
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class_name = self._class_names.get(cls_id, f"class_{cls_id}")
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# Filter by trigger classes if specified
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if trigger_classes and class_name not in trigger_classes:
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continue
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# Get track ID if available
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track_id = None
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if hasattr(boxes, 'id') and boxes.id is not None:
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track_id = int(boxes.id[i])
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detection = Detection(
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bbox=[float(x1), float(y1), float(x2), float(y2)],
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confidence=conf,
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class_id=cls_id,
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class_name=class_name,
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track_id=track_id
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)
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detections.append(detection)
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except Exception as e:
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logger.error(f"Failed to parse results: {str(e)}", exc_info=True)
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return detections
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def track(
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self,
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image: np.ndarray,
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confidence_threshold: float = 0.5,
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trigger_classes: Optional[List[str]] = None,
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persist: bool = True
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) -> InferenceResult:
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"""
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Run tracking on an image
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Args:
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image: Input image as numpy array (BGR format)
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confidence_threshold: Minimum confidence for detections
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trigger_classes: List of class names to filter
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persist: Whether to persist tracks across frames
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Returns:
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InferenceResult containing detections with track IDs
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"""
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if self.model is None:
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raise RuntimeError(f"Model {self.model_id} not loaded")
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try:
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import time
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start_time = time.time()
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# Run tracking
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results = self.model.track(
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image,
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conf=confidence_threshold,
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persist=persist,
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verbose=False
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)
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inference_time = time.time() - start_time
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# Parse results
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detections = self._parse_results(results[0], trigger_classes)
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return InferenceResult(
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detections=detections,
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image_shape=(image.shape[0], image.shape[1]),
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inference_time=inference_time,
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model_id=self.model_id
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)
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except Exception as e:
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logger.error(f"Tracking failed for model {self.model_id}: {str(e)}", exc_info=True)
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raise
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def predict_classification(
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self,
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image: np.ndarray,
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top_k: int = 1
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) -> Dict[str, float]:
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"""
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Run classification on an image
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Args:
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image: Input image as numpy array (BGR format)
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top_k: Number of top predictions to return
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Returns:
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Dictionary of class_name -> confidence scores
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"""
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if self.model is None:
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raise RuntimeError(f"Model {self.model_id} not loaded")
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try:
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# Run inference using predict method for classification (like ML engineer's approach)
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results = self.model.predict(source=image, verbose=False)
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# For classification models, extract probabilities
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if results and len(results) > 0 and hasattr(results[0], 'probs') and results[0].probs is not None:
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probs = results[0].probs
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top_indices = probs.top5[:top_k]
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top_conf = probs.top5conf[:top_k].cpu().numpy()
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predictions = {}
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for idx, conf in zip(top_indices, top_conf):
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class_name = self._class_names.get(int(idx), f"class_{idx}")
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predictions[class_name] = float(conf)
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return predictions
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else:
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logger.warning(f"Model {self.model_id} does not support classification or no probs found")
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return {}
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except Exception as e:
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logger.error(f"Classification failed for model {self.model_id}: {str(e)}", exc_info=True)
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raise
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def crop_detection(
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self,
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image: np.ndarray,
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detection: Detection,
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padding: int = 0
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) -> np.ndarray:
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"""
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Crop image to detection bounding box
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Args:
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image: Original image
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detection: Detection to crop
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padding: Additional padding around the box
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Returns:
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Cropped image region
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"""
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h, w = image.shape[:2]
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x1, y1, x2, y2 = detection.bbox
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# Add padding and clip to image boundaries
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x1 = max(0, int(x1) - padding)
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y1 = max(0, int(y1) - padding)
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x2 = min(w, int(x2) + padding)
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y2 = min(h, int(y2) + padding)
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return image[y1:y2, x1:x2]
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def get_class_names(self) -> Dict[int, str]:
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"""Get the class names dictionary"""
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return self._class_names.copy()
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def get_num_classes(self) -> int:
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"""Get the number of classes the model can detect"""
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return len(self._class_names)
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def is_classification_model(self) -> bool:
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"""Check if this is a classification model"""
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return 'cls' in str(self.model_path).lower() or 'classify' in str(self.model_path).lower()
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def clear_cache(self) -> None:
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"""Clear model resources (no cache in isolated mode)"""
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if self.model:
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# Clear any model resources if needed
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logger.info(f"Cleared resources for model {self.model_id} (no shared cache)")
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@classmethod
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def clear_all_cache(cls) -> None:
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"""No-op in isolated mode (no shared cache to clear)"""
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logger.info("No shared cache to clear in isolated mode")
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def warmup(self, image_size: Tuple[int, int] = (640, 640)) -> None:
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"""
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Warmup the model with a dummy inference
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Args:
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image_size: Size of dummy image (height, width)
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"""
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try:
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dummy_image = np.zeros((image_size[0], image_size[1], 3), dtype=np.uint8)
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self.infer(dummy_image, confidence_threshold=0.5)
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logger.info(f"Model {self.model_id} warmed up")
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except Exception as e:
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logger.warning(f"Failed to warmup model {self.model_id}: {str(e)}")
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class ModelInferenceManager:
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"""Manages multiple YOLO models for a pipeline"""
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def __init__(self, model_dir: Path):
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"""
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Initialize the inference manager
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Args:
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model_dir: Directory containing model files
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"""
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self.model_dir = model_dir
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self.models: Dict[str, YOLOWrapper] = {}
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self._lock = Lock()
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logger.info(f"Initialized ModelInferenceManager with model directory: {model_dir}")
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def load_model(
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self,
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model_id: str,
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model_file: str,
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device: Optional[str] = None
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) -> YOLOWrapper:
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"""
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Load a model for inference
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Args:
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model_id: Unique identifier for the model
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model_file: Filename of the model
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device: Device to run on
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Returns:
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YOLOWrapper instance
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"""
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with self._lock:
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# Check if already loaded for this specific manager instance
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if model_id in self.models:
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logger.debug(f"Model {model_id} already loaded in this manager instance")
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return self.models[model_id]
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# Load the model (each instance loads independently)
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model_path = self.model_dir / model_file
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if not model_path.exists():
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raise FileNotFoundError(f"Model file not found: {model_path}")
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logger.info(f"Loading model {model_id} in isolation for this manager instance")
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wrapper = YOLOWrapper(model_path, model_id, device)
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self.models[model_id] = wrapper
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return wrapper
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def get_model(self, model_id: str) -> Optional[YOLOWrapper]:
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"""
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Get a loaded model
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Args:
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model_id: Model identifier
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Returns:
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YOLOWrapper instance or None if not loaded
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"""
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return self.models.get(model_id)
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def unload_model(self, model_id: str) -> bool:
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"""
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Unload a model to free memory
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Args:
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model_id: Model identifier
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Returns:
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True if unloaded, False if not found
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"""
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with self._lock:
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if model_id in self.models:
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self.models[model_id].clear_cache()
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del self.models[model_id]
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logger.info(f"Unloaded model {model_id}")
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return True
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return False
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def unload_all(self) -> None:
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"""Unload all models"""
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with self._lock:
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for model_id in list(self.models.keys()):
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self.models[model_id].clear_cache()
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self.models.clear()
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logger.info("Unloaded all models") |