feat(project): move edge_inference_service contents to root and update paths
- Moved all project files and directories (config, core, models, etc.) from edge_inference_service/ to the repository root ai_edge/ - Updated model path in config/settings.py to reflect new structure - Revised usage paths in __init__.py documentation
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373
utils/logger.py
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373
utils/logger.py
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"""
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分级日志系统
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提供多级别日志支持，包含文件输出、控制台输出、性能指标记录
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"""
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import os
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import sys
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import logging
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import logging.handlers
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import time
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import threading
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import json
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from datetime import datetime
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from typing import Any, Dict, Optional, Union
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from pathlib import Path
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from config.settings import get_settings
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class PerformanceLogger:
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"""性能指标记录器"""
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def __init__(self):
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self._metrics: Dict[str, list] = {}
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self._lock = threading.Lock()
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def record(self, metric_name: str, value: float, tags: Optional[Dict[str, str]] = None):
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"""记录性能指标"""
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with self._lock:
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key = metric_name
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if metric_name not in self._metrics:
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self._metrics[metric_name] = []
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self._metrics[metric_name].append({
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"value": value,
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"timestamp": time.time(),
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"datetime": datetime.now().isoformat(),
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"tags": tags or {}
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})
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def get_metrics(self, metric_name: Optional[str] = None) -> Dict[str, Any]:
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"""获取性能指标"""
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with self._lock:
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if metric_name:
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return self._metrics.get(metric_name, [])
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return dict(self._metrics)
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def get_last_value(self, metric_name: str) -> Optional[float]:
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"""获取最新指标值"""
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with self._lock:
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metrics = self._metrics.get(metric_name, [])
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if metrics:
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return metrics[-1].get("value")
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return None
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def get_statistics(self, metric_name: str) -> Dict[str, float]:
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"""获取指标统计信息"""
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with self._lock:
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values = [m["value"] for m in self._metrics.get(metric_name, [])]
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if not values:
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return {}
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return {
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"count": len(values),
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"min": min(values),
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"max": max(values),
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"avg": sum(values) / len(values),
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"sum": sum(values),
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}
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def clear(self):
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"""清空所有指标"""
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with self._lock:
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self._metrics.clear()
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class StructuredLogger:
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"""结构化日志记录器"""
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def __init__(self, name: str = "edge_inference"):
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self.name = name
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self._logger = None
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self._performance_logger = PerformanceLogger()
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self._log_dir = "./logs"
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self._init_logger()
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def _init_logger(self):
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"""初始化日志配置"""
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settings = get_settings()
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self._log_level = getattr(logging, settings.log_level.upper(), logging.INFO)
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self._log_dir = settings.log_dir
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self._max_size = settings.log_file_max_size
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self._backup_count = settings.log_file_backup_count
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os.makedirs(self._log_dir, exist_ok=True)
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self._logger = logging.getLogger(self.name)
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self._logger.setLevel(self._log_level)
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self._logger.handlers.clear()
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formatter = logging.Formatter(
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fmt='%(asctime)s | %(levelname)-8s | %(name)s | %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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console_handler = logging.StreamHandler(sys.stdout)
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console_handler.setLevel(self._log_level)
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console_handler.setFormatter(formatter)
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self._logger.addHandler(console_handler)
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self._add_file_handler(formatter)
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def _add_file_handler(self, formatter: logging.Formatter):
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"""添加文件处理器"""
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log_file = os.path.join(self._log_dir, f"{self.name}.log")
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try:
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file_handler = logging.handlers.RotatingFileHandler(
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log_file,
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maxBytes=self._max_size,
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backupCount=self._backup_count,
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encoding='utf-8'
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)
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file_handler.setLevel(self._log_level)
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file_handler.setFormatter(formatter)
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self._logger.addHandler(file_handler)
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error_file = os.path.join(self._log_dir, f"{self.name}_error.log")
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error_handler = logging.handlers.RotatingFileHandler(
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error_file,
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maxBytes=self._max_size,
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backupCount=self._backup_count,
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encoding='utf-8'
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)
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error_handler.setLevel(logging.ERROR)
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error_handler.setFormatter(formatter)
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self._logger.addHandler(error_handler)
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except Exception as e:
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sys.stderr.write(f"创建日志文件处理器失败: {e}\n")
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def _log(
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self,
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level: int,
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message: str,
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extra: Optional[Dict[str, Any]] = None,
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exc_info: bool = False
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):
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"""结构化日志记录"""
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log_data = {
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"timestamp": datetime.now().isoformat(),
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"logger": self.name,
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}
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if extra:
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log_data.update(extra)
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extra_fields = {"structured_data": json.dumps(log_data, ensure_ascii=False)}
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self._logger.log(level, message, extra=extra_fields, exc_info=exc_info)
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def debug(self, message: str, **kwargs):
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"""DEBUG级别日志"""
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self._log(logging.DEBUG, message, kwargs)
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def info(self, message: str, **kwargs):
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"""INFO级别日志"""
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self._log(logging.INFO, message, kwargs)
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def warning(self, message: str, **kwargs):
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"""WARNING级别日志"""
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self._log(logging.WARNING, message, kwargs)
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def error(self, message: str, exc_info: bool = True, **kwargs):
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"""ERROR级别日志"""
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self._log(logging.ERROR, message, kwargs, exc_info=exc_info)
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def critical(self, message: str, exc_info: bool = True, **kwargs):
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"""CRITICAL级别日志"""
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self._log(logging.CRITICAL, message, kwargs, exc_info=exc_info)
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def performance(self, metric_name: str, value: float,
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duration_ms: Optional[float] = None, **tags):
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"""记录性能指标"""
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self._performance_logger.record(metric_name, value, tags)
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perf_data = {
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"metric": metric_name,
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"value": value,
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"duration_ms": duration_ms,
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"tags": tags
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}
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self.info(f"性能指标: {metric_name} = {value}", **perf_data)
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def log_inference_latency(self, latency_ms: float, batch_size: int = 1):
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"""记录推理延迟"""
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self.performance(
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"inference_latency_ms",
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latency_ms,
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batch_size=batch_size,
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throughput_fps=1000.0 / latency_ms if latency_ms > 0 else 0
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)
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def log_frame_rate(self, fps: float, camera_id: str):
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"""记录帧率"""
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self.performance(
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"frame_rate_fps",
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fps,
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camera_id=camera_id
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)
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def log_resource_usage(
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self,
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cpu_percent: float,
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memory_mb: float,
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gpu_memory_mb: Optional[float] = None
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):
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"""记录资源使用情况"""
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self.performance(
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"cpu_percent",
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cpu_percent,
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memory_mb=memory_mb,
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gpu_memory_mb=gpu_memory_mb
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)
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def log_alert(self, alert_type: str, camera_id: str, roi_id: str,
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confidence: Optional[float] = None):
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"""记录告警事件"""
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self.info(
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f"告警触发: {alert_type}",
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alert_type=alert_type,
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camera_id=camera_id,
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roi_id=roi_id,
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confidence=confidence
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)
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def log_connection_event(self, event_type: str,
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connection_type: str,
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target: str,
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success: bool,
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error_msg: Optional[str] = None):
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"""记录连接事件"""
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self.info(
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f"连接事件: {event_type} - {connection_type} -> {target}",
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event_type=event_type,
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connection_type=connection_type,
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target=target,
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success=success,
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error_msg=error_msg
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)
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def get_performance_metrics(self) -> Dict[str, Any]:
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"""获取性能指标"""
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return {
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"metrics": self._performance_logger.get_metrics(),
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"statistics": {
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name: self._performance_logger.get_statistics(name)
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for name in self._performance_logger.get_metrics()
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}
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}
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def get_statistics(self, metric_name: str) -> Dict[str, float]:
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"""获取指定指标统计"""
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return self._performance_logger.get_statistics(metric_name)
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def export_metrics(self, output_path: str):
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"""导出性能指标到文件"""
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metrics = self.get_performance_metrics()
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try:
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(metrics, f, ensure_ascii=False, indent=2)
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self.info(f"性能指标已导出: {output_path}")
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except Exception as e:
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self.error(f"导出性能指标失败: {e}")
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def flush(self):
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"""刷新日志处理器"""
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for handler in self._logger.handlers:
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if hasattr(handler, 'flush'):
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handler.flush()
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def close(self):
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"""关闭日志系统"""
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self.flush()
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for handler in self._logger.handlers:
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handler.close()
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self._logger.handlers.clear()
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class ContextLogger:
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"""上下文日志记录器，自动附加上下文信息"""
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def __init__(self, base_logger: StructuredLogger, context: Dict[str, Any]):
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self._base_logger = base_logger
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self._context = context
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def _add_context(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
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"""添加上下文信息"""
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result = dict(self._context)
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result.update(kwargs)
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return result
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def debug(self, message: str, **kwargs):
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"""DEBUG级别日志"""
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self._base_logger.debug(message, **self._add_context(kwargs))
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def info(self, message: str, **kwargs):
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"""INFO级别日志"""
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self._base_logger.info(message, **self._add_context(kwargs))
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def warning(self, message: str, **kwargs):
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"""WARNING级别日志"""
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self._base_logger.warning(message, **self._add_context(kwargs))
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def error(self, message: str, **kwargs):
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"""ERROR级别日志"""
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self._base_logger.error(message, **self._add_context(kwargs))
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def critical(self, message: str, **kwargs):
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"""CRITICAL级别日志"""
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self._base_logger.critical(message, **self._add_context(kwargs))
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def with_context(self, **additional_context) -> 'ContextLogger':
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"""添加额外上下文"""
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new_context = dict(self._context)
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new_context.update(additional_context)
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return ContextLogger(self._base_logger, new_context)
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_structured_logger_instance = None
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def get_logger(name: str = "edge_inference") -> StructuredLogger:
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"""获取结构化日志记录器单例"""
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global _structured_logger_instance
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if _structured_logger_instance is None:
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_structured_logger_instance = StructuredLogger(name)
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return _structured_logger_instance
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def create_logger_with_context(base_logger: StructuredLogger,
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context: Dict[str, Any]) -> ContextLogger:
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"""创建带上下文的日志记录器"""
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return ContextLogger(base_logger, context)
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def log_performance_operation(operation_name: str, logger_instance: StructuredLogger):
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"""性能日志装饰器"""
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def decorator(func):
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def wrapper(*args, **kwargs):
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start_time = time.perf_counter()
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try:
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result = func(*args, **kwargs)
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duration_ms = (time.perf_counter() - start_time) * 1000
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logger_instance.log_inference_latency(
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duration_ms,
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batch_size=1
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)
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logger_instance.debug(
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f"操作完成: {operation_name}, 耗时: {duration_ms:.2f}ms"
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)
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return result
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except Exception as e:
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duration_ms = (time.perf_counter() - start_time) * 1000
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logger_instance.error(
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f"操作失败: {operation_name}, 耗时: {duration_ms:.2f}ms, 错误: {e}"
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)
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raise
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return wrapper
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return decorator
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