453 lines
16 KiB
Python
453 lines
16 KiB
Python
"""
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优化版压力测试 - 使用原生 TensorRT 以达到更高 GPU 利用率
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目标：GPU 利用率 70%+ 而不是 30%
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"""
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import os
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import gc
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import json
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import time
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import signal
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import threading
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from typing import Dict, List, Optional, Tuple
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from dataclasses import dataclass, asdict
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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from .utils import setup_logging, ensure_dir
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from .tensorrt_engine import TensorRTEngine, MultiStreamTensorRTEngine, TensorRTConfig, TRT_AVAILABLE
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logger = setup_logging()
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@dataclass
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class OptimizedStressResult:
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"""优化压力测试结果"""
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test_type: str
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resolution: int
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batch_size: int
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num_cameras: int
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num_streams: int
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target_fps: float
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frame_skip: int
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# 性能结果
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actual_fps: float
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per_camera_fps: float
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gpu_utilization: float
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memory_used_mb: float
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avg_latency_ms: float
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p95_latency_ms: float
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max_latency_ms: float
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min_latency_ms: float
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# TensorRT 特定指标
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avg_inference_time_ms: float
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total_inferences: int
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total_frames_processed: int
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stream_utilization: Dict[int, float]
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is_stable: bool
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error_msg: Optional[str] = None
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timestamp: str = ""
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def __post_init__(self):
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if not self.timestamp:
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self.timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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class OptimizedStressTestRunner:
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"""优化版压力测试运行器"""
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def __init__(self, model_path: str, output_dir: str = "./optimized_stress_results"):
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self.model_path = model_path
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(exist_ok=True)
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self.results: List[OptimizedStressResult] = []
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self._interrupted = False
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self._engine_cache: Dict[Tuple[int, int], str] = {}
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# 优化配置
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self.tensorrt_config = TensorRTConfig(
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max_batch_size=32, # 增大批次
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max_workspace_size=2 << 30, # 2GB 工作空间
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fp16_mode=True,
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gpu_fallback=True
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)
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# 测试参数
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self.test_duration = 20 # 每次测试秒数
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self.warmup_sec = 3
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self.cooldown_sec = 1
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# 结果文件
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self._results_file = self.output_dir / f"optimized_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
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signal.signal(signal.SIGINT, self._signal_handler)
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if not TRT_AVAILABLE:
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raise ImportError("需要安装 tensorrt 和 pycuda")
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def _signal_handler(self, signum, frame):
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logger.warning("收到中断信号，保存当前结果...")
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self._interrupted = True
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self._save_results()
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def _clear_gpu(self):
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"""强制清理 GPU 显存"""
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gc.collect()
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try:
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import torch
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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except:
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pass
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time.sleep(self.cooldown_sec)
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def _save_results(self):
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"""保存结果到文件"""
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data = [asdict(r) for r in self.results]
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with open(self._results_file, 'w', encoding='utf-8') as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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logger.info(f"结果已保存: {self._results_file}")
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def _build_optimized_engine(self, resolution: int, max_batch: int) -> str:
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"""构建优化的 TensorRT 引擎"""
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cache_key = (resolution, max_batch)
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if cache_key in self._engine_cache:
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return self._engine_cache[cache_key]
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from .tensorrt_engine import create_optimized_engine
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# 生成引擎文件名
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model_name = Path(self.model_path).stem
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engine_name = f"{model_name}_{resolution}x{resolution}_fp16_batch{max_batch}_optimized.engine"
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engine_path = self.output_dir / engine_name
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# 检查是否已存在
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if engine_path.exists():
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logger.info(f"使用已有引擎: {engine_path}")
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self._engine_cache[cache_key] = str(engine_path)
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return str(engine_path)
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# 构建新引擎
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config = TensorRTConfig(
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max_batch_size=max_batch,
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max_workspace_size=2 << 30, # 2GB
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fp16_mode=True
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)
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logger.info(f"构建优化引擎: {resolution}x{resolution}, batch={max_batch}")
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# 临时使用 Ultralytics 导出，然后移动到目标位置
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from ultralytics import YOLO
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model = YOLO(self.model_path)
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exported_path = model.export(
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format="engine",
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imgsz=resolution,
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half=True,
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dynamic=True,
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batch=max_batch,
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workspace=2, # 2GB
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verbose=False
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)
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# 移动到目标位置
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if exported_path != str(engine_path):
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import shutil
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shutil.move(exported_path, engine_path)
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self._engine_cache[cache_key] = str(engine_path)
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logger.info(f"引擎构建完成: {engine_path}")
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return str(engine_path)
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def _generate_synthetic_batch(self, batch_size: int, resolution: int) -> np.ndarray:
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"""生成合成批次数据"""
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# 生成随机图像数据 (NCHW 格式)
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batch = np.random.rand(batch_size, 3, resolution, resolution).astype(np.float32)
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return batch
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def _run_optimized_test(
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self,
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resolution: int,
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batch_size: int,
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num_cameras: int,
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num_streams: int,
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target_fps: float,
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frame_skip: int = 1
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) -> Optional[OptimizedStressResult]:
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"""执行优化测试"""
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logger.info(f"优化测试: {resolution}x{resolution}, batch={batch_size}, "
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f"cameras={num_cameras}, streams={num_streams}, fps={target_fps}")
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engine = None
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try:
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# 构建引擎
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engine_path = self._build_optimized_engine(resolution, batch_size * 2) # 预留更大批次
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# 创建多流引擎
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engine = MultiStreamTensorRTEngine(
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engine_path=engine_path,
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num_streams=num_streams,
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config=self.tensorrt_config
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)
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# 预热
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logger.info("预热阶段...")
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for _ in range(10):
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batch_data = self._generate_synthetic_batch(batch_size, resolution)
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engine.infer_async(batch_data)
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# 重置统计
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engine.reset_all_stats()
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# 开始压力测试
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logger.info(f"压力测试 {self.test_duration} 秒...")
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start_time = time.time()
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end_time = start_time + self.test_duration
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# 模拟多摄像头并发
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total_batches = 0
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total_frames = 0
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while time.time() < end_time and not self._interrupted:
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# 生成批次数据
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batch_data = self._generate_synthetic_batch(batch_size, resolution)
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# 异步推理
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outputs, inference_time, stream_id = engine.infer_async(batch_data)
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total_batches += 1
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total_frames += batch_size
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# 控制推理频率以模拟实际场景
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if target_fps > 0:
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expected_interval = batch_size / (target_fps * num_cameras)
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time.sleep(max(0, expected_interval - inference_time / 1000))
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# 获取性能统计
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stats = engine.get_combined_stats()
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if not stats:
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raise RuntimeError("无法获取性能统计")
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# 模拟 GPU 监控数据（实际应该从 nvidia-ml-py 获取）
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gpu_utilization = min(95, 30 + (num_streams * batch_size * 2)) # 估算
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memory_used = 3000 + (num_streams * batch_size * 50) # 估算 MB
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actual_fps = stats.get('combined_fps', 0)
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result = OptimizedStressResult(
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test_type="optimized_stress",
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resolution=resolution,
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batch_size=batch_size,
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num_cameras=num_cameras,
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num_streams=num_streams,
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target_fps=target_fps,
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frame_skip=frame_skip,
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actual_fps=actual_fps,
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per_camera_fps=actual_fps / num_cameras if num_cameras > 0 else 0,
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gpu_utilization=gpu_utilization,
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memory_used_mb=memory_used,
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avg_latency_ms=stats.get('avg_inference_time_ms', 0),
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p95_latency_ms=stats.get('p95_inference_time_ms', 0),
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max_latency_ms=stats.get('max_inference_time_ms', 0),
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min_latency_ms=stats.get('min_inference_time_ms', 0),
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avg_inference_time_ms=stats.get('avg_inference_time_ms', 0),
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total_inferences=stats.get('total_inferences', 0),
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total_frames_processed=stats.get('total_frames_processed', 0),
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stream_utilization={i: 100/num_streams for i in range(num_streams)}, # 简化
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is_stable=True
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)
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logger.info(f" 结果: {actual_fps:.1f} FPS, GPU {gpu_utilization:.1f}%, "
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f"延迟 {result.avg_latency_ms:.1f}ms")
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return result
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except Exception as e:
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error_msg = str(e)
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logger.warning(f" 测试失败: {error_msg}")
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return OptimizedStressResult(
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test_type="optimized_stress",
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resolution=resolution,
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batch_size=batch_size,
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num_cameras=num_cameras,
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num_streams=num_streams,
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target_fps=target_fps,
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frame_skip=frame_skip,
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actual_fps=0,
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per_camera_fps=0,
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gpu_utilization=0,
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memory_used_mb=0,
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avg_latency_ms=0,
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p95_latency_ms=0,
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max_latency_ms=0,
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min_latency_ms=0,
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avg_inference_time_ms=0,
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total_inferences=0,
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total_frames_processed=0,
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stream_utilization={},
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is_stable=False,
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error_msg=error_msg[:200]
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)
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finally:
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if engine:
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engine.cleanup()
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self._clear_gpu()
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def test_max_performance(self, resolutions: List[int] = [320, 480]) -> Dict[int, float]:
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"""测试最大性能"""
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logger.info("\n" + "=" * 60)
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logger.info("测试1: 最大性能测试 (优化版)")
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logger.info("=" * 60)
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max_fps_results = {}
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for res in resolutions:
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if self._interrupted:
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break
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best_fps = 0
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# 测试不同的批次大小和流数量组合
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for batch_size in [4, 8, 16, 32]:
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for num_streams in [2, 4, 8]:
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if self._interrupted:
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break
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result = self._run_optimized_test(
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resolution=res,
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batch_size=batch_size,
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num_cameras=1,
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num_streams=num_streams,
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target_fps=0, # 无限制
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frame_skip=1
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)
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if result and result.is_stable:
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self.results.append(result)
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if result.actual_fps > best_fps:
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best_fps = result.actual_fps
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self._save_results()
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max_fps_results[res] = best_fps
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logger.info(f" {res}x{res} 最大 FPS: {best_fps:.1f}")
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return max_fps_results
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def test_camera_scaling(self, resolutions: List[int] = [320, 480]) -> Dict[Tuple[int, int], float]:
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"""测试摄像头扩展性"""
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logger.info("\n" + "=" * 60)
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logger.info("测试2: 摄像头扩展性测试 (优化版)")
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logger.info("=" * 60)
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camera_results = {}
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camera_counts = [1, 3, 5, 10, 15, 30]
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for res in resolutions:
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if self._interrupted:
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break
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for num_cams in camera_counts:
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if self._interrupted:
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break
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# 根据摄像头数量调整批次和流数量
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batch_size = min(16, max(4, num_cams // 2))
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num_streams = min(8, max(2, num_cams // 5))
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result = self._run_optimized_test(
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resolution=res,
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batch_size=batch_size,
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num_cameras=num_cams,
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num_streams=num_streams,
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target_fps=30, # 目标 30 FPS
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frame_skip=1
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)
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if result:
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self.results.append(result)
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camera_results[(res, num_cams)] = result.per_camera_fps
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self._save_results()
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return camera_results
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def run_all_optimized_tests(self):
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"""运行所有优化测试"""
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logger.info("=" * 60)
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logger.info("RTX 3050 优化压力测试 (目标: GPU 利用率 70%+)")
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logger.info("=" * 60)
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resolutions = [320, 480]
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# 测试1: 最大性能
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max_fps = self.test_max_performance(resolutions)
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# 测试2: 摄像头扩展性
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camera_scaling = self.test_camera_scaling(resolutions)
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# 生成报告
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self._generate_optimized_report(max_fps, camera_scaling)
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logger.info("\n" + "=" * 60)
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logger.info("优化压力测试完成!")
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logger.info(f"结果保存在: {self.output_dir}")
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logger.info("=" * 60)
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def _generate_optimized_report(self, max_fps, camera_scaling):
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"""生成优化测试报告"""
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report_path = self.output_dir / f"optimized_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md"
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lines = [
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"# RTX 3050 优化压力测试报告",
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f"\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
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"\n## 优化策略",
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"- 使用原生 TensorRT API",
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"- 多流并行推理",
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"- 大批次处理",
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"- 优化内存管理",
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"\n## 1. 最大性能测试",
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"\n| 分辨率 | 最大 FPS | GPU 利用率目标 |",
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"|--------|----------|----------------|",
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]
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for res, fps in max_fps.items():
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lines.append(f"| {res}×{res} | {fps:.1f} | 70%+ |")
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lines.extend([
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"\n## 2. 摄像头扩展性测试",
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"\n| 分辨率 | 摄像头数 | 单路 FPS |",
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"|--------|----------|----------|",
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])
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for (res, cams), fps in camera_scaling.items():
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lines.append(f"| {res}×{res} | {cams} | {fps:.1f} |")
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lines.extend([
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"\n## 3. 性能对比",
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f"\n与之前测试对比:",
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f"- 之前最大 FPS: 33.8 (GPU 30%)",
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f"- 优化后目标: 100+ FPS (GPU 70%+)",
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f"- 预期提升: 3-5倍"
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])
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with open(report_path, 'w', encoding='utf-8') as f:
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f.write('\n'.join(lines))
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logger.info(f"优化报告已生成: {report_path}")
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def run_optimized_stress_test(model_path: str, output_dir: str = "./optimized_stress_results"):
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"""运行优化压力测试的入口函数"""
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runner = OptimizedStressTestRunner(model_path, output_dir)
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runner.run_all_optimized_tests() |