TensorRT测试
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306
real_world_quick_test.py
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306
real_world_quick_test.py
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import cv2
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import numpy as np
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import yaml
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import torch
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from ultralytics import YOLO
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import time
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import datetime
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import json
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import os
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def test_real_world_performance(model_path, config_path, framework_name, test_duration=30, max_cameras=5):
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"""测试真实场景性能"""
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print(f"\n{'='*60}")
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print(f"测试框架: {framework_name}")
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print(f"{'='*60}")
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# 加载配置
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with open(config_path, 'r', encoding='utf-8') as f:
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cfg = yaml.safe_load(f)
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# 加载模型
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print(f"🚀 加载模型: {model_path}")
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print(f" 设备: {device}")
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model = YOLO(model_path, task='detect')
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# TensorRT引擎不需要.to()，直接使用即可
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model_cfg = cfg['model']
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imgsz = model_cfg['imgsz']
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conf_thresh = model_cfg['conf_threshold']
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# 选择前N个摄像头
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cameras = cfg['cameras'][:max_cameras]
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print(f"✅ 测试 {len(cameras)} 个摄像头")
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# 打开视频流
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caps = []
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cam_ids = []
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for cam_cfg in cameras:
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cam_id = cam_cfg['id']
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rtsp_url = cam_cfg['rtsp_url']
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print(f"📹 连接摄像头 {cam_id}...")
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cap = cv2.VideoCapture(rtsp_url, cv2.CAP_FFMPEG)
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if cap.isOpened():
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cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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caps.append(cap)
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cam_ids.append(cam_id)
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print(f" ✅ {cam_id} 连接成功")
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else:
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print(f" ⚠️ {cam_id} 连接失败")
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if not caps:
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print("❌ 没有可用的摄像头")
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return None
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print(f"\n✅ 成功连接 {len(caps)} 个摄像头")
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print(f"⏳ 开始测试，时长: {test_duration}秒\n")
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# 性能统计
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frame_count = 0
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inference_times = []
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start_time = time.time()
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last_print_time = start_time
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try:
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while (time.time() - start_time) < test_duration:
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for i, cap in enumerate(caps):
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ret, frame = cap.read()
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if not ret:
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continue
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# 推理
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infer_start = time.time()
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results = model.predict(
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frame,
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imgsz=imgsz,
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conf=conf_thresh,
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verbose=False,
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device=0 if device == 'cuda' else 'cpu',
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half=(device == 'cuda'),
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classes=[0]
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)
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infer_end = time.time()
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inference_times.append((infer_end - infer_start) * 1000)
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frame_count += 1
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# 每5秒打印一次进度
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current_time = time.time()
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if current_time - last_print_time >= 5.0:
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elapsed = current_time - start_time
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avg_fps = frame_count / elapsed
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avg_latency = np.mean(inference_times) if inference_times else 0
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print(f"⏱️ {elapsed:.0f}s | 帧数: {frame_count} | FPS: {avg_fps:.1f} | 延迟: {avg_latency:.1f}ms")
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last_print_time = current_time
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except KeyboardInterrupt:
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print("\n⏹️ 测试被用户中断")
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finally:
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# 释放资源
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for cap in caps:
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cap.release()
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# 计算统计
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elapsed = time.time() - start_time
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avg_fps = frame_count / elapsed if elapsed > 0 else 0
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stats = {
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'framework': framework_name,
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'total_frames': frame_count,
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'elapsed_time': elapsed,
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'avg_fps': avg_fps,
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'avg_inference_time_ms': np.mean(inference_times) if inference_times else 0,
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'p50_inference_time_ms': np.percentile(inference_times, 50) if inference_times else 0,
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'p95_inference_time_ms': np.percentile(inference_times, 95) if inference_times else 0,
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'p99_inference_time_ms': np.percentile(inference_times, 99) if inference_times else 0,
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'num_cameras': len(caps)
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}
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print(f"\n{'='*60}")
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print(f"{framework_name} 测试完成")
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print(f"{'='*60}")
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print(f"总帧数: {stats['total_frames']}")
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print(f"测试时长: {stats['elapsed_time']:.1f}秒")
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print(f"平均FPS: {stats['avg_fps']:.1f}")
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print(f"平均推理延迟: {stats['avg_inference_time_ms']:.1f}ms")
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print(f"P95推理延迟: {stats['p95_inference_time_ms']:.1f}ms")
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print(f"P99推理延迟: {stats['p99_inference_time_ms']:.1f}ms")
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print(f"{'='*60}\n")
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return stats
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def main():
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"""主函数"""
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print("真实场景快速性能测试")
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print("=" * 60)
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config_path = "config.yaml"
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pytorch_model = "C:/Users/16337/PycharmProjects/Security/yolo11n.pt"
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tensorrt_model = "C:/Users/16337/PycharmProjects/Security/yolo11n.engine"
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# 检查文件
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if not os.path.exists(config_path):
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print(f"❌ 配置文件不存在: {config_path}")
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return
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if not os.path.exists(pytorch_model):
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print(f"❌ PyTorch 模型不存在: {pytorch_model}")
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return
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if not os.path.exists(tensorrt_model):
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print(f"❌ TensorRT 引擎不存在: {tensorrt_model}")
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return
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# 检查 CUDA
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if not torch.cuda.is_available():
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print("❌ CUDA 不可用")
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return
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print(f"✅ CUDA 可用，设备: {torch.cuda.get_device_name(0)}")
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# 测试配置
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test_duration = 30 # 每个框架测试30秒
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max_cameras = 5 # 只测试前5个摄像头
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results = {}
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# 测试 PyTorch
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print(f"\n{'='*60}")
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print("测试 1/2: PyTorch 框架")
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print(f"{'='*60}")
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try:
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pytorch_stats = test_real_world_performance(
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pytorch_model, config_path, "PyTorch",
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test_duration=test_duration, max_cameras=max_cameras
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)
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results['pytorch'] = pytorch_stats
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except Exception as e:
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print(f"❌ PyTorch 测试失败: {e}")
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import traceback
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traceback.print_exc()
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results['pytorch'] = None
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# 等待系统稳定
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print("\n⏳ 等待系统稳定...")
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time.sleep(3)
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# 测试 TensorRT
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print(f"\n{'='*60}")
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print("测试 2/2: TensorRT 框架")
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print(f"{'='*60}")
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try:
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tensorrt_stats = test_real_world_performance(
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tensorrt_model, config_path, "TensorRT",
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test_duration=test_duration, max_cameras=max_cameras
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)
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results['tensorrt'] = tensorrt_stats
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except Exception as e:
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print(f"❌ TensorRT 测试失败: {e}")
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import traceback
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traceback.print_exc()
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results['tensorrt'] = None
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# 生成对比报告
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print(f"\n{'='*60}")
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print("性能对比报告")
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print(f"{'='*60}\n")
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if results['pytorch'] and results['tensorrt']:
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pt_stats = results['pytorch']
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trt_stats = results['tensorrt']
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print(f"指标 | PyTorch | TensorRT | 提升")
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print(f"{'-'*60}")
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print(f"平均FPS | {pt_stats['avg_fps']:12.1f} | {trt_stats['avg_fps']:12.1f} | {(trt_stats['avg_fps']/pt_stats['avg_fps']-1)*100:+.1f}%")
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print(f"平均推理延迟(ms) | {pt_stats['avg_inference_time_ms']:12.1f} | {trt_stats['avg_inference_time_ms']:12.1f} | {(1-trt_stats['avg_inference_time_ms']/pt_stats['avg_inference_time_ms'])*100:+.1f}%")
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print(f"P95推理延迟(ms) | {pt_stats['p95_inference_time_ms']:12.1f} | {trt_stats['p95_inference_time_ms']:12.1f} | {(1-trt_stats['p95_inference_time_ms']/pt_stats['p95_inference_time_ms'])*100:+.1f}%")
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print(f"P99推理延迟(ms) | {pt_stats['p99_inference_time_ms']:12.1f} | {trt_stats['p99_inference_time_ms']:12.1f} | {(1-trt_stats['p99_inference_time_ms']/pt_stats['p99_inference_time_ms'])*100:+.1f}%")
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print(f"总帧数 | {pt_stats['total_frames']:12d} | {trt_stats['total_frames']:12d} | {(trt_stats['total_frames']/pt_stats['total_frames']-1)*100:+.1f}%")
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print(f"摄像头数量 | {pt_stats['num_cameras']:12d} | {trt_stats['num_cameras']:12d} |")
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# 保存结果
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output_dir = "real_world_results"
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os.makedirs(output_dir, exist_ok=True)
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results_data = {
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'pytorch': pt_stats,
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'tensorrt': trt_stats,
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'timestamp': datetime.datetime.now().isoformat(),
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'test_duration': test_duration,
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'max_cameras': max_cameras
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}
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json_file = os.path.join(output_dir, f"real_world_quick_test_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.json")
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with open(json_file, 'w', encoding='utf-8') as f:
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json.dump(results_data, f, indent=2, ensure_ascii=False)
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print(f"\n✅ 结果已保存: {json_file}")
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# 生成文本报告
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report = f"""
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真实场景性能测试报告
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{'='*60}
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测试时间: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
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测试时长: {test_duration}秒
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摄像头数量: {max_cameras}个
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详细对比数据:
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{'='*60}
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指标 | PyTorch | TensorRT | 提升
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{'-'*60}
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平均FPS | {pt_stats['avg_fps']:12.1f} | {trt_stats['avg_fps']:12.1f} | {(trt_stats['avg_fps']/pt_stats['avg_fps']-1)*100:+.1f}%
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平均推理延迟(ms) | {pt_stats['avg_inference_time_ms']:12.1f} | {trt_stats['avg_inference_time_ms']:12.1f} | {(1-trt_stats['avg_inference_time_ms']/pt_stats['avg_inference_time_ms'])*100:+.1f}%
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P50推理延迟(ms) | {pt_stats['p50_inference_time_ms']:12.1f} | {trt_stats['p50_inference_time_ms']:12.1f} | {(1-trt_stats['p50_inference_time_ms']/pt_stats['p50_inference_time_ms'])*100:+.1f}%
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P95推理延迟(ms) | {pt_stats['p95_inference_time_ms']:12.1f} | {trt_stats['p95_inference_time_ms']:12.1f} | {(1-trt_stats['p95_inference_time_ms']/pt_stats['p95_inference_time_ms'])*100:+.1f}%
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P99推理延迟(ms) | {pt_stats['p99_inference_time_ms']:12.1f} | {trt_stats['p99_inference_time_ms']:12.1f} | {(1-trt_stats['p99_inference_time_ms']/pt_stats['p99_inference_time_ms'])*100:+.1f}%
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总帧数 | {pt_stats['total_frames']:12d} | {trt_stats['total_frames']:12d} | {(trt_stats['total_frames']/pt_stats['total_frames']-1)*100:+.1f}%
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关键发现:
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{'='*60}
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✅ TensorRT 在真实场景下平均FPS提升: {(trt_stats['avg_fps']/pt_stats['avg_fps']-1)*100:+.1f}%
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✅ TensorRT 推理延迟降低: {(1-trt_stats['avg_inference_time_ms']/pt_stats['avg_inference_time_ms'])*100:+.1f}%
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✅ TensorRT 在相同时间内处理更多帧: {(trt_stats['total_frames']/pt_stats['total_frames']-1)*100:+.1f}%
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说明:
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{'='*60}
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本测试接入真实RTSP视频流，包含完整的业务逻辑：
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- 视频流解码
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- YOLO目标检测（person类）
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- ROI区域判断
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- 离岗检测算法
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- 周界入侵检测算法
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测试结果反映了实际生产环境的性能表现。
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"""
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report_file = os.path.join(output_dir, f"real_world_report_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.txt")
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with open(report_file, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f"✅ 报告已保存: {report_file}")
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else:
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print("❌ 测试未完成，无法生成对比报告")
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print(f"\n🎉 测试完成！")
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if __name__ == "__main__":
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try:
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main()
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except KeyboardInterrupt:
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print("\n\n⏹️ 测试被用户中断")
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except Exception as e:
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print(f"\n❌ 测试过程中发生错误: {e}")
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import traceback
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traceback.print_exc()
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