217 lines
7.4 KiB
Python
217 lines
7.4 KiB
Python
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#!/usr/bin/env python3
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"""
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批量测试不同配置的性能
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自动测试不同批次大小、输入尺寸的组合
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"""
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import subprocess
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import json
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import os
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import time
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import pandas as pd
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import matplotlib.pyplot as plt
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from datetime import datetime
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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def run_test(batch_size, target_size, max_cameras, duration=30):
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"""运行单次测试"""
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print(f"\n{'='*60}")
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print(f"测试配置: batch_size={batch_size}, target_size={target_size}, cameras={max_cameras}")
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print(f"{'='*60}\n")
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cmd = [
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'python', 'optimized_multi_camera_tensorrt.py',
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'--batch-size', str(batch_size),
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'--target-size', str(target_size),
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'--max-cameras', str(max_cameras),
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'--duration', str(duration)
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]
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=duration+30)
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# 查找最新的结果文件
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result_dir = 'multi_camera_results'
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if os.path.exists(result_dir):
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files = [f for f in os.listdir(result_dir) if f.startswith('results_') and f.endswith('.json')]
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if files:
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latest_file = max(files, key=lambda f: os.path.getmtime(os.path.join(result_dir, f)))
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with open(os.path.join(result_dir, latest_file), 'r', encoding='utf-8') as f:
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data = json.load(f)
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return data
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return None
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except subprocess.TimeoutExpired:
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print(f"⚠️ 测试超时")
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return None
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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return None
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def main():
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"""主函数"""
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print("批量配置性能测试")
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print("=" * 60)
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# 测试配置
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test_configs = [
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# (batch_size, target_size, max_cameras)
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(2, 640, 5),
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(4, 640, 5),
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(8, 640, 5),
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(4, 480, 5),
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(4, 640, 10),
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(8, 640, 10),
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]
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test_duration = 30 # 每次测试30秒
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results = []
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for i, (batch_size, target_size, max_cameras) in enumerate(test_configs, 1):
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print(f"\n进度: {i}/{len(test_configs)}")
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data = run_test(batch_size, target_size, max_cameras, test_duration)
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if data:
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results.append({
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'batch_size': batch_size,
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'target_size': target_size,
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'max_cameras': max_cameras,
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'avg_fps': data['avg_fps'],
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'avg_inference_ms': data['avg_inference_ms'],
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'p95_inference_ms': data['p95_inference_ms'],
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'p99_inference_ms': data['p99_inference_ms'],
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'total_frames': data['total_frames']
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})
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# 等待系统稳定
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if i < len(test_configs):
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print("\n⏳ 等待系统稳定...")
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time.sleep(5)
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# 生成报告
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if results:
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generate_report(results)
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else:
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print("\n❌ 没有成功的测试结果")
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def generate_report(results):
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"""生成对比报告"""
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print(f"\n{'='*60}")
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print("批量测试结果汇总")
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print(f"{'='*60}\n")
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# 创建DataFrame
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df = pd.DataFrame(results)
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# 打印表格
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print(df.to_string(index=False))
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# 保存CSV
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output_dir = 'batch_test_results'
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os.makedirs(output_dir, exist_ok=True)
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csv_file = os.path.join(output_dir, f"batch_test_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv")
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df.to_csv(csv_file, index=False, encoding='utf-8-sig')
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print(f"\n✅ CSV已保存: {csv_file}")
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# 生成可视化
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generate_visualizations(df, output_dir)
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def generate_visualizations(df, output_dir):
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"""生成可视化图表"""
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print("\n🎨 生成可视化图表...")
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# 图表1: 批次大小 vs FPS（固定target_size=640, cameras=5）
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fig, axes = plt.subplots(2, 2, figsize=(16, 12))
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# 筛选数据
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df_640_5 = df[(df['target_size'] == 640) & (df['max_cameras'] == 5)]
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if not df_640_5.empty:
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# FPS vs Batch Size
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ax1 = axes[0, 0]
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ax1.plot(df_640_5['batch_size'], df_640_5['avg_fps'], 'o-', linewidth=2, markersize=10)
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ax1.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax1.set_ylabel('平均FPS', fontsize=12, fontweight='bold')
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ax1.set_title('批次大小 vs FPS (640x640, 5摄像头)', fontsize=14, fontweight='bold')
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ax1.grid(True, alpha=0.3)
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# 添加数值标签
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for x, y in zip(df_640_5['batch_size'], df_640_5['avg_fps']):
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ax1.text(x, y + 1, f'{y:.1f}', ha='center', va='bottom', fontweight='bold')
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# 延迟 vs Batch Size
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ax2 = axes[0, 1]
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ax2.plot(df_640_5['batch_size'], df_640_5['avg_inference_ms'], 'o-',
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linewidth=2, markersize=10, label='平均延迟')
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ax2.plot(df_640_5['batch_size'], df_640_5['p95_inference_ms'], 's-',
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linewidth=2, markersize=10, label='P95延迟')
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ax2.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax2.set_ylabel('延迟 (ms)', fontsize=12, fontweight='bold')
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ax2.set_title('批次大小 vs 延迟 (640x640, 5摄像头)', fontsize=14, fontweight='bold')
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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# 图表2: 摄像头数量 vs FPS（固定batch_size=4, target_size=640）
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df_4_640 = df[(df['batch_size'] == 4) & (df['target_size'] == 640)]
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if not df_4_640.empty:
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ax3 = axes[1, 0]
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ax3.plot(df_4_640['max_cameras'], df_4_640['avg_fps'], 'o-', linewidth=2, markersize=10)
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ax3.set_xlabel('摄像头数量', fontsize=12, fontweight='bold')
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ax3.set_ylabel('平均FPS', fontsize=12, fontweight='bold')
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ax3.set_title('摄像头数量 vs FPS (batch=4, 640x640)', fontsize=14, fontweight='bold')
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ax3.grid(True, alpha=0.3)
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# 添加数值标签
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for x, y in zip(df_4_640['max_cameras'], df_4_640['avg_fps']):
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ax3.text(x, y + 1, f'{y:.1f}', ha='center', va='bottom', fontweight='bold')
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# 图表3: 输入尺寸对比（固定batch_size=4, cameras=5）
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df_4_5 = df[(df['batch_size'] == 4) & (df['max_cameras'] == 5)]
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if not df_4_5.empty:
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ax4 = axes[1, 1]
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x = range(len(df_4_5))
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width = 0.35
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ax4.bar([i - width/2 for i in x], df_4_5['avg_fps'], width, label='FPS', alpha=0.8)
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ax4.bar([i + width/2 for i in x], df_4_5['avg_inference_ms'], width, label='延迟(ms)', alpha=0.8)
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ax4.set_xlabel('输入尺寸', fontsize=12, fontweight='bold')
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ax4.set_ylabel('数值', fontsize=12, fontweight='bold')
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ax4.set_title('输入尺寸对比 (batch=4, 5摄像头)', fontsize=14, fontweight='bold')
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ax4.set_xticks(x)
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ax4.set_xticklabels([f"{size}x{size}" for size in df_4_5['target_size']])
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ax4.legend()
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ax4.grid(True, alpha=0.3, axis='y')
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plt.tight_layout()
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chart_file = os.path.join(output_dir, f"batch_test_charts_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
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plt.savefig(chart_file, dpi=300, bbox_inches='tight')
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print(f"✅ 图表已保存: {chart_file}")
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plt.show()
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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❌ 测试异常: {e}")
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import traceback
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traceback.print_exc()
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