349 lines
14 KiB
Python
349 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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性能测试结果可视化脚本
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生成 PyTorch vs TensorRT 性能对比图表
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"""
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import json
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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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 load_results(json_file):
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"""加载测试结果"""
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with open(json_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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def create_fps_comparison_chart(results, output_dir):
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"""创建 FPS 对比图表"""
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# 单帧推理 FPS 对比
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pytorch_single_fps = results['pytorch']['single_inference']['avg_fps']
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tensorrt_single_fps = results['tensorrt']['single_inference']['avg_fps']
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engines = ['PyTorch', 'TensorRT']
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fps_values = [pytorch_single_fps, tensorrt_single_fps]
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colors = ['#FF6B6B', '#4ECDC4']
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bars1 = ax1.bar(engines, fps_values, color=colors, alpha=0.8)
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ax1.set_title('单帧推理性能对比', fontsize=14, fontweight='bold')
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ax1.set_ylabel('FPS (帧/秒)', fontsize=12)
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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 bar, value in zip(bars1, fps_values):
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height + 1,
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f'{value:.1f}', ha='center', va='bottom', fontweight='bold')
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# 性能提升百分比
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improvement = (tensorrt_single_fps - pytorch_single_fps) / pytorch_single_fps * 100
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ax1.text(0.5, max(fps_values) * 0.8, f'TensorRT 提升: {improvement:.1f}%',
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ha='center', transform=ax1.transData, fontsize=12,
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bbox=dict(boxstyle="round,pad=0.3", facecolor="yellow", alpha=0.7))
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# 批量推理 FPS 对比（仅显示 PyTorch 的批量结果，因为 TensorRT 批量测试失败）
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if 'batch_inference' in results['pytorch']:
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batch_sizes = []
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pytorch_batch_fps = []
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for batch_result in results['pytorch']['batch_inference']:
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batch_sizes.append(batch_result['batch_size'])
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pytorch_batch_fps.append(batch_result['avg_fps'])
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ax2.plot(batch_sizes, pytorch_batch_fps, 'o-', color='#FF6B6B',
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linewidth=2, markersize=8, label='PyTorch')
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ax2.axhline(y=tensorrt_single_fps, color='#4ECDC4', linestyle='--',
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linewidth=2, label='TensorRT (单帧)')
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ax2.set_title('批量推理性能 (PyTorch)', fontsize=14, fontweight='bold')
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ax2.set_xlabel('批次大小', fontsize=12)
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ax2.set_ylabel('FPS (帧/秒)', fontsize=12)
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ax2.grid(True, alpha=0.3)
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ax2.legend()
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# 添加数值标签
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for i, (batch_size, fps) in enumerate(zip(batch_sizes, pytorch_batch_fps)):
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ax2.text(batch_size, fps + 2, f'{fps:.1f}', ha='center', va='bottom')
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, 'fps_comparison.png'), dpi=300, bbox_inches='tight')
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plt.show()
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def create_latency_comparison_chart(results, output_dir):
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"""创建延迟对比图表"""
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fig, ax = plt.subplots(1, 1, figsize=(10, 6))
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# 单帧推理延迟对比
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pytorch_latency = results['pytorch']['single_inference']['avg_latency_ms']
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tensorrt_latency = results['tensorrt']['single_inference']['avg_latency_ms']
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engines = ['PyTorch', 'TensorRT']
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latency_values = [pytorch_latency, tensorrt_latency]
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colors = ['#FF6B6B', '#4ECDC4']
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bars = ax.bar(engines, latency_values, color=colors, alpha=0.8)
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ax.set_title('推理延迟对比', fontsize=14, fontweight='bold')
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ax.set_ylabel('延迟 (毫秒)', fontsize=12)
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ax.grid(True, alpha=0.3)
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# 添加数值标签
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for bar, value in zip(bars, latency_values):
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height = bar.get_height()
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ax.text(bar.get_x() + bar.get_width()/2., height + 0.1,
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f'{value:.1f}ms', ha='center', va='bottom', fontweight='bold')
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# 延迟改善百分比
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improvement = (pytorch_latency - tensorrt_latency) / pytorch_latency * 100
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ax.text(0.5, max(latency_values) * 0.8, f'TensorRT 延迟减少: {improvement:.1f}%',
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ha='center', transform=ax.transData, fontsize=12,
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bbox=dict(boxstyle="round,pad=0.3", facecolor="lightgreen", alpha=0.7))
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, 'latency_comparison.png'), dpi=300, bbox_inches='tight')
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plt.show()
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def create_concurrent_performance_chart(results, output_dir):
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"""创建并发性能图表"""
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if 'concurrent_streams' not in results['pytorch']:
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return
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# 提取并发数据
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concurrent_counts = []
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pytorch_total_fps = []
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pytorch_single_fps = []
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for result in results['pytorch']['concurrent_streams']:
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concurrent_counts.append(result['concurrent_streams'])
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pytorch_total_fps.append(result['avg_fps'] * result['concurrent_streams'])
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pytorch_single_fps.append(result['avg_fps'])
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# 总 FPS 对比
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ax1.plot(concurrent_counts, pytorch_total_fps, 'o-', color='#FF6B6B',
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linewidth=2, markersize=8, label='PyTorch 总FPS')
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# TensorRT 理论总FPS（基于单帧性能）
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tensorrt_single_fps = results['tensorrt']['single_inference']['avg_fps']
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tensorrt_theoretical_fps = [tensorrt_single_fps * count for count in concurrent_counts]
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ax1.plot(concurrent_counts, tensorrt_theoretical_fps, '--', color='#4ECDC4',
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linewidth=2, label='TensorRT 理论总FPS')
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ax1.set_title('并发总FPS性能', fontsize=14, fontweight='bold')
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ax1.set_xlabel('并发摄像头数量', fontsize=12)
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ax1.set_ylabel('总FPS (帧/秒)', fontsize=12)
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ax1.grid(True, alpha=0.3)
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ax1.legend()
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# 单流平均 FPS
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ax2.plot(concurrent_counts, pytorch_single_fps, 'o-', color='#FF6B6B',
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linewidth=2, markersize=8, label='PyTorch 单流FPS')
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ax2.axhline(y=tensorrt_single_fps, color='#4ECDC4', linestyle='--',
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linewidth=2, label='TensorRT 单流FPS')
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ax2.set_title('单流平均FPS性能', fontsize=14, fontweight='bold')
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ax2.set_xlabel('并发摄像头数量', fontsize=12)
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ax2.set_ylabel('单流FPS (帧/秒)', fontsize=12)
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ax2.grid(True, alpha=0.3)
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ax2.legend()
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, 'concurrent_performance.png'), dpi=300, bbox_inches='tight')
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plt.show()
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def create_resource_utilization_chart(results, output_dir):
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"""创建资源利用率对比图表"""
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
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# GPU 利用率对比
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pytorch_gpu = results['pytorch']['single_inference']['avg_gpu_util']
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tensorrt_gpu = results['tensorrt']['single_inference']['avg_gpu_util']
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engines = ['PyTorch', 'TensorRT']
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gpu_utils = [pytorch_gpu, tensorrt_gpu]
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colors = ['#FF6B6B', '#4ECDC4']
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bars1 = ax1.bar(engines, gpu_utils, color=colors, alpha=0.8)
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ax1.set_title('GPU 利用率对比', fontsize=12, fontweight='bold')
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ax1.set_ylabel('GPU 利用率 (%)', fontsize=10)
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ax1.grid(True, alpha=0.3)
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for bar, value in zip(bars1, gpu_utils):
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height + 0.5,
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f'{value:.1f}%', ha='center', va='bottom', fontweight='bold')
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# GPU 内存使用对比
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pytorch_mem = results['pytorch']['single_inference']['avg_gpu_memory_mb']
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tensorrt_mem = results['tensorrt']['single_inference']['avg_gpu_memory_mb']
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gpu_mems = [pytorch_mem, tensorrt_mem]
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bars2 = ax2.bar(engines, gpu_mems, color=colors, alpha=0.8)
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ax2.set_title('GPU 内存使用对比', fontsize=12, fontweight='bold')
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ax2.set_ylabel('GPU 内存 (MB)', fontsize=10)
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ax2.grid(True, alpha=0.3)
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for bar, value in zip(bars2, gpu_mems):
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height = bar.get_height()
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ax2.text(bar.get_x() + bar.get_width()/2., height + 10,
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f'{value:.0f}MB', ha='center', va='bottom', fontweight='bold')
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# CPU 利用率对比
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pytorch_cpu = results['pytorch']['single_inference']['avg_cpu_util']
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tensorrt_cpu = results['tensorrt']['single_inference']['avg_cpu_util']
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cpu_utils = [pytorch_cpu, tensorrt_cpu]
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bars3 = ax3.bar(engines, cpu_utils, color=colors, alpha=0.8)
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ax3.set_title('CPU 利用率对比', fontsize=12, fontweight='bold')
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ax3.set_ylabel('CPU 利用率 (%)', fontsize=10)
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ax3.grid(True, alpha=0.3)
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for bar, value in zip(bars3, cpu_utils):
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height = bar.get_height()
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ax3.text(bar.get_x() + bar.get_width()/2., height + 0.2,
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f'{value:.1f}%', ha='center', va='bottom', fontweight='bold')
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# 综合性能效率（FPS/GPU利用率）
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pytorch_efficiency = pytorch_single_fps / pytorch_gpu if pytorch_gpu > 0 else 0
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tensorrt_efficiency = tensorrt_single_fps / tensorrt_gpu if tensorrt_gpu > 0 else 0
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efficiencies = [pytorch_efficiency, tensorrt_efficiency]
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bars4 = ax4.bar(engines, efficiencies, color=colors, alpha=0.8)
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ax4.set_title('性能效率对比 (FPS/GPU利用率)', fontsize=12, fontweight='bold')
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ax4.set_ylabel('效率 (FPS/%)', fontsize=10)
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ax4.grid(True, alpha=0.3)
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for bar, value in zip(bars4, efficiencies):
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height = bar.get_height()
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ax4.text(bar.get_x() + bar.get_width()/2., height + 0.05,
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f'{value:.1f}', ha='center', va='bottom', fontweight='bold')
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plt.tight_layout()
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plt.savefig(os.path.join(output_dir, 'resource_utilization.png'), dpi=300, bbox_inches='tight')
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plt.show()
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def generate_summary_report(results, output_dir):
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"""生成总结报告"""
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pytorch_single = results['pytorch']['single_inference']
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tensorrt_single = results['tensorrt']['single_inference']
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# 计算性能提升
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fps_improvement = (tensorrt_single['avg_fps'] - pytorch_single['avg_fps']) / pytorch_single['avg_fps'] * 100
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latency_improvement = (pytorch_single['avg_latency_ms'] - tensorrt_single['avg_latency_ms']) / pytorch_single['avg_latency_ms'] * 100
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gpu_util_change = tensorrt_single['avg_gpu_util'] - pytorch_single['avg_gpu_util']
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report = f"""
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YOLOv11 性能对比测试总结报告
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{'='*50}
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测试时间: {results['timestamp']}
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模型路径: {results['model_path']}
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单帧推理性能对比:
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{'='*30}
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PyTorch:
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- 平均FPS: {pytorch_single['avg_fps']:.1f}
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- 平均延迟: {pytorch_single['avg_latency_ms']:.1f}ms
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- GPU利用率: {pytorch_single['avg_gpu_util']:.1f}%
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- GPU内存: {pytorch_single['avg_gpu_memory_mb']:.0f}MB
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TensorRT:
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- 平均FPS: {tensorrt_single['avg_fps']:.1f}
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- 平均延迟: {tensorrt_single['avg_latency_ms']:.1f}ms
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- GPU利用率: {tensorrt_single['avg_gpu_util']:.1f}%
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- GPU内存: {tensorrt_single['avg_gpu_memory_mb']:.0f}MB
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性能提升:
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{'='*30}
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🚀 FPS 提升: {fps_improvement:.1f}%
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⚡ 延迟减少: {latency_improvement:.1f}%
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📊 GPU利用率变化: {gpu_util_change:+.1f}%
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批量推理性能 (PyTorch):
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{'='*30}"""
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if 'batch_inference' in results['pytorch']:
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for batch_result in results['pytorch']['batch_inference']:
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batch_size = batch_result['batch_size']
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batch_fps = batch_result['avg_fps']
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batch_latency = batch_result['avg_latency_ms']
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report += f"\n批次大小 {batch_size}: {batch_fps:.1f} FPS, {batch_latency:.1f}ms"
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if 'concurrent_streams' in results['pytorch']:
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report += f"\n\n并发性能 (PyTorch):\n{'='*30}"
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for conc_result in results['pytorch']['concurrent_streams']:
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conc_count = conc_result['concurrent_streams']
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total_fps = conc_result['avg_fps'] * conc_count
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single_fps = conc_result['avg_fps']
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report += f"\n{conc_count}路并发: 总FPS {total_fps:.1f}, 单流FPS {single_fps:.1f}"
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report += f"""
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推荐配置:
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{'='*30}
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✅ 单摄像头场景: 推荐使用 TensorRT (性能提升 {fps_improvement:.1f}%)
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✅ 多摄像头场景: 需要根据并发数量选择合适的引擎
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✅ 资源受限环境: TensorRT 在相同性能下GPU利用率更低
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注意事项:
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{'='*30}
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⚠️ TensorRT 引擎需要预先导出，首次导出耗时较长
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⚠️ TensorRT 批量推理需要固定批次大小的引擎
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⚠️ 实际部署时需要考虑模型加载时间和内存占用
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"""
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# 保存报告
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report_file = os.path.join(output_dir, 'performance_summary.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(report)
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print(f"\n📁 总结报告已保存: {report_file}")
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def main():
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"""主函数"""
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# 查找最新的测试结果文件
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results_dir = "benchmark_results"
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if not os.path.exists(results_dir):
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print("❌ 未找到测试结果目录")
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return
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json_files = [f for f in os.listdir(results_dir) if f.startswith('benchmark_results_') and f.endswith('.json')]
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if not json_files:
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print("❌ 未找到测试结果文件")
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return
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# 使用最新的结果文件
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latest_file = sorted(json_files)[-1]
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json_path = os.path.join(results_dir, latest_file)
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print(f"📊 加载测试结果: {json_path}")
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results = load_results(json_path)
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# 创建可视化输出目录
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viz_dir = os.path.join(results_dir, "visualizations")
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os.makedirs(viz_dir, exist_ok=True)
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print("🎨 生成可视化图表...")
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# 生成各种图表
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create_fps_comparison_chart(results, viz_dir)
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create_latency_comparison_chart(results, viz_dir)
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create_concurrent_performance_chart(results, viz_dir)
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create_resource_utilization_chart(results, viz_dir)
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# 生成总结报告
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generate_summary_report(results, viz_dir)
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print(f"\n✅ 所有可视化图表已生成完成！")
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print(f"📁 输出目录: {viz_dir}")
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if __name__ == "__main__":
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main() |