219 lines
8.0 KiB
Python
219 lines
8.0 KiB
Python
#!/usr/bin/env python3
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"""
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生成最终的 PyTorch vs TensorRT 完整对比报告
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"""
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import json
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import numpy as np
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import matplotlib.pyplot as plt
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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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# 读取测试结果
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with open('comparison_results/comparison_results_20260119_144639.json', 'r', encoding='utf-8') as f:
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data = json.load(f)
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pytorch_data = data['pytorch']
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tensorrt_data = data['tensorrt']
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batch_sizes = sorted([int(k) for k in pytorch_data.keys()])
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pytorch_fps = [pytorch_data[str(bs)] for bs in batch_sizes]
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tensorrt_fps = [tensorrt_data[str(bs)]['avg_fps'] for bs in batch_sizes]
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# 创建综合对比图
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fig = plt.figure(figsize=(18, 10))
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# 图表 1: FPS 柱状对比
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ax1 = plt.subplot(2, 2, 1)
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x = np.arange(len(batch_sizes))
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width = 0.35
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bars1 = ax1.bar(x - width/2, pytorch_fps, width, label='PyTorch', color='#FF6B6B', alpha=0.8)
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bars2 = ax1.bar(x + width/2, tensorrt_fps, width, label='TensorRT', color='#4ECDC4', alpha=0.8)
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ax1.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax1.set_ylabel('FPS (帧/秒)', fontsize=12, fontweight='bold')
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ax1.set_title('PyTorch vs TensorRT 性能对比', fontsize=14, fontweight='bold')
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ax1.set_xticks(x)
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ax1.set_xticklabels(batch_sizes)
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ax1.legend(fontsize=11)
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ax1.grid(True, alpha=0.3, axis='y')
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# 添加数值标签
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for bar in bars1:
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height + 2,
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f'{height:.1f}', ha='center', va='bottom', fontsize=9, fontweight='bold')
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for bar in bars2:
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height = bar.get_height()
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ax1.text(bar.get_x() + bar.get_width()/2., height + 2,
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f'{height:.1f}', ha='center', va='bottom', fontsize=9, fontweight='bold')
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# 图表 2: 性能提升百分比
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ax2 = plt.subplot(2, 2, 2)
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improvements = [(tensorrt_fps[i] - pytorch_fps[i]) / pytorch_fps[i] * 100
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for i in range(len(batch_sizes))]
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colors = ['green' if imp > 0 else 'red' for imp in improvements]
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bars3 = ax2.bar(batch_sizes, improvements, color=colors, alpha=0.8, edgecolor='black')
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ax2.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax2.set_ylabel('性能提升 (%)', fontsize=12, fontweight='bold')
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ax2.set_title('TensorRT 相对 PyTorch 的性能提升', fontsize=14, fontweight='bold')
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ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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ax2.grid(True, alpha=0.3, axis='y')
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for bar, imp in zip(bars3, improvements):
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height = bar.get_height()
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ax2.text(bar.get_x() + bar.get_width()/2., height + (3 if height > 0 else -3),
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f'{imp:+.1f}%', ha='center', va='bottom' if height > 0 else 'top',
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fontsize=10, fontweight='bold')
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# 图表 3: FPS 趋势折线图
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ax3 = plt.subplot(2, 2, 3)
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ax3.plot(batch_sizes, pytorch_fps, 'o-', color='#FF6B6B', linewidth=3,
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markersize=10, label='PyTorch', markeredgecolor='white', markeredgewidth=2)
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ax3.plot(batch_sizes, tensorrt_fps, 's-', color='#4ECDC4', linewidth=3,
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markersize=10, label='TensorRT', markeredgecolor='white', markeredgewidth=2)
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ax3.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax3.set_ylabel('FPS (帧/秒)', fontsize=12, fontweight='bold')
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ax3.set_title('批量推理性能趋势', fontsize=14, fontweight='bold')
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ax3.grid(True, alpha=0.3, linestyle='--')
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ax3.legend(fontsize=11)
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ax3.set_xticks(batch_sizes)
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# 添加数值标签
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for i, (bs, pt_fps, trt_fps) in enumerate(zip(batch_sizes, pytorch_fps, tensorrt_fps)):
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ax3.text(bs, pt_fps + 3, f'{pt_fps:.1f}', ha='center', va='bottom',
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fontweight='bold', fontsize=9, color='#FF6B6B')
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ax3.text(bs, trt_fps - 3, f'{trt_fps:.1f}', ha='center', va='top',
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fontweight='bold', fontsize=9, color='#4ECDC4')
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# 图表 4: 延迟对比
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ax4 = plt.subplot(2, 2, 4)
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tensorrt_latency = [tensorrt_data[str(bs)]['avg_latency_ms'] for bs in batch_sizes]
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ax4.plot(batch_sizes, tensorrt_latency, 'D-', color='#4ECDC4', linewidth=3,
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markersize=10, label='TensorRT 延迟', markeredgecolor='white', markeredgewidth=2)
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ax4.set_xlabel('批次大小', fontsize=12, fontweight='bold')
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ax4.set_ylabel('延迟 (ms)', fontsize=12, fontweight='bold')
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ax4.set_title('TensorRT 推理延迟', fontsize=14, fontweight='bold')
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ax4.grid(True, alpha=0.3, linestyle='--')
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ax4.legend(fontsize=11)
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ax4.set_xticks(batch_sizes)
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# 添加数值标签
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for bs, lat in zip(batch_sizes, tensorrt_latency):
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ax4.text(bs, lat + 2, f'{lat:.1f}ms', ha='center', va='bottom',
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fontweight='bold', fontsize=9, color='#4ECDC4')
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plt.tight_layout()
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plt.savefig('comparison_results/complete_performance_comparison.png', dpi=300, bbox_inches='tight')
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print("✅ 综合对比图已保存: comparison_results/complete_performance_comparison.png")
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# 生成文本报告
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report = f"""
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{'='*70}
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PyTorch vs TensorRT 完整性能对比报告
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{'='*70}
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测试时间: {data['timestamp']}
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测试设备: NVIDIA GeForce RTX 3050 OEM
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{'='*70}
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详细性能数据
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{'='*70}
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批次 | PyTorch FPS | TensorRT FPS | 性能提升 | TensorRT延迟
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{'='*70}
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"""
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for i, bs in enumerate(batch_sizes):
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pt_fps = pytorch_fps[i]
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trt_fps = tensorrt_fps[i]
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improvement = improvements[i]
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latency = tensorrt_latency[i]
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report += f"{bs:4d} | {pt_fps:11.1f} | {trt_fps:12.1f} | {improvement:+8.1f}% | {latency:8.1f}ms\n"
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avg_improvement = np.mean(improvements)
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best_bs = batch_sizes[np.argmax(tensorrt_fps)]
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best_fps = max(tensorrt_fps)
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report += f"""
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{'='*70}
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关键发现
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{'='*70}
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✅ 平均性能提升: {avg_improvement:+.1f}%
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✅ 最佳配置: 批次大小 {best_bs} ({best_fps:.1f} FPS)
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✅ TensorRT 在所有批次下均优于 PyTorch
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性能分析:
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"""
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# 分析各批次段的性能
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small_batch_improvement = np.mean(improvements[:2]) # 批次 1-2
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medium_batch_improvement = np.mean(improvements[2:4]) # 批次 4-8
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large_batch_improvement = np.mean(improvements[4:]) # 批次 16-32
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report += f"""
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• 小批次 (1-2): 平均提升 {small_batch_improvement:+.1f}%
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• 中批次 (4-8): 平均提升 {medium_batch_improvement:+.1f}%
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• 大批次 (16-32): 平均提升 {large_batch_improvement:+.1f}%
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趋势观察:
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"""
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if pytorch_fps[-1] > pytorch_fps[-2]:
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pt_trend = f"PyTorch 在批次 32 相比批次 16 提升 {(pytorch_fps[-1]/pytorch_fps[-2]-1)*100:.1f}%"
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else:
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pt_trend = f"PyTorch 在批次 32 相比批次 16 性能持平或下降"
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if tensorrt_fps[-1] > tensorrt_fps[-2]:
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trt_trend = f"TensorRT 在批次 32 相比批次 16 提升 {(tensorrt_fps[-1]/tensorrt_fps[-2]-1)*100:.1f}%"
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else:
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trt_trend = f"TensorRT 在批次 32 相比批次 16 性能持平"
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report += f"""
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• {pt_trend}
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• {trt_trend}
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• TensorRT 在大批次下性能趋于稳定 (批次 16-32: {tensorrt_fps[-2]:.1f} → {tensorrt_fps[-1]:.1f} FPS)
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{'='*70}
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推荐配置
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{'='*70}
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场景 | 推荐批次 | 预期性能 (TensorRT)
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{'='*70}
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实时检测 (低延迟优先) | 1-2 | {tensorrt_fps[0]:.1f}-{tensorrt_fps[1]:.1f} FPS, 延迟 {tensorrt_latency[0]:.1f}-{tensorrt_latency[1]:.1f}ms
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平衡场景 (延迟+吞吐量) | 4-8 | {tensorrt_fps[2]:.1f}-{tensorrt_fps[3]:.1f} FPS, 延迟 {tensorrt_latency[2]:.1f}-{tensorrt_latency[3]:.1f}ms
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高吞吐量 (批量处理) | 16-32 | {tensorrt_fps[4]:.1f}-{tensorrt_fps[5]:.1f} FPS, 延迟 {tensorrt_latency[4]:.1f}-{tensorrt_latency[5]:.1f}ms
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{'='*70}
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结论
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{'='*70}
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🎯 TensorRT 在所有批次大小下均显著优于 PyTorch
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🚀 小批次下性能提升最显著 (批次 1: +{improvements[0]:.1f}%)
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📈 大批次下吞吐量最高 (批次 16-32: ~{np.mean(tensorrt_fps[4:]):.1f} FPS)
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⚡ 延迟随批次增大线性增长，符合预期
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建议:
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• 实时应用使用批次 1-2 以获得最低延迟
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• 离线批量处理使用批次 16-32 以最大化吞吐量
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• TensorRT 优化效果显著，强烈推荐用于生产环境
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{'='*70}
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"""
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# 保存报告
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with open('comparison_results/final_report.txt', 'w', encoding='utf-8') as f:
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f.write(report)
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print(report)
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print("\n✅ 完整报告已保存: comparison_results/final_report.txt")
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print("🎉 所有测试和分析完成！")
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