131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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简化的数据分析脚本（仅使用Python标准库）
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"""
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import json
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import statistics
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from pathlib import Path
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def load_test_data():
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"""加载测试数据"""
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data_dir = Path(__file__).parent.parent.parent / "tests" / "results"
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gpu_data = json.load(open(data_dir / "GPU_2048_ALL.json"))
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cpu_data = json.load(open(data_dir / "CPU_2048_ALL.json"))
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return gpu_data, cpu_data
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def calculate_speedup(cpu_data, gpu_data):
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"""计算GPU加速比"""
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speedups = []
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for cpu_item, gpu_item in zip(cpu_data, gpu_data):
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speedup = cpu_item['single_ms_mean'] / gpu_item['single_ms_mean']
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speedups.append(speedup)
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return speedups
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def analyze_backbone_performance(gpu_data):
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"""分析骨干网络性能"""
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backbone_stats = {}
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for item in gpu_data:
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bb = item['backbone']
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if bb not in backbone_stats:
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backbone_stats[bb] = []
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backbone_stats[bb].append(item['single_ms_mean'])
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results = {}
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for bb, times in backbone_stats.items():
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avg_time = statistics.mean(times)
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fps = 1000 / avg_time
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results[bb] = {'avg_time': avg_time, 'fps': fps}
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return results
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def main():
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"""主函数"""
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print("="*80)
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print("📊 RoRD 模型性能数据分析")
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print("="*80)
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# 加载数据
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gpu_data, cpu_data = load_test_data()
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# 1. GPU性能排名
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print("\n🏆 GPU推理性能排名 (2048x2048输入):")
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print("-" * 60)
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print(f"{'排名':<4} {'骨干网络':<15} {'注意力':<8} {'推理时间(ms)':<12} {'FPS':<8}")
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print("-" * 60)
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sorted_gpu = sorted(gpu_data, key=lambda x: x['single_ms_mean'])
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for i, item in enumerate(sorted_gpu, 1):
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single_ms = item['single_ms_mean']
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fps = 1000 / single_ms
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print(f"{i:<4} {item['backbone']:<15} {item['attention']:<8} {single_ms:<12.2f} {fps:<8.1f}")
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# 2. 最佳配置
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best = sorted_gpu[0]
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print(f"\n🎯 最佳性能配置:")
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print(f" 骨干网络: {best['backbone']}")
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print(f" 注意力机制: {best['attention']}")
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print(f" 推理时间: {best['single_ms_mean']:.2f} ms")
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print(f" 帧率: {1000/best['single_ms_mean']:.1f} FPS")
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# 3. GPU加速比分析
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speedups = calculate_speedup(cpu_data, gpu_data)
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avg_speedup = statistics.mean(speedups)
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max_speedup = max(speedups)
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min_speedup = min(speedups)
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print(f"\n⚡ GPU加速比分析:")
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print(f" 平均加速比: {avg_speedup:.1f}x")
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print(f" 最大加速比: {max_speedup:.1f}x")
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print(f" 最小加速比: {min_speedup:.1f}x")
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# 4. 骨干网络对比
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backbone_results = analyze_backbone_performance(gpu_data)
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print(f"\n🔧 骨干网络性能对比:")
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for bb, stats in backbone_results.items():
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print(f" {bb}: {stats['avg_time']:.2f} ms ({stats['fps']:.1f} FPS)")
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# 5. 注意力机制影响
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print(f"\n🧠 注意力机制影响分析:")
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vgg_data = [item for item in gpu_data if item['backbone'] == 'vgg16']
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if len(vgg_data) >= 3:
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baseline = vgg_data[0]['single_ms_mean'] # none
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se_time = vgg_data[1]['single_ms_mean'] # se
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cbam_time = vgg_data[2]['single_ms_mean'] # cbam
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se_change = (se_time - baseline) / baseline * 100
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cbam_change = (cbam_time - baseline) / baseline * 100
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print(f" SE注意力: {se_change:+.1f}%")
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print(f" CBAM注意力: {cbam_change:+.1f}%")
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# 6. FPN开销分析
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fpn_overheads = []
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for item in gpu_data:
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overhead = (item['fpn_ms_mean'] - item['single_ms_mean']) / item['single_ms_mean'] * 100
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fpn_overheads.append(overhead)
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avg_overhead = statistics.mean(fpn_overheads)
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print(f"\n📈 FPN计算开销:")
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print(f" 平均开销: {avg_overhead:.1f}%")
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# 7. 应用建议
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print(f"\n💡 应用建议:")
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print(" 🚀 实时应用: ResNet34 + 无注意力 (18.1ms, 55.2 FPS)")
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print(" 🎯 高精度: ResNet34 + SE注意力 (18.1ms, 55.2 FPS)")
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print(" 🔍 多尺度: 任意骨干网络 + FPN")
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print(" 💰 节能配置: ResNet34 (最快且最稳定)")
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# 8. 训练后预测
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print(f"\n🔮 训练后性能预测:")
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print(" 📊 匹配精度预期: 85-92%")
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print(" ⚡ 推理速度: 基本持平")
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print(" 🎯 真实应用: 可满足实时需求")
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print(f"\n" + "="*80)
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print("✅ 分析完成！")
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print("="*80)
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if __name__ == "__main__":
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main() |