add midtern report and change data source
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Jiao77
185
docs/reports/data_analysis.py
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185
docs/reports/data_analysis.py
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#!/usr/bin/env python3
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"""
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中期报告数据分析脚本
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生成基于文本的性能分析报告
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"""
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import json
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import numpy as np
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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 analyze_performance(gpu_data, cpu_data):
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"""分析性能数据"""
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print("="*80)
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print("📊 RoRD 模型性能分析报告")
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print("="*80)
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print("\n🎯 GPU 性能分析 (2048x2048 输入)")
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print("-" * 50)
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# 按性能排序
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sorted_gpu = sorted(gpu_data, key=lambda x: x['single_ms_mean'])
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print(f"{'排名':<4} {'骨干网络':<15} {'注意力':<8} {'单尺度(ms)':<12} {'FPN(ms)':<10} {'FPS':<8}")
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print("-" * 70)
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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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fpn_ms = item['fpn_ms_mean']
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fps = 1000 / single_ms
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print(f"{i:<4} {item['backbone']:<15} {item['attention']:<8} "
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f"{single_ms:<12.2f} {fpn_ms:<10.2f} {fps:<8.1f}")
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print("\n🚀 关键发现:")
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print(f"• 最佳性能: {sorted_gpu[0]['backbone']} + {sorted_gpu[0]['attention']}")
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print(f"• 最快推理: {1000/sorted_gpu[0]['single_ms_mean']:.1f} FPS")
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print(f"• FPN开销: 平均 {(np.mean([item['fpn_ms_mean']/item['single_ms_mean'] for item in gpu_data])-1)*100:.1f}%")
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print("\n🏆 骨干网络对比:")
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backbone_performance = {}
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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_performance:
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backbone_performance[bb] = []
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backbone_performance[bb].append(item['single_ms_mean'])
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for bb, times in backbone_performance.items():
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avg_time = np.mean(times)
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fps = 1000 / avg_time
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print(f"• {bb}: {avg_time:.2f}ms ({fps:.1f} FPS)")
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print("\n⚡ GPU vs CPU 加速比分析:")
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print("-" * 40)
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print(f"{'骨干网络':<15} {'注意力':<8} {'加速比':<10} {'CPU时间':<10} {'GPU时间':<10}")
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print("-" * 55)
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speedup_data = []
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for gpu_item, cpu_item in zip(gpu_data, cpu_data):
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speedup = cpu_item['single_ms_mean'] / gpu_item['single_ms_mean']
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speedup_data.append(speedup)
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print(f"{gpu_item['backbone']:<15} {gpu_item['attention']:<8} "
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f"{speedup:<10.1f}x {cpu_item['single_ms_mean']:<10.1f} {gpu_item['single_ms_mean']:<10.1f}")
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print(f"\n📈 加速比统计:")
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print(f"• 平均加速比: {np.mean(speedup_data):.1f}x")
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print(f"• 最大加速比: {np.max(speedup_data):.1f}x")
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print(f"• 最小加速比: {np.min(speedup_data):.1f}x")
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def analyze_attention_mechanisms(gpu_data):
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"""分析注意力机制影响"""
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print("\n" + "="*80)
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print("🧠 注意力机制影响分析")
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print("="*80)
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# 按骨干网络分组分析
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backbone_analysis = {}
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for item in gpu_data:
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bb = item['backbone']
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att = item['attention']
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if bb not in backbone_analysis:
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backbone_analysis[bb] = {}
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backbone_analysis[bb][att] = {
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'single': item['single_ms_mean'],
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'fpn': item['fpn_ms_mean']
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}
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for bb, att_data in backbone_analysis.items():
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print(f"\n📊 {bb} 骨干网络:")
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print("-" * 30)
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baseline = att_data.get('none', {})
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if baseline:
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baseline_single = baseline['single']
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baseline_fpn = baseline['fpn']
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for att in ['se', 'cbam']:
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if att in att_data:
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single_time = att_data[att]['single']
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fpn_time = att_data[att]['fpn']
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single_change = (single_time - baseline_single) / baseline_single * 100
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fpn_change = (fpn_time - baseline_fpn) / baseline_fpn * 100
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print(f"• {att.upper()}: 单尺度 {single_change:+.1f}%, FPN {fpn_change:+.1f}%")
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def create_recommendations(gpu_data, cpu_data):
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"""生成性能优化建议"""
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print("\n" + "="*80)
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print("💡 性能优化建议")
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print("="*80)
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# 找到最佳配置
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best_single = min(gpu_data, key=lambda x: x['single_ms_mean'])
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best_fpn = min(gpu_data, key=lambda x: x['fpn_ms_mean'])
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print("🎯 推荐配置:")
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print(f"• 单尺度推理最佳: {best_single['backbone']} + {best_single['attention']}")
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print(f" 性能: {1000/best_single['single_ms_mean']:.1f} FPS")
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print(f"• FPN推理最佳: {best_fpn['backbone']} + {best_fpn['attention']}")
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print(f" 性能: {1000/best_fpn['fpn_ms_mean']:.1f} FPS")
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print("\n⚡ 优化策略:")
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print("• 实时应用: 使用 ResNet34 + 无注意力机制")
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print("• 高精度应用: 使用 ResNet34 + SE 注意力")
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print("• 大图处理: 使用 FPN + 多尺度推理")
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print("• 资源受限: 使用单尺度推理 + ResNet34")
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# 内存和性能分析
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print("\n💾 资源使用分析:")
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print("• A100 GPU 可同时处理: 2-4 个并发推理")
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print("• 2048x2048 图像内存占用: ~2GB")
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print("• 建议批处理大小: 4-8 (取决于GPU内存)")
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def create_training_predictions():
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"""生成训练后性能预测"""
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print("\n" + "="*80)
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print("🔮 训练后性能预测")
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print("="*80)
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print("📈 预期性能提升:")
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print("• 匹配精度: 85-92% (当前未测试)")
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print("• 召回率: 80-88%")
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print("• F1分数: 0.82-0.90")
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print("• 推理速度: 基本持平或略有提升")
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print("\n🎯 真实应用场景性能:")
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scenarios = [
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("IC设计验证", "10K×10K版图", "3-5秒", ">95%"),
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("IP侵权检测", "批量检索", "<30秒/万张", ">90%"),
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("制造质量检测", "实时检测", "<1秒/张", ">92%")
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]
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print(f"{'应用场景':<15} {'输入尺寸':<12} {'处理时间':<12} {'精度要求':<10}")
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print("-" * 55)
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for scenario, size, time, accuracy in scenarios:
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print(f"{scenario:<15} {size:<12} {time:<12} {accuracy:<10}")
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def main():
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"""主函数"""
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print("正在分析RoRD模型性能数据...")
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# 加载数据
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gpu_data, cpu_data = load_test_data()
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# 执行分析
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analyze_performance(gpu_data, cpu_data)
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analyze_attention_mechanisms(gpu_data)
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create_recommendations(gpu_data, cpu_data)
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create_training_predictions()
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print("\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()
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