200 lines
7.8 KiB
Python
200 lines
7.8 KiB
Python
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"""
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解析验证结果并生成对比报告
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"""
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import re
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import json
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from pathlib import Path
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from datetime import datetime
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import numpy as np
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class ResultsParser:
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def __init__(self):
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self.results_dir = Path("vehicle_person_benchmark")
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self.results_dir.mkdir(exist_ok=True)
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def parse_file(self, filepath):
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"""解析验证结果文件"""
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with open(filepath, 'r', encoding='utf-8', errors='replace') as f:
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content = f.read()
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results = {}
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# 提取速度信息
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speed_match = re.search(r'(\d+\.\d+)ms preprocess.*?(\d+\.\d+)ms inference.*?(\d+\.\d+)ms postprocess', content)
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if speed_match:
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results['preprocess_ms'] = float(speed_match.group(1))
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results['inference_ms'] = float(speed_match.group(2))
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results['postprocess_ms'] = float(speed_match.group(3))
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# 提取整体AP
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ap5095_match = re.search(r'Average Precision.*?IoU=0\.50:0\.95.*?=\s*([\d.]+)', content)
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if ap5095_match:
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results['mAP50_95'] = float(ap5095_match.group(1))
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ap50_match = re.search(r'Average Precision.*?IoU=0\.50\s.*?=\s*([\d.]+)', content)
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if ap50_match:
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results['mAP50'] = float(ap50_match.group(1))
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# 提取Person和Vehicle类别
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results['person'] = self._parse_category(content, 'person')
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results['car'] = self._parse_category(content, 'car')
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results['bicycle'] = self._parse_category(content, 'bicycle')
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results['motorcycle'] = self._parse_category(content, 'motorcycle')
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results['bus'] = self._parse_category(content, 'bus')
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results['truck'] = self._parse_category(content, 'truck')
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# 计算所有车辆的平均值
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vehicle_keys = ['bicycle', 'car', 'motorcycle', 'bus', 'truck']
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results['all_vehicles'] = {
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'ap50_95': np.mean([results[k].get('ap50_95', 0) for k in vehicle_keys]),
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'ap50': np.mean([results[k].get('ap50', 0) for k in vehicle_keys]),
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'P': np.mean([results[k].get('P', 0) for k in vehicle_keys]),
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'R': np.mean([results[k].get('R', 0) for k in vehicle_keys])
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}
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return results
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def _parse_category(self, content, category):
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"""解析特定类别的指标"""
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lines = content.split('\n')
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metrics = {}
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for i, line in enumerate(lines):
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if category in line.lower() and 'Class' not in line and '----' not in line:
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parts = line.split()
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if len(parts) >= 6:
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try:
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metrics['P'] = float(parts[2]) if parts[2] != '0' else 0.0
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metrics['R'] = float(parts[3]) if parts[3] != '0' else 0.0
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metrics['ap50'] = float(parts[4]) if parts[4] != '0' else 0.0
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metrics['ap50_95'] = float(parts[5]) if parts[5] != '0' else 0.0
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except:
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pass
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break
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return metrics
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def generate_report(self):
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"""生成完整报告"""
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all_results = {
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'FP32_PyTorch': self.parse_file('fp32_results.txt'),
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'INT8_640p': self.parse_file('int8_640_results.txt'),
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'FP16_640p': self.parse_file('fp16_640_results.txt'),
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'FP16_480p': self.parse_file('fp16_480_results.txt')
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}
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report = []
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report.append("="*70)
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report.append("YOLO11n 人和车辆检测性能对比分析报告")
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report.append("="*70)
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report.append(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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report.append("")
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# 1. 整体性能对比
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report.append("-"*70)
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report.append("一、整体性能对比")
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report.append("-"*70)
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report.append(f"{'配置':<15} {'mAP50-95':<12} {'mAP50':<12} {'推理(ms)':<12} {'FPS':<10}")
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report.append("-"*70)
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for name, data in all_results.items():
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map50_95 = data.get('mAP50_95', 0)
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map50 = data.get('mAP50', 0)
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inf_ms = data.get('inference_ms', 0)
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fps = round(1000/inf_ms, 1) if inf_ms > 0 else 0
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report.append(f"{name:<15} {map50_95:<12.4f} {map50:<12.4f} {inf_ms:<12.1f} {fps:<10.1f}")
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report.append("")
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# 2. Person类别
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report.append("-"*70)
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report.append("二、Person (人) 类别检测性能")
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report.append("-"*70)
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report.append(f"{'配置':<15} {'P':<10} {'R':<10} {'AP50':<12} {'AP50-95':<12}")
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report.append("-"*70)
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for name, data in all_results.items():
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person = data.get('person', {})
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p = person.get('P', 0)
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r = person.get('R', 0)
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ap50 = person.get('ap50', 0)
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ap50_95 = person.get('ap50_95', 0)
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report.append(f"{name:<15} {p:<10.3f} {r:<10.3f} {ap50:<12.4f} {ap50_95:<12.4f}")
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report.append("")
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# 3. Vehicle类别
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report.append("-"*70)
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report.append("三、Vehicles (车辆) 类别检测性能")
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report.append("-"*70)
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report.append(f"{'配置':<15} {'P':<10} {'R':<10} {'AP50':<12} {'AP50-95':<12}")
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report.append("-"*70)
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for name, data in all_results.items():
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vehicles = data.get('all_vehicles', {})
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p = vehicles.get('P', 0)
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r = vehicles.get('R', 0)
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ap50 = vehicles.get('ap50', 0)
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ap50_95 = vehicles.get('ap50_95', 0)
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report.append(f"{name:<15} {p:<10.3f} {r:<10.3f} {ap50:<12.4f} {ap50_95:<12.4f}")
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report.append("")
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# 4. 速度对比
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report.append("-"*70)
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report.append("四、推理速度对比")
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report.append("-"*70)
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speeds = [(name, data.get('inference_ms', 0)) for name, data in all_results.items() if data.get('inference_ms', 0) > 0]
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speeds.sort(key=lambda x: x[1])
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for i, (name, ms) in enumerate(speeds, 1):
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fps = 1000/ms if ms > 0 else 0
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report.append(f" {i}. {name}: {ms:.2f}ms ({fps:.1f} FPS)")
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report.append("")
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# 5. 计算掉点
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report.append("-"*70)
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report.append("五、精度掉点分析 (相对于FP32)")
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report.append("-"*70)
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fp32_map = all_results.get('FP32_PyTorch', {}).get('mAP50_95', 0)
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for name, data in all_results.items():
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if name == 'FP32_PyTorch' or data.get('mAP50_95', 0) == 0:
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continue
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if fp32_map > 0:
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drop = (fp32_map - data['mAP50_95']) / fp32_map * 100
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report.append(f" {name}: mAP50-95 掉点 {drop:.2f}%")
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report.append("")
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# 6. 结论
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report.append("="*70)
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report.append("六、结论与建议")
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report.append("="*70)
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report.append("")
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report.append("1. 精度最优: 选择 FP16_640p 或 INT8_640p")
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report.append("2. 速度最快: 选择 FP16_480p")
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report.append("3. 性价比: 推荐 FP16_640p (平衡精度和速度)")
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report.append("4. INT8量化在人和车辆检测上表现良好")
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report.append("")
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# 保存报告
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report_text = '\n'.join(report)
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report_file = self.results_dir / "final_report.txt"
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with open(report_file, 'w', encoding='utf-8') as f:
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f.write(report_text)
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print(report_text)
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print(f"\n报告已保存: {report_file}")
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# 保存JSON数据
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with open(self.results_dir / "all_results.json", 'w', encoding='utf-8') as f:
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json.dump(all_results, f, indent=2, ensure_ascii=False)
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if __name__ == "__main__":
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parser = ResultsParser()
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parser.generate_report()
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