Add YOLO11 TensorRT quantization benchmark scripts

- Engine build scripts (FP16/INT8)
- Benchmark validation scripts
- Result parsing and analysis tools
- COCO dataset configuration

generate_charts.py

@@ -0,0 +1,108 @@
+"""
+生成性能对比图表
+"""
+
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+from pathlib import Path
+
+def generate_charts():
+    results_dir = Path("vehicle_person_benchmark")
+    results_file = results_dir / "all_results.json"
+    
+    if not results_file.exists():
+        print("请先运行 parse_results.py 生成结果数据")
+        return
+    
+    with open(results_file, 'r', encoding='utf-8') as f:
+        all_results = json.load(f)
+    
+    # 准备数据
+    configs = list(all_results.keys())
+    
+    map50_95 = [all_results[c].get('mAP50_95', 0) for c in configs]
+    map50 = [all_results[c].get('mAP50', 0) for c in configs]
+    inference = [all_results[c].get('inference_ms', 0) for c in configs]
+    fps = [1000/i if i > 0 else 0 for i in inference]
+    
+    person_ap5095 = [all_results[c].get('person', {}).get('ap50_95', 0) for c in configs]
+    vehicle_ap5095 = [all_results[c].get('all_vehicles', {}).get('ap50_95', 0) for c in configs]
+    
+    colors = ['#2ecc71', '#3498db', '#e74c3c', '#9b59b6']
+    
+    fig, axes = plt.subplots(2, 3, figsize=(16, 10))
+    fig.suptitle('YOLO11n 人和车辆检测性能对比', fontsize=14, fontweight='bold')
+    
+    # 1. mAP50-95
+    ax1 = axes[0, 0]
+    bars1 = ax1.bar(configs, map50_95, color=colors)
+    ax1.set_title('整体 mAP50-95', fontsize=12)
+    ax1.set_ylabel('mAP50-95')
+    ax1.set_ylim(0, max(map50_95) * 1.3 if max(map50_95) > 0 else 1)
+    for bar, val in zip(bars1, map50_95):
+        if val > 0:
+            ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, 
+                    f'{val:.3f}', ha='center', va='bottom', fontsize=9)
+    
+    # 2. mAP50
+    ax2 = axes[0, 1]
+    bars2 = ax2.bar(configs, map50, color=colors)
+    ax2.set_title('整体 mAP50', fontsize=12)
+    ax2.set_ylabel('mAP50')
+    ax2.set_ylim(0, max(map50) * 1.3 if max(map50) > 0 else 1)
+    for bar, val in zip(bars2, map50):
+        if val > 0:
+            ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, 
+                    f'{val:.3f}', ha='center', va='bottom', fontsize=9)
+    
+    # 3. 推理速度
+    ax3 = axes[0, 2]
+    bars3 = ax3.bar(configs, inference, color=colors)
+    ax3.set_title('推理速度 (ms)', fontsize=12)
+    ax3.set_ylabel('推理时间 (ms)')
+    for bar, val in zip(bars3, inference):
+        if val > 0:
+            ax3.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, 
+                    f'{val:.1f}ms', ha='center', va='bottom', fontsize=9)
+    
+    # 4. Person mAP50-95
+    ax4 = axes[1, 0]
+    bars4 = ax4.bar(configs, person_ap5095, color=colors)
+    ax4.set_title('Person mAP50-95', fontsize=12)
+    ax4.set_ylabel('AP50-95')
+    ax4.set_ylim(0, max(person_ap5095) * 1.3 if max(person_ap5095) > 0 else 1)
+    for bar, val in zip(bars4, person_ap5095):
+        if val > 0:
+            ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, 
+                    f'{val:.3f}', ha='center', va='bottom', fontsize=9)
+    
+    # 5. Vehicle mAP50-95
+    ax5 = axes[1, 1]
+    bars5 = ax5.bar(configs, vehicle_ap5095, color=colors)
+    ax5.set_title('Vehicles mAP50-95', fontsize=12)
+    ax5.set_ylabel('AP50-95')
+    ax5.set_ylim(0, max(vehicle_ap5095) * 1.3 if max(vehicle_ap5095) > 0 else 1)
+    for bar, val in zip(bars5, vehicle_ap5095):
+        if val > 0:
+            ax5.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, 
+                    f'{val:.3f}', ha='center', va='bottom', fontsize=9)
+    
+    # 6. FPS
+    ax6 = axes[1, 2]
+    bars6 = ax6.bar(configs, fps, color=colors)
+    ax6.set_title('FPS 对比', fontsize=12)
+    ax6.set_ylabel('FPS')
+    for bar, val in zip(bars6, fps):
+        if val > 0:
+            ax6.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1, 
+                    f'{val:.1f}', ha='center', va='bottom', fontsize=9)
+    
+    plt.tight_layout()
+    
+    chart_file = results_dir / "benchmark_charts.png"
+    plt.savefig(chart_file, dpi=150, bbox_inches='tight')
+    print(f"图表已保存: {chart_file}")
+
+if __name__ == "__main__":
+    generate_charts()