#!/usr/bin/env python3 """ 中期报告性能分析可视化脚本 生成各种图表用于中期报告展示 """ import json import matplotlib.pyplot as plt import numpy as np import seaborn as sns from pathlib import Path # 设置中文字体 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False def load_test_data(): """加载测试数据""" data_dir = Path(__file__).parent.parent.parent / "tests" / "results" gpu_data = json.load(open(data_dir / "GPU_2048_ALL.json")) cpu_data = json.load(open(data_dir / "CPU_2048_ALL.json")) return gpu_data, cpu_data def create_performance_comparison(gpu_data, cpu_data): """创建性能对比图表""" # 提取数据 backbones = [] single_gpu = [] fpn_gpu = [] single_cpu = [] fpn_cpu = [] for item in gpu_data: backbones.append(f"{item['backbone']}\n({item['attention']})") single_gpu.append(item['single_ms_mean']) fpn_gpu.append(item['fpn_ms_mean']) for item in cpu_data: single_cpu.append(item['single_ms_mean']) fpn_cpu.append(item['fpn_ms_mean']) # 创建图表 fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12)) # 图1: GPU单尺度性能 bars1 = ax1.bar(backbones, single_gpu, color='skyblue', alpha=0.8) ax1.set_title('GPU单尺度推理性能 (ms)', fontsize=14, fontweight='bold') ax1.set_ylabel('推理时间 (ms)') ax1.tick_params(axis='x', rotation=45) # 添加数值标签 for bar in bars1: height = bar.get_height() ax1.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}', ha='center', va='bottom') # 图2: GPU FPN性能 bars2 = ax2.bar(backbones, fpn_gpu, color='lightcoral', alpha=0.8) ax2.set_title('GPU FPN推理性能 (ms)', fontsize=14, fontweight='bold') ax2.set_ylabel('推理时间 (ms)') ax2.tick_params(axis='x', rotation=45) for bar in bars2: height = bar.get_height() ax2.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}', ha='center', va='bottom') # 图3: GPU vs CPU 单尺度对比 x = np.arange(len(backbones)) width = 0.35 bars3 = ax3.bar(x - width/2, single_gpu, width, label='GPU', color='skyblue', alpha=0.8) bars4 = ax3.bar(x + width/2, single_cpu, width, label='CPU', color='orange', alpha=0.8) ax3.set_title('GPU vs CPU 单尺度性能对比', fontsize=14, fontweight='bold') ax3.set_ylabel('推理时间 (ms)') ax3.set_xticks(x) ax3.set_xticklabels(backbones, rotation=45) ax3.legend() ax3.set_yscale('log') # 使用对数坐标 # 图4: 加速比分析 speedup = [c/g for c, g in zip(single_cpu, single_gpu)] bars5 = ax4.bar(backbones, speedup, color='green', alpha=0.8) ax4.set_title('GPU加速比分析', fontsize=14, fontweight='bold') ax4.set_ylabel('加速比 (倍)') ax4.tick_params(axis='x', rotation=45) ax4.grid(True, alpha=0.3) for bar in bars5: height = bar.get_height() ax4.text(bar.get_x() + bar.get_width()/2., height, f'{height:.1f}x', ha='center', va='bottom') plt.tight_layout() plt.savefig(Path(__file__).parent / "performance_comparison.png", dpi=300, bbox_inches='tight') plt.show() def create_attention_analysis(gpu_data): """创建注意力机制分析图表""" # 按骨干网络分组 backbone_attention = {} for item in gpu_data: backbone = item['backbone'] attention = item['attention'] if backbone not in backbone_attention: backbone_attention[backbone] = {} backbone_attention[backbone][attention] = { 'single': item['single_ms_mean'], 'fpn': item['fpn_ms_mean'] } # 创建图表 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # 单尺度性能 backbones = list(backbone_attention.keys()) attentions = ['none', 'se', 'cbam'] x = np.arange(len(backbones)) width = 0.25 for i, att in enumerate(attentions): single_times = [backbone_attention[bb].get(att, {}).get('single', 0) for bb in backbones] bars = ax1.bar(x + i*width, single_times, width, label=f'{att.upper()}' if att != 'none' else 'None', alpha=0.8) ax1.set_title('注意力机制对单尺度性能影响', fontsize=14, fontweight='bold') ax1.set_ylabel('推理时间 (ms)') ax1.set_xticks(x + width) ax1.set_xticklabels(backbones) ax1.legend() # FPN性能 for i, att in enumerate(attentions): fpn_times = [backbone_attention[bb].get(att, {}).get('fpn', 0) for bb in backbones] bars = ax2.bar(x + i*width, fpn_times, width, label=f'{att.upper()}' if att != 'none' else 'None', alpha=0.8) ax2.set_title('注意力机制对FPN性能影响', fontsize=14, fontweight='bold') ax2.set_ylabel('推理时间 (ms)') ax2.set_xticks(x + width) ax2.set_xticklabels(backbones) ax2.legend() plt.tight_layout() plt.savefig(Path(__file__).parent / "attention_analysis.png", dpi=300, bbox_inches='tight') plt.show() def create_efficiency_analysis(gpu_data): """创建效率分析图表""" # 计算FPS和效率指标 results = [] for item in gpu_data: single_fps = 1000 / item['single_ms_mean'] # 单尺度FPS fpn_fps = 1000 / item['fpn_ms_mean'] # FPN FPS fpn_overhead = (item['fpn_ms_mean'] - item['single_ms_mean']) / item['single_ms_mean'] * 100 results.append({ 'backbone': item['backbone'], 'attention': item['attention'], 'single_fps': single_fps, 'fpn_fps': fpn_fps, 'fpn_overhead': fpn_overhead }) # 排序 results.sort(key=lambda x: x['single_fps'], reverse=True) # 创建图表 fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10)) # 图1: FPS排名 names = [f"{r['backbone']}\n({r['attention']})" for r in results] single_fps = [r['single_fps'] for r in results] bars1 = ax1.barh(names, single_fps, color='gold', alpha=0.8) ax1.set_title('模型推理速度排名 (FPS)', fontsize=14, fontweight='bold') ax1.set_xlabel('每秒帧数 (FPS)') for bar in bars1: width = bar.get_width() ax1.text(width + 1, bar.get_y() + bar.get_height()/2, f'{width:.1f}', ha='left', va='center') # 图2: FPN开销分析 fpn_overhead = [r['fpn_overhead'] for r in results] bars2 = ax2.barh(names, fpn_overhead, color='lightgreen', alpha=0.8) ax2.set_title('FPN计算开销 (%)', fontsize=14, fontweight='bold') ax2.set_xlabel('开销百分比 (%)') for bar in bars2: width = bar.get_width() ax2.text(width + 1, bar.get_y() + bar.get_height()/2, f'{width:.1f}%', ha='left', va='center') # 图3: 骨干网络性能对比 backbone_fps = {} for r in results: bb = r['backbone'] if bb not in backbone_fps: backbone_fps[bb] = [] backbone_fps[bb].append(r['single_fps']) backbones = list(backbone_fps.keys()) avg_fps = [np.mean(backbone_fps[bb]) for bb in backbones] std_fps = [np.std(backbone_fps[bb]) for bb in backbones] bars3 = ax3.bar(backbones, avg_fps, yerr=std_fps, capsize=5, color='skyblue', alpha=0.8, edgecolor='navy') ax3.set_title('骨干网络平均性能对比', fontsize=14, fontweight='bold') ax3.set_ylabel('平均FPS') ax3.grid(True, alpha=0.3) # 图4: 性能分类 performance_categories = {'优秀': [], '良好': [], '一般': []} for r in results: fps = r['single_fps'] if fps >= 50: performance_categories['优秀'].append(r) elif fps >= 30: performance_categories['良好'].append(r) else: performance_categories['一般'].append(r) categories = list(performance_categories.keys()) counts = [len(performance_categories[cat]) for cat in categories] colors = ['gold', 'silver', 'orange'] wedges, texts, autotexts = ax4.pie(counts, labels=categories, colors=colors, autopct='%1.0f%%', startangle=90) ax4.set_title('模型性能分布', fontsize=14, fontweight='bold') plt.tight_layout() plt.savefig(Path(__file__).parent / "efficiency_analysis.png", dpi=300, bbox_inches='tight') plt.show() def main(): """主函数""" print("正在生成中期报告可视化图表...") # 加载数据 gpu_data, cpu_data = load_test_data() # 生成图表 create_performance_comparison(gpu_data, cpu_data) create_attention_analysis(gpu_data) create_efficiency_analysis(gpu_data) print("图表生成完成！保存在 docs/reports/ 目录下") if __name__ == "__main__": main()