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add midtern report and change data source

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Jiao77
2025-11-09 18:02:40 +08:00
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#!/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()