260 lines
8.8 KiB
Python
260 lines
8.8 KiB
Python
#!/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() |