nodes/spawner1145-CUI-Lumina2-TeaCache
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Update analyze_teacache.py

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spawner
2025-06-07 01:06:43 +08:00
committed by GitHub
parent 39b4882823
commit 167b5c4f9b
1 changed files with 15 additions and 17 deletions
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analyze_teacache.py
+15 -17
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@@ -24,7 +24,6 @@ try:
if 'lpips_distance' in df.columns:
print("\n" + "-"*15 + f" 应用质量门槛 (LPIPS <= {max_lpips_thresh}) " + "-"*15)
original_count = len(df)
# 筛选前，确保lpips_distance列存在且不为空值
df = df.dropna(subset=['lpips_distance'])
df = df[df['lpips_distance'] <= max_lpips_thresh]
filtered_count = len(df)
@@ -37,13 +36,10 @@ try:
df['hit_ratio'] = (df['cache_hits'] / df['total_inferences']).fillna(0)
# 寻找基准和计算节省时间
# 寻找基准和计算节省时间 (仅用于“速度-命中率”得分)
baseline_runs = df[df['rel_l1_thresh'] == 0]
if baseline_runs.empty:
print("警告：未找到基准运行 (rel_l1_thresh == 0)，无法计算'节省时间'和'综合得分'。")
if df.empty:
# 如果筛选后df为空，无法继续
return None, None
print("警告：未找到基准运行 (rel_l1_thresh == 0)，无法计算'节省时间'相关的得分。")
baseline_time = df['generation_time'].max()
else:
baseline_time = baseline_runs['generation_time'].min()
@@ -53,12 +49,14 @@ try:
df['time_saved'] = baseline_time - df['generation_time']
df.loc[df['time_saved'] < 0, 'time_saved'] = 0
# 计算两种综合效率得分
if 'lpips_distance' in df.columns:
df['score_lpips'] = (df['time_saved'] / df['lpips_distance'].replace(0, float('inf'))).fillna(0)
# 命中率 / LPIPS距离
df['score_lpips'] = (df['hit_ratio'] / df['lpips_distance'].replace(0, float('inf'))).fillna(0)
else:
print("警告: 数据中不包含 'lpips_distance'，跳过LPIPS相关分析。")
df['score_lpips'] = 0
# “速度-命中率”的得分公式保持不变
df['score_hit_ratio'] = df['time_saved'] * df['hit_ratio']
# 找出各项最佳参数
@@ -71,11 +69,11 @@ try:
best_indices = {
"最快生成速度": cached_runs['generation_time'].idxmin(),
"最高缓存命中率": cached_runs['hit_ratio'].idxmax(),
"最佳综合效率 (命中率)": cached_runs['score_hit_ratio'].idxmax(),
"最佳速度-命中率综合效率": cached_runs['score_hit_ratio'].idxmax(), # 名字更新
}
if df['score_lpips'].sum() > 0 and 'lpips_distance' in cached_runs.columns and cached_runs['lpips_distance'].notna().any():
best_indices["最低LPIPS (最佳画质)"] = cached_runs['lpips_distance'].idxmin()
best_indices["最佳综合效率 (LPIPS)"] = cached_runs['score_lpips'].idxmax()
best_indices["最佳质量-命中率综合效率 (LPIPS)"] = cached_runs['score_lpips'].idxmax() # 名字更新
for name, idx in best_indices.items():
best_run = df.loc[idx]
@@ -85,8 +83,8 @@ try:
"生成时间": f"{best_run['generation_time']:.2f}s",
"命中率": f"{best_run['hit_ratio']:.2%}",
"LPIPS": f"{best_run.get('lpips_distance', 'N/A'):.4f}" if pd.notna(best_run.get('lpips_distance')) else "N/A",
"综合得分(命中率)": f"{best_run['score_hit_ratio']:.2f}",
"综合得分(LPIPS)": f"{best_run['score_lpips']:.2f}",
"速度-命中率得分": f"{best_run['score_hit_ratio']:.2f}",
"质量-命中率得分(LPIPS)": f"{best_run['score_lpips']:.4f}", # 增加小数位精度
}
}
@@ -98,7 +96,7 @@ try:
print("\n" + "="*25 + " 分析结果 " + "="*25)
for name, data in results.items():
print(f"\n--- {name}")
print(f"\n--- {name} ---")
print(f" 🏆 最佳Coefficients: {data['coefficients']}")
print(" 相关指标:")
for key, val in data['value'].items():
@@ -122,7 +120,7 @@ try:
fig1, ax1 = plt.subplots(figsize=(12, 8))
scatter1 = ax1.scatter(plot_df['generation_time'], plot_df['lpips_distance'], c=plot_df['score_lpips'], cmap='viridis', alpha=0.7, s=50)
fig1.colorbar(scatter1, label='综合效率得分 (LPIPS Score)')
fig1.colorbar(scatter1, label='质量-命中率综合得分 (Hit Ratio / LPIPS)')
title = '生成速度 vs 图像质量 (LPIPS)'
if max_lpips_thresh:
@@ -134,7 +132,7 @@ try:
best_lpips_score_idx = plot_df['score_lpips'].idxmax()
best_point = plot_df.loc[best_lpips_score_idx]
ax1.scatter(best_point['generation_time'], best_point['lpips_distance'], color='red', s=150, ec='black', marker='*', label='最佳LPIPS综合效率')
ax1.scatter(best_point['generation_time'], best_point['lpips_distance'], color='red', s=150, ec='black', marker='*', label='最佳质量-命中率综合效率点')
ax1.text(best_point['generation_time'], best_point['lpips_distance'], ' 最佳综合点', color='red', ha='left')
ax1.legend()
ax1.grid(True)
@@ -142,14 +140,14 @@ try:
# 图表2: 时间 vs 缓存命中率
fig2, ax2 = plt.subplots(figsize=(12, 8))
scatter2 = ax2.scatter(plot_df['generation_time'], plot_df['hit_ratio'], c=plot_df['score_hit_ratio'], cmap='plasma', alpha=0.7, s=50)
fig2.colorbar(scatter2, label='综合效率得分 (Hit Ratio Score)')
fig2.colorbar(scatter2, label='速度-命中率综合得分 (Time Saved * Hit Ratio)')
ax2.set_title('生成速度 vs 缓存命中率', fontsize=16)
ax2.set_xlabel('生成时间 (秒) - 越低越好', fontsize=12)
ax2.set_ylabel('缓存命中率 - 越高越好', fontsize=12)
ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: '{:.0%}'.format(y)))
best_hr_score_idx = plot_df['score_hit_ratio'].idxmax()
best_point_hr = plot_df.loc[best_hr_score_idx]
ax2.scatter(best_point_hr['generation_time'], best_point_hr['hit_ratio'], color='blue', s=150, ec='black', marker='*', label='最佳命中率综合效率')
ax2.scatter(best_point_hr['generation_time'], best_point_hr['hit_ratio'], color='blue', s=150, ec='black', marker='*', label='最佳速度-命中率综合效率点')
ax2.text(best_point_hr['generation_time'], best_point_hr['hit_ratio'], ' 最佳综合点', color='blue', ha='left')
ax2.legend()
ax2.grid(True)