From eec5d86dc431a846b9967c101f3865d1329dba75 Mon Sep 17 00:00:00 2001 From: spawner Date: Sat, 7 Jun 2025 12:33:55 +0800 Subject: [PATCH] Update analyze_teacache.py --- analyze_teacache.py | 370 ++++++++++++++++++++++---------------------- 1 file changed, 188 insertions(+), 182 deletions(-) diff --git a/analyze_teacache.py b/analyze_teacache.py index 085284d..eba6397 100644 --- a/analyze_teacache.py +++ b/analyze_teacache.py @@ -1,182 +1,188 @@ -try: - import json - import argparse - import pandas as pd - import matplotlib.pyplot as plt - from pathlib import Path - - def analyze_runs(data, max_lpips_thresh=None): - """ - 对加载的JSON数据进行处理和分析。 - """ - if not data: - print("错误：JSON文件中没有数据。") - return None, None - - # 数据预处理 - df = pd.DataFrame(data) - df = df.dropna(subset=['coefficients']) - if df.empty: - print("错误：未找到包含'coefficients'的有效运行记录。") - return None, None - - if max_lpips_thresh is not None and max_lpips_thresh > 0: - 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) - df = df.dropna(subset=['lpips_distance']) - df = df[df['lpips_distance'] <= max_lpips_thresh] - filtered_count = len(df) - print(f"已从 {original_count} 条记录中筛选出 {filtered_count} 条符合条件的记录。") - if df.empty: - print("警告：应用阈值后，没有符合条件的运行记录。") - return None, None - else: - print("警告: 数据中不包含 'lpips_distance'，无法应用质量门槛。") - - df['hit_ratio'] = (df['cache_hits'] / df['total_inferences']).fillna(0) - - # 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'相关的得分。") - baseline_time = df['generation_time'].max() - else: - baseline_time = baseline_runs['generation_time'].min() - - print(f"基准运行时间 (最快无缓存): {baseline_time:.2f} 秒") - - df['time_saved'] = baseline_time - df['generation_time'] - df.loc[df['time_saved'] < 0, 'time_saved'] = 0 - - if 'lpips_distance' in df.columns: - # 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 - results = {} - cached_runs = df[df['rel_l1_thresh'] > 0] - if cached_runs.empty: - print("警告: 未找到任何开启缓存的运行记录，无法推荐最优参数。") - return df, None - - 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] - results[name] = { - "coefficients": best_run['coefficients'], - "value": { - "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", - "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 - } - } - - return df, results - - def print_results(results): - if not results: - return - - print("\n" + "="*25 + " 分析结果 " + "="*25) - for name, data in results.items(): - print(f"\n--- {name} ---") - print(f" 🏆 最佳Coefficients: {data['coefficients']}") - print(" 相关指标:") - for key, val in data['value'].items(): - print(f" - {key}: {val}") - print("\n" + "="*62) - - - def create_plots(df, results, max_lpips_thresh=None): - if df is None or results is None: - print("数据不足，无法生成图表。") - return - - plot_df = df[df['rel_l1_thresh'] > 0].copy() - if plot_df.empty: - print("无缓存运行数据，无法生成图表。") - return - - # 图表1: 时间 vs LPIPS - if 'score_lpips' in plot_df.columns and plot_df['lpips_distance'].notna().any(): - plt.style.use('seaborn-v0_8-darkgrid') - 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='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: - title += f'\n(已过滤, LPIPS <= {max_lpips_thresh})' - ax1.set_title(title, fontsize=16) - - ax1.set_xlabel('生成时间 (秒) - 越低越好', fontsize=12) - ax1.set_ylabel('LPIPS 距离 - 越低越好', fontsize=12) - - 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) - - # 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='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) - - print("\n正在生成可视化图表，请在弹出的窗口中查看。关闭图表窗口后程序将结束。") - plt.tight_layout() - plt.show() - - if __name__ == "__main__": - parser = argparse.ArgumentParser(description="分析 TeaCache 的 JSON 输出文件，并找出最优参数。") - parser.add_argument("json_file", type=Path, help="指向 teacache_analysis.json 文件的路径。") - parser.add_argument( - "--max_lpips", - type=float, - default=None, - help="设置可接受的最大LPIPS距离阈值，用于过滤低质量数据。例如: --max_lpips 0.6" - ) - args = parser.parse_args() - - json_path = args.json_file - if not json_path.is_file(): - print(f"错误: 文件不存在 -> {json_path}") - else: - print(f"正在读取文件: {json_path}") - with open(json_path, 'r', encoding='utf-8') as f: - analysis_data = json.load(f) - - df, best_results = analyze_runs(analysis_data, args.max_lpips) - print_results(best_results) - create_plots(df, best_results, args.max_lpips) -except: - pass +import json +import argparse +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path + +def analyze_runs(data, max_lpips_thresh=None): + """ + 对加载的JSON数据进行处理和分析。 + """ + if not data: + print("错误：JSON文件中没有数据。") + return None, None + + # 数据预处理 + df = pd.DataFrame(data) + df = df.dropna(subset=['coefficients']) + if df.empty: + print("错误：未找到包含'coefficients'的有效运行记录。") + return None, None + + if max_lpips_thresh is not None and max_lpips_thresh > 0: + 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) + df = df.dropna(subset=['lpips_distance']) + df = df[df['lpips_distance'] <= max_lpips_thresh] + filtered_count = len(df) + print(f"已从 {original_count} 条记录中筛选出 {filtered_count} 条符合条件的记录。") + if df.empty: + print("警告：应用阈值后，没有符合条件的运行记录。") + return None, None + else: + print("警告: 数据中不包含 'lpips_distance'，无法应用质量门槛。") + + df['hit_ratio'] = (df['cache_hits'] / df['total_inferences']).fillna(0) + + # 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'相关的得分。") + baseline_time = df['generation_time'].max() + else: + baseline_time = baseline_runs['generation_time'].min() + + print(f"基准运行时间 (最快无缓存): {baseline_time:.2f} 秒") + + df['time_saved'] = baseline_time - df['generation_time'] + df.loc[df['time_saved'] < 0, 'time_saved'] = 0 + + if 'lpips_distance' in df.columns: + # 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 + results = {} + cached_runs = df[df['rel_l1_thresh'] > 0] + if cached_runs.empty: + print("警告: 未找到任何开启缓存的运行记录，无法推荐最优参数。") + return df, None + + 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] + results[name] = { + "coefficients": best_run['coefficients'], + "value": { + "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", + "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 + } + } + + return df, results + +def print_results(results): + if not results: + return + + print("\n" + "="*25 + " 分析结果 " + "="*25) + for name, data in results.items(): + print(f"\n--- {name} ---") + print(f" 🏆 最佳Coefficients: {data['coefficients']}") + print(" 相关指标:") + for key, val in data['value'].items(): + print(f" - {key}: {val}") + print("\n" + "="*62) + + +def create_plots(df, results, max_lpips_thresh=None): + if df is None or results is None: + print("Insufficient data to generate plots.") + return + + plot_df = df[df['rel_l1_thresh'] > 0].copy() + if plot_df.empty: + print("No cache-enabled run data available, cannot generate plots.") + return + + # Plot 1: Time vs LPIPS + if 'score_lpips' in plot_df.columns and plot_df['lpips_distance'].notna().any(): + plt.style.use('seaborn-v0_8-darkgrid') + 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='Quality-Hit Ratio Score (Hit Ratio / LPIPS)') + + title = 'Generation Speed vs Image Quality (LPIPS)' + if max_lpips_thresh: + title += f'\n(Filtering applied, LPIPS <= {max_lpips_thresh})' + ax1.set_title(title, fontsize=16) + + ax1.set_xlabel('Generation Time (seconds) - Lower is Better', fontsize=12) + ax1.set_ylabel('LPIPS Distance - Lower is Better', fontsize=12) + + 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, edgecolor='black', marker='*', + label='Best Quality-Hit Ratio Point') + ax1.text(best_point['generation_time'], best_point['lpips_distance'], + ' Best Combined Point', color='red', ha='left') + ax1.legend() + ax1.grid(True) + + # Plot 2: Time vs Cache Hit Ratio + 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='Speed-Hit Ratio Score (Time Saved * Hit Ratio)') + ax2.set_title('Generation Speed vs Cache Hit Rate', fontsize=16) + ax2.set_xlabel('Generation Time (seconds) - Lower is Better', fontsize=12) + ax2.set_ylabel('Cache Hit Rate - Higher is Better', 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, edgecolor='black', marker='*', + label='Best Speed-Hit Ratio Point') + ax2.text(best_point_hr['generation_time'], best_point_hr['hit_ratio'], + ' Best Combined Point', color='blue', ha='left') + ax2.legend() + ax2.grid(True) + + print("\nGenerating visualization charts. Please check the pop-up windows. The program will end after you close the chart windows.") + plt.tight_layout() + plt.show() + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="分析 TeaCache 的 JSON 输出文件，并找出最优参数。") + parser.add_argument("json_file", default="teacache_analysis.json", nargs='?', type=Path, help="指向 teacache_analysis.json 文件的路径。") + parser.add_argument( + "--max_lpips", + type=float, + default=0.56, + help="设置可接受的最大LPIPS距离阈值，用于过滤低质量数据。例如: --max_lpips 0.6" + ) + args = parser.parse_args() + + json_path = args.json_file + if not json_path.is_file(): + print(f"错误: 文件不存在 -> {json_path}") + else: + print(f"正在读取文件: {json_path}") + with open(json_path, 'r', encoding='utf-8') as f: + analysis_data = json.load(f) + + df, best_results = analyze_runs(analysis_data, args.max_lpips) + print_results(best_results) + create_plots(df, best_results, args.max_lpips)