#!/usr/bin/env python3 """ 生成按退化类型分类的详细表格 输出格式: Corruption Category novel_ap50_PC_c novel_ap50_rPC(%) base_ap50_PC_c base_ap50_rPC(%) all_ap50_PC_c all_ap50_rPC(%) gaussian_noise noise 27.32 72.83 38.18 69.50 35.34 70.15 shot_noise noise 27.17 72.43 37.67 68.57 34.92 69.32 ... """ import os import json import argparse import pickle try: import pandas as pd HAS_PANDAS = True except ImportError: HAS_PANDAS = False print("Warning: pandas not installed, Excel output disabled") # 15 种 benchmark 退化类型 BENCHMARK_CORRUPTIONS = [ 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' ] # 退化类别 CORRUPTION_CATEGORIES = { 'gaussian_noise': 'noise', 'shot_noise': 'noise', 'impulse_noise': 'noise', 'defocus_blur': 'blur', 'glass_blur': 'blur', 'motion_blur': 'blur', 'zoom_blur': 'blur', 'snow': 'weather', 'frost': 'weather', 'fog': 'weather', 'brightness': 'weather', 'contrast': 'digital', 'elastic_transform': 'digital', 'pixelate': 'digital', 'jpeg_compression': 'digital' } # 预定义的 Clean 数据性能 (P_clean) PREDEFINED_CLEAN_METRICS = { 'clearclip': { 'base_ap50': 44.00, 'novel_ap50': 26.74, 'all_ap50': 39.49, }, 'clipself': { 'base_ap50': 54.94, 'novel_ap50': 37.51, 'all_ap50': 50.38, } } def load_model_results(results_dir, model_name): """加载单个模型的结果""" # 尝试加载 merged_results.pkl (包含详细的 corruption_avg) pkl_path = os.path.join(results_dir, 'merged_results.pkl') if os.path.exists(pkl_path): with open(pkl_path, 'rb') as f: data = pickle.load(f) return data # 尝试加载 JSON 汇总 json_path = os.path.join(results_dir, 'robustness_summary.json') if os.path.exists(json_path): with open(json_path, 'r') as f: return {'robustness_results': json.load(f)} return None def get_clean_metrics(model_name): """获取模型的 clean metrics""" model_key = model_name.lower().replace('-', '').replace('_', '') for key, metrics in PREDEFINED_CLEAN_METRICS.items(): if key in model_key or model_key in key: return metrics.copy() return {} def generate_corruption_table(results_dir, model_name): """生成按退化类型的详细表格""" data = load_model_results(results_dir, model_name) if data is None: print(f"ERROR: Could not load results from {results_dir}") return None robustness = data.get('robustness_results', data) corruption_avg = robustness.get('corruption_avg', {}) # 获取 P_clean p_clean = robustness.get('P', robustness.get('P_clean', {})) if not p_clean: p_clean = get_clean_metrics(model_name) rows = [] for corr in BENCHMARK_CORRUPTIONS: category = CORRUPTION_CATEGORIES.get(corr, 'unknown') corr_data = corruption_avg.get(corr, {}) row = { 'Corruption': corr, 'Category': category, } for metric in ['novel_ap50', 'base_ap50', 'all_ap50']: pc_c = corr_data.get(metric) p_clean_val = p_clean.get(metric) # PC_c (Performance under Corruption for this corruption type) row[f'{metric}_PC_c'] = round(pc_c, 2) if pc_c is not None else None # rPC(%) = PC_c / P_clean * 100 if pc_c is not None and p_clean_val is not None and p_clean_val > 0: rpc = (pc_c / p_clean_val) * 100 row[f'{metric}_rPC(%)'] = round(rpc, 2) else: row[f'{metric}_rPC(%)'] = None rows.append(row) return rows, p_clean def print_corruption_table(rows, model_name, p_clean): """打印退化类型表格""" print(f"\n{'='*120}") print(f"Corruption-level Performance: {model_name}") print(f"P_clean: novel_ap50={p_clean.get('novel_ap50')}, base_ap50={p_clean.get('base_ap50')}, all_ap50={p_clean.get('all_ap50')}") print(f"{'='*120}") # 表头 header = f"{'Corruption':<20} {'Category':<10}" for metric in ['novel_ap50', 'base_ap50', 'all_ap50']: header += f" {metric+'_PC_c':>14} {metric+'_rPC(%)':>14}" print(header) print("-" * 120) # 数据行 for row in rows: line = f"{row['Corruption']:<20} {row['Category']:<10}" for metric in ['novel_ap50', 'base_ap50', 'all_ap50']: pc_c = row.get(f'{metric}_PC_c') rpc = row.get(f'{metric}_rPC(%)') pc_c_str = f"{pc_c:.2f}" if pc_c is not None else "N/A" rpc_str = f"{rpc:.2f}" if rpc is not None else "N/A" line += f" {pc_c_str:>14} {rpc_str:>14}" print(line) print("=" * 120) def save_corruption_table_excel(all_tables, output_path): """保存所有模型的退化类型表格到 Excel""" if not HAS_PANDAS: print("ERROR: pandas not installed, cannot save Excel") return columns = ['Corruption', 'Category', 'novel_ap50_PC_c', 'novel_ap50_rPC(%)', 'base_ap50_PC_c', 'base_ap50_rPC(%)', 'all_ap50_PC_c', 'all_ap50_rPC(%)'] with pd.ExcelWriter(output_path, engine='openpyxl') as writer: for model_name, (rows, p_clean) in all_tables.items(): # 创建 DataFrame df = pd.DataFrame(rows) df = df[columns] # 计算 mPC mpc = {} for metric in ['novel_ap50', 'base_ap50', 'all_ap50']: values = [row.get(f'{metric}_PC_c') for row in rows if row.get(f'{metric}_PC_c') is not None] if values: mpc[metric] = sum(values) / len(values) # 计算整体 rPC rpc = {} for metric in ['novel_ap50', 'base_ap50', 'all_ap50']: if mpc.get(metric) and p_clean.get(metric): rpc[metric] = (mpc[metric] / p_clean[metric]) * 100 # 创建汇总行 (确保每行都有完整的列) summary_rows = [ # 空行 {col: None for col in columns}, # P_clean 行 { 'Corruption': 'P_clean', 'Category': None, 'novel_ap50_PC_c': p_clean.get('novel_ap50'), 'novel_ap50_rPC(%)': None, 'base_ap50_PC_c': p_clean.get('base_ap50'), 'base_ap50_rPC(%)': None, 'all_ap50_PC_c': p_clean.get('all_ap50'), 'all_ap50_rPC(%)': None, }, # mPC 行 { 'Corruption': 'mPC', 'Category': None, 'novel_ap50_PC_c': round(mpc.get('novel_ap50', 0), 2), 'novel_ap50_rPC(%)': round(rpc.get('novel_ap50', 0), 2), 'base_ap50_PC_c': round(mpc.get('base_ap50', 0), 2), 'base_ap50_rPC(%)': round(rpc.get('base_ap50', 0), 2), 'all_ap50_PC_c': round(mpc.get('all_ap50', 0), 2), 'all_ap50_rPC(%)': round(rpc.get('all_ap50', 0), 2), }, ] df_summary = pd.DataFrame(summary_rows, columns=columns) # 合并到主 DataFrame df_full = pd.concat([df, df_summary], ignore_index=True) df_full.to_excel(writer, sheet_name=model_name, index=False) print(f"Corruption table Excel saved to: {output_path}") def save_corruption_table_json(all_tables, output_path): """保存所有模型的退化类型表格到 JSON""" result = {} for model_name, (rows, p_clean) in all_tables.items(): result[model_name] = { 'P_clean': p_clean, 'corruption_details': rows } with open(output_path, 'w') as f: json.dump(result, f, indent=2) print(f"Corruption table JSON saved to: {output_path}") def main(): parser = argparse.ArgumentParser(description='Generate corruption-level performance table') parser.add_argument('--results-dirs', type=str, nargs='+', required=True, help='Directories containing model results') parser.add_argument('--model-names', type=str, nargs='+', default=None, help='Model names (default: infer from directory names)') parser.add_argument('--output-dir', type=str, default='.', help='Output directory') parser.add_argument('--output-prefix', type=str, default='corruption_table', help='Output file prefix') args = parser.parse_args() # 推断模型名称 if args.model_names is None: args.model_names = [os.path.basename(d.rstrip('/')) for d in args.results_dirs] if len(args.model_names) != len(args.results_dirs): print("ERROR: Number of model names must match number of results directories") return # 生成所有模型的表格 all_tables = {} for results_dir, model_name in zip(args.results_dirs, args.model_names): print(f"Generating table for {model_name} from {results_dir}...") result = generate_corruption_table(results_dir, model_name) if result: rows, p_clean = result all_tables[model_name] = (rows, p_clean) print_corruption_table(rows, model_name, p_clean) if not all_tables: print("ERROR: No valid results found!") return # 保存结果 os.makedirs(args.output_dir, exist_ok=True) excel_path = os.path.join(args.output_dir, f'{args.output_prefix}.xlsx') save_corruption_table_excel(all_tables, excel_path) json_path = os.path.join(args.output_dir, f'{args.output_prefix}.json') save_corruption_table_json(all_tables, json_path) if __name__ == '__main__': main()