#!/usr/bin/env python3 """ 比较多个模型的 OV-COCO 鲁棒性结果 生成格式: ClearCLIP CLIPSelf P_clean mPC rPC (%) P_clean mPC rPC (%) novel_ap50 26.74 13.84 51.76 37.51 29.27 78.05 base_ap50 44.00 26.21 59.57 54.94 40.81 74.27 all_ap50 39.49 22.97 58.17 50.38 37.79 75.01 """ 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") # 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, 'bbox_mAP': 20.30, 'bbox_mAP_50': 39.50, }, 'clipself': { 'base_ap50': 54.94, 'novel_ap50': 37.51, 'all_ap50': 50.38, 'bbox_mAP': 27.70, 'bbox_mAP_50': 50.00, } } def load_model_results(results_dir, model_name): """加载单个模型的结果""" # 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 json.load(f) # 尝试加载 pkl 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) robustness = data.get('robustness_results', {}) return { 'model': model_name, 'P_clean': robustness.get('P', {}), 'mPC': robustness.get('mPC', {}), 'rPC': {k: v * 100 for k, v in robustness.get('rPC', {}).items()}, 'category_mPC': robustness.get('category_mPC', {}) } return None def print_comparison_table(models_data): """打印多模型比较表格""" model_names = list(models_data.keys()) metrics = ['novel_ap50', 'base_ap50', 'all_ap50'] # 计算列宽 col_width = 10 # 打印表头 print("\n" + "=" * 80) print("OV-COCO Robustness Comparison") print("=" * 80) # 打印模型名称行 print(f"{'Metric':<12}", end="") for model in model_names: print(f" | {model:^{col_width * 3 + 4}}", end="") print() # 打印子表头 print(f"{'':12}", end="") for _ in model_names: print(f" | {'P_clean':>{col_width}} {'mPC':>{col_width}} {'rPC(%)':>{col_width}}", end="") print() print("-" * (12 + (col_width * 3 + 5) * len(model_names))) # 打印数据行 for metric in metrics: print(f"{metric:<12}", end="") for model in model_names: data = models_data[model] p_val = data.get('P_clean', {}).get(metric, None) mpc_val = data.get('mPC', {}).get(metric, None) rpc_val = data.get('rPC', {}).get(metric, None) p_str = f"{p_val:.2f}" if p_val is not None else "N/A" mpc_str = f"{mpc_val:.2f}" if mpc_val is not None else "N/A" rpc_str = f"{rpc_val:.2f}" if rpc_val is not None else "N/A" print(f" | {p_str:>{col_width}} {mpc_str:>{col_width}} {rpc_str:>{col_width}}", end="") print() print("=" * 80) def save_comparison_excel(models_data, output_path): """保存多模型比较到 Excel""" if not HAS_PANDAS: print("ERROR: pandas not installed, cannot save Excel") return model_names = list(models_data.keys()) metrics = ['novel_ap50', 'base_ap50', 'all_ap50', 'bbox_mAP', 'bbox_mAP_50'] with pd.ExcelWriter(output_path, engine='openpyxl') as writer: # Sheet 1: Core Comparison rows = [] for metric in metrics[:3]: # 只取核心指标 row = {'Metric': metric} for model in model_names: data = models_data[model] row[f'{model}_P_clean'] = data.get('P_clean', {}).get(metric) row[f'{model}_mPC'] = data.get('mPC', {}).get(metric) row[f'{model}_rPC(%)'] = data.get('rPC', {}).get(metric) rows.append(row) df_core = pd.DataFrame(rows) df_core.to_excel(writer, sheet_name='Core Comparison', index=False) # Sheet 2: Extended Comparison rows = [] for metric in metrics: row = {'Metric': metric} for model in model_names: data = models_data[model] row[f'{model}_P_clean'] = data.get('P_clean', {}).get(metric) row[f'{model}_mPC'] = data.get('mPC', {}).get(metric) row[f'{model}_rPC(%)'] = data.get('rPC', {}).get(metric) rows.append(row) df_ext = pd.DataFrame(rows) df_ext.to_excel(writer, sheet_name='Extended Comparison', index=False) # Sheet 3: Category Comparison categories = ['noise', 'blur', 'weather', 'digital'] for metric in ['base_ap50', 'novel_ap50', 'all_ap50']: rows = [] for cat in categories: row = {'Category': cat} for model in model_names: data = models_data[model] val = data.get('category_mPC', {}).get(cat, {}).get(metric) row[model] = round(val, 2) if val is not None else None rows.append(row) df_cat = pd.DataFrame(rows) df_cat.to_excel(writer, sheet_name=f'Category {metric}', index=False) print(f"Comparison Excel saved to: {output_path}") def save_comparison_json(models_data, output_path): """保存比较结果到 JSON""" with open(output_path, 'w') as f: json.dump(models_data, f, indent=2) print(f"Comparison JSON saved to: {output_path}") def main(): parser = argparse.ArgumentParser(description='Compare OV-COCO robustness results across models') 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 for comparison files') parser.add_argument('--output-prefix', type=str, default='robustness_comparison', 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 # 加载所有模型结果 models_data = {} for results_dir, model_name in zip(args.results_dirs, args.model_names): print(f"Loading results for {model_name} from {results_dir}...") data = load_model_results(results_dir, model_name) if data: models_data[model_name] = data else: print(f"Warning: Could not load results for {model_name}") if not models_data: print("ERROR: No valid results found!") return # 打印比较表格 print_comparison_table(models_data) # 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_comparison_excel(models_data, excel_path) json_path = os.path.join(args.output_dir, f'{args.output_prefix}.json') save_comparison_json(models_data, json_path) if __name__ == '__main__': main()