#!/usr/bin/env python3 # Copyright (c) OpenMMLab. All rights reserved. # Modified for OV-COCO dataset to compute base_ap50 and novel_ap50 """ OV-COCO 鲁棒性测试脚本 与标准 test_robustness.py 的区别: 1. 使用 dataset.evaluate() 替代 coco_eval_with_return() 2. 这样 CocoDatasetOV 的 evaluate_det_segm() 会计算 base_ap50/novel_ap50/all_ap50 """ import argparse import copy import os import os.path as osp import sys import mmcv import torch from mmcv import DictAction from mmcv.parallel import MMDataParallel from mmcv.runner import load_checkpoint, wrap_fp16_model from mmdet.apis import set_random_seed, single_gpu_test from mmdet.datasets import build_dataloader, build_dataset from mmdet.models import build_detector def parse_args(): parser = argparse.ArgumentParser(description='OV-COCO Robustness Test') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') parser.add_argument('--out', help='output result file') parser.add_argument( '--corruptions', type=str, nargs='+', default='benchmark', choices=[ 'all', 'benchmark', 'noise', 'blur', 'weather', 'digital', 'holdout', 'None', 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter', 'saturate' ], help='corruptions') parser.add_argument( '--severities', type=int, nargs='+', default=[1, 2, 3, 4, 5], help='corruption severity levels') parser.add_argument( '--eval', type=str, nargs='+', default=['bbox'], choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'], help='eval types') parser.add_argument( '--workers', type=int, default=8, help='workers per gpu') parser.add_argument('--show', action='store_true', help='show results') parser.add_argument( '--show-dir', help='directory where painted images will be saved') parser.add_argument( '--show-score-thr', type=float, default=0.3, help='score threshold (default: 0.3)') parser.add_argument('--seed', type=int, default=None, help='random seed') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config') args = parser.parse_args() return args def get_corruption_list(corruptions_arg): """解析 corruption 参数，返回实际的 corruption 列表""" if 'all' in corruptions_arg: return [ 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter', 'saturate' ] elif 'benchmark' in corruptions_arg: return [ 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' ] elif 'noise' in corruptions_arg: return ['gaussian_noise', 'shot_noise', 'impulse_noise'] elif 'blur' in corruptions_arg: return ['defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur'] elif 'weather' in corruptions_arg: return ['snow', 'frost', 'fog', 'brightness'] elif 'digital' in corruptions_arg: return ['contrast', 'elastic_transform', 'pixelate', 'jpeg_compression'] elif 'holdout' in corruptions_arg: return ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate'] elif 'None' in corruptions_arg: return ['None'] else: return corruptions_arg def main(): args = parse_args() assert args.out, 'Please specify output file with --out' if not args.out.endswith(('.pkl', '.pickle')): raise ValueError('The output file must be a pkl file.') cfg = mmcv.Config.fromfile(args.config) if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None cfg.data.test.test_mode = True if args.workers == 0: args.workers = cfg.data.workers_per_gpu # set random seeds if args.seed is not None: set_random_seed(args.seed) corruptions = get_corruption_list(args.corruptions) # 如果是 None corruption，只测试 severity 0 if corruptions == ['None']: args.severities = [0] aggregated_results = {} for corruption in corruptions: aggregated_results[corruption] = {} for severity in args.severities: print(f'\nTesting {corruption} at severity {severity}') # 复制测试数据配置 test_data_cfg = copy.deepcopy(cfg.data.test) # 添加 corruption transform if severity > 0: corruption_trans = dict( type='Corrupt', corruption=corruption, severity=severity) # 在 LoadImageFromFile 之后插入 test_data_cfg['pipeline'].insert(1, corruption_trans) # 构建数据集 (使用 CocoDatasetOV) dataset = build_dataset(test_data_cfg) data_loader = build_dataloader( dataset, samples_per_gpu=1, workers_per_gpu=args.workers, dist=False, shuffle=False) # 构建模型 cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) fp16_cfg = cfg.get('fp16', None) if fp16_cfg is not None: wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') # 设置类别 if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES model = MMDataParallel(model, device_ids=[0]) # 运行推理 show_dir = args.show_dir if show_dir is not None: show_dir = osp.join(show_dir, corruption, str(severity)) if not osp.exists(show_dir): os.makedirs(show_dir) outputs = single_gpu_test( model, data_loader, args.show, show_dir, args.show_score_thr) # 保存原始输出 mmcv.dump(outputs, args.out) # 使用 dataset.evaluate() 计算指标 # 关键: classwise=True 才能计算 base_ap50 和 novel_ap50 eval_results = dataset.evaluate( outputs, metric=args.eval, classwise=True, logger=None ) # 打印结果 print(f'\nResults for {corruption} at severity {severity}:') for key, value in eval_results.items(): if isinstance(value, float): print(f' {key}: {value:.4f}') else: print(f' {key}: {value}') # 存储结果 aggregated_results[corruption][severity] = eval_results # 清理 GPU 内存 del model del data_loader del dataset torch.cuda.empty_cache() # 保存聚合结果 eval_results_filename = ( osp.splitext(args.out)[0] + '_results' + osp.splitext(args.out)[1]) mmcv.dump(aggregated_results, eval_results_filename) print(f'\nAggregated results saved to: {eval_results_filename}') # 打印汇总 print_summary(aggregated_results) def print_summary(aggregated_results): """打印结果汇总""" print('\n' + '=' * 80) print('Summary of OV-COCO Robustness Evaluation') print('=' * 80) # 收集所有指标 all_base_ap50 = [] all_novel_ap50 = [] all_all_ap50 = [] all_bbox_mAP = [] all_bbox_mAP_50 = [] print(f'\n{"Corruption":<25} {"Sev":>4} {"Base AP50":>10} {"Novel AP50":>11} {"All AP50":>10} {"mAP":>8} {"mAP50":>8}') print('-' * 80) for corruption in aggregated_results: for severity in sorted(aggregated_results[corruption].keys()): results = aggregated_results[corruption][severity] base_ap50 = results.get('base_ap50', float('nan')) novel_ap50 = results.get('novel_ap50', float('nan')) all_ap50 = results.get('all_ap50', float('nan')) bbox_mAP = results.get('bbox_mAP', float('nan')) bbox_mAP_50 = results.get('bbox_mAP_50', float('nan')) print(f'{corruption:<25} {severity:>4} {base_ap50:>10.2f} {novel_ap50:>11.2f} {all_ap50:>10.2f} {bbox_mAP:>8.3f} {bbox_mAP_50:>8.3f}') if severity > 0: # 不包含 clean data all_base_ap50.append(base_ap50) all_novel_ap50.append(novel_ap50) all_all_ap50.append(all_ap50) all_bbox_mAP.append(bbox_mAP) all_bbox_mAP_50.append(bbox_mAP_50) # 计算 mPC import numpy as np print('-' * 80) print(f'{"mPC (Mean)":<25} {"":>4} {np.nanmean(all_base_ap50):>10.2f} {np.nanmean(all_novel_ap50):>11.2f} {np.nanmean(all_all_ap50):>10.2f} {np.nanmean(all_bbox_mAP):>8.3f} {np.nanmean(all_bbox_mAP_50):>8.3f}') print('=' * 80) if __name__ == '__main__': main()