diff --git a/configs/keypoint/README.md b/configs/keypoint/README.md index 3a3cb3720f6a48ad18e44836f988a0b53c0e0843..59f26f00371d7e5bb43a676b7c4041fcfd7075c0 100644 --- a/configs/keypoint/README.md +++ b/configs/keypoint/README.md @@ -75,6 +75,9 @@ CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/hig #MPII DataSet CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/hrnet/hrnet_w32_256x256_mpii.yml + +#当只需要保存评估预测的结果时，可以通过设置save_prediction_only参数实现，评估预测结果默认保存在output/keypoints_results.json文件中 +CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml --save_prediction_only ``` ​ **模型预测：** diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index 0121970ef156486bcf36239e7a65dc8193760699..c7e80fffbb8fd3a38eb980b36a31d1303d0afa6f 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -227,19 +227,27 @@ class Trainer(object): eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() + save_prediction_only = self.cfg.get('save_prediction_only', False) self._metrics = [ - KeyPointTopDownCOCOEval(anno_file, - len(eval_dataset), self.cfg.num_joints, - self.cfg.save_dir) + KeyPointTopDownCOCOEval( + anno_file, + len(eval_dataset), + self.cfg.num_joints, + self.cfg.save_dir, + save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'KeyPointTopDownMPIIEval': eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() + save_prediction_only = self.cfg.get('save_prediction_only', False) self._metrics = [ - KeyPointTopDownMPIIEval(anno_file, - len(eval_dataset), self.cfg.num_joints, - self.cfg.save_dir) + KeyPointTopDownMPIIEval( + anno_file, + len(eval_dataset), + self.cfg.num_joints, + self.cfg.save_dir, + save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'MOTDet': self._metrics = [JDEDetMetric(), ] diff --git a/ppdet/metrics/keypoint_metrics.py b/ppdet/metrics/keypoint_metrics.py index 150018c610af1514e96fc37a490590af27a26417..d8bc0e7821728f2a6b2578fc64611edce50ff8eb 100644 --- a/ppdet/metrics/keypoint_metrics.py +++ b/ppdet/metrics/keypoint_metrics.py @@ -20,6 +20,8 @@ from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from ..modeling.keypoint_utils import oks_nms from scipy.io import loadmat, savemat +from ppdet.utils.logger import setup_logger +logger = setup_logger(__name__) __all__ = ['KeyPointTopDownCOCOEval', 'KeyPointTopDownMPIIEval'] @@ -38,7 +40,8 @@ class KeyPointTopDownCOCOEval(object): output_eval, iou_type='keypoints', in_vis_thre=0.2, - oks_thre=0.9): + oks_thre=0.9, + save_prediction_only=False): super(KeyPointTopDownCOCOEval, self).__init__() self.coco = COCO(anno_file) self.num_samples = num_samples @@ -48,6 +51,7 @@ class KeyPointTopDownCOCOEval(object): self.oks_thre = oks_thre self.output_eval = output_eval self.res_file = os.path.join(output_eval, "keypoints_results.json") + self.save_prediction_only = save_prediction_only self.reset() def reset(self): @@ -90,6 +94,7 @@ class KeyPointTopDownCOCOEval(object): os.makedirs(self.output_eval) with open(self.res_file, 'w') as f: json.dump(results, f, sort_keys=True, indent=4) + logger.info(f'The keypoint result is saved to {self.res_file}.') try: json.load(open(self.res_file)) except Exception: @@ -178,6 +183,10 @@ class KeyPointTopDownCOCOEval(object): self.get_final_results(self.results['all_preds'], self.results['all_boxes'], self.results['image_path']) + if self.save_prediction_only: + logger.info(f'The keypoint result is saved to {self.res_file} ' + 'and do not evaluate the mAP.') + return coco_dt = self.coco.loadRes(self.res_file) coco_eval = COCOeval(self.coco, coco_dt, 'keypoints') coco_eval.params.useSegm = None @@ -191,6 +200,8 @@ class KeyPointTopDownCOCOEval(object): self.eval_results['keypoint'] = keypoint_stats def log(self): + if self.save_prediction_only: + return stats_names = [ 'AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)' @@ -213,9 +224,12 @@ class KeyPointTopDownMPIIEval(object): num_samples, num_joints, output_eval, - oks_thre=0.9): + oks_thre=0.9, + save_prediction_only=False): super(KeyPointTopDownMPIIEval, self).__init__() self.ann_file = anno_file + self.res_file = os.path.join(output_eval, "keypoints_results.json") + self.save_prediction_only = save_prediction_only self.reset() def reset(self): @@ -239,9 +253,32 @@ class KeyPointTopDownMPIIEval(object): self.results.append(results) def accumulate(self): + self._mpii_keypoint_results_save() + if self.save_prediction_only: + logger.info(f'The keypoint result is saved to {self.res_file} ' + 'and do not evaluate the mAP.') + return + self.eval_results = self.evaluate(self.results) + def _mpii_keypoint_results_save(self): + results = [] + for res in self.results: + if len(res) == 0: + continue + result = [{ + 'preds': res['preds'][k].tolist(), + 'boxes': res['boxes'][k].tolist(), + 'image_path': res['image_path'][k], + } for k in range(len(res))] + results.extend(result) + with open(self.res_file, 'w') as f: + json.dump(results, f, sort_keys=True, indent=4) + logger.info(f'The keypoint result is saved to {self.res_file}.') + def log(self): + if self.save_prediction_only: + return for item, value in self.eval_results.items(): print("{} : {}".format(item, value))