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Source code for mmpose.datasets.datasets.top_down.topdown_aic_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import warnings

from mmcv import Config
from xtcocotools.cocoeval import COCOeval

from ...builder import DATASETS
from .topdown_coco_dataset import TopDownCocoDataset


[docs]@DATASETS.register_module() class TopDownAicDataset(TopDownCocoDataset): """AicDataset dataset for top-down pose estimation. "AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding", arXiv'2017. More details can be found in the `paper <https://arxiv.org/abs/1711.06475>`__ The dataset loads raw features and apply specified transforms to return a dict containing the image tensors and other information. AIC keypoint indexes:: 0: "right_shoulder", 1: "right_elbow", 2: "right_wrist", 3: "left_shoulder", 4: "left_elbow", 5: "left_wrist", 6: "right_hip", 7: "right_knee", 8: "right_ankle", 9: "left_hip", 10: "left_knee", 11: "left_ankle", 12: "head_top", 13: "neck" Args: ann_file (str): Path to the annotation file. img_prefix (str): Path to a directory where images are held. Default: None. data_cfg (dict): config pipeline (list[dict | callable]): A sequence of data transforms. dataset_info (DatasetInfo): A class containing all dataset info. test_mode (bool): Store True when building test or validation dataset. Default: False. """ def __init__(self, ann_file, img_prefix, data_cfg, pipeline, dataset_info=None, test_mode=False): if dataset_info is None: warnings.warn( 'dataset_info is missing. ' 'Check https://github.com/open-mmlab/mmpose/pull/663 ' 'for details.', DeprecationWarning) cfg = Config.fromfile('configs/_base_/datasets/aic.py') dataset_info = cfg._cfg_dict['dataset_info'] super(TopDownCocoDataset, self).__init__( ann_file, img_prefix, data_cfg, pipeline, dataset_info=dataset_info, test_mode=test_mode) self.use_gt_bbox = data_cfg['use_gt_bbox'] self.bbox_file = data_cfg['bbox_file'] self.det_bbox_thr = data_cfg.get('det_bbox_thr', 0.0) self.use_nms = data_cfg.get('use_nms', True) self.soft_nms = data_cfg['soft_nms'] self.nms_thr = data_cfg['nms_thr'] self.oks_thr = data_cfg['oks_thr'] self.vis_thr = data_cfg['vis_thr'] self.db = self._get_db() print(f'=> num_images: {self.num_images}') print(f'=> load {len(self.db)} samples') def _get_db(self): """Load dataset.""" assert self.use_gt_bbox gt_db = self._load_coco_keypoint_annotations() return gt_db def _do_python_keypoint_eval(self, res_file): """Keypoint evaluation using COCOAPI.""" coco_det = self.coco.loadRes(res_file) coco_eval = COCOeval( self.coco, coco_det, 'keypoints', self.sigmas, use_area=False) coco_eval.params.useSegm = None coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() stats_names = [ 'AP', 'AP .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)' ] info_str = list(zip(stats_names, coco_eval.stats)) return info_str
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