class_docstr: COCO keypoint detection dataset.
  
  Parameters
  ----------
  root : str, default '~/mxnet/datasets/coco'
      Path to folder storing the dataset.
  splits : list of str, default ['person_keypoints_val2017']
      Json annotations name.
      Candidates can be: person_keypoints_val2017, person_keypoints_train2017.
  check_centers : bool, default is False
      If true, will force check centers of bbox and keypoints, respectively.
      If centers are far away from each other, remove this label.
  skip_empty : bool, default is False
      Whether skip entire image if no valid label is found. Use `False` if this dataset is
      for validation to avoid COCO metric error.
code: def __init__(
      self,
      root=os.path.join("~", ".mxnet", "datasets", "coco"),
      splits=("person_keypoints_val2017",),
      check_centers=False,
      skip_empty=True,
  ):
      super(COCOKeyPoints, self).__init__(root)
      self._root = os.path.expanduser(root)
      if isinstance(splits, mx.base.string_types):
          splits = [splits]
      self._splits = splits
      self._coco = []
      self._check_centers = check_centers
      self._skip_empty = skip_empty
      self.index_map = dict(
          zip(type(self).CLASSES, range(self.num_class))
      )
      self.json_id_to_contiguous = None
      self.contiguous_id_to_json = None
      self._items, self._labels = self._load_jsons()
  
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import copy
  import numpy
  import mxnet
  from .utils import try_import_pycocotools
  from ..base import VisionDataset
  from ...utils.bbox import bbox_xywh_to_xyxy, bbox_clip_xyxy
label_desc: Use image node_id 1 to create a new COCOKeyPoints object from inherited base classes: VisionDataset with example: obj = COCOKeyPoints(root, splits, check_centers, skip_empty)