class_docstr: COCO dataset for top-down pose estimation. 
  
  The dataset loads raw features and apply specified transforms
  to return a dict containing the image tensors and other information.
  
  COCO keypoint indexes:
  
      0: 'nose',
      1: 'left_eye',
      2: 'right_eye',
      3: 'left_ear',
      4: 'right_ear',
      5: 'left_shoulder',
      6: 'right_shoulder',
      7: 'left_elbow',
      8: 'right_elbow',
      9: 'left_wrist',
      10: 'right_wrist',
      11: 'left_hip',
      12: 'right_hip',
      13: 'left_knee',
      14: 'right_knee',
      15: 'left_ankle',
      16: 'right_ankle'
  
  Args:
      dataset_dir (str): Root path to the dataset.
      image_dir (str): Path to a directory where images are held.
      anno_path (str): Relative path to the annotation file.
      num_joints (int): Keypoint numbers
      trainsize (list):[w, h] Image target size
      transform (composed(operators)): A sequence of data transforms.
      bbox_file (str): Path to a detection bbox file
          Default: None.
      use_gt_bbox (bool): Whether to use ground truth bbox
          Default: True.
      pixel_std (int): The pixel std of the scale
          Default: 200.
      image_thre (float): The threshold to filter the detection box
          Default: 0.0.
code: def __init__(
      self,
      dataset_dir,
      image_dir,
      anno_path,
      num_joints,
      trainsize,
      transform=[],
      bbox_file=None,
      use_gt_bbox=True,
      pixel_std=200,
      image_thre=0.0,
      center_scale=None,
  ):
      super().__init__(
          dataset_dir, image_dir, anno_path, num_joints, transform
      )
  
      self.bbox_file = bbox_file
      self.use_gt_bbox = use_gt_bbox
      self.trainsize = trainsize
      self.pixel_std = pixel_std
      self.image_thre = image_thre
      self.center_scale = center_scale
      self.dataset_name = "coco"
  
imports: import os
  import cv2
  import numpy
  import json
  import copy
  import pycocotools
  from pycocotools.coco import COCO
  from .dataset import DetDataset
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 1 to create a new KeypointTopDownCocoDataset object from inherited base classes: KeypointTopDownBaseDataset with example: obj = KeypointTopDownCocoDataset(dataset_dir, image_dir, anno_path, num_joints, trainsize, transform, bbox_file, use_gt_bbox, pixel_std, image_thre, center_scale)