class_docstr: Load detection dataset.
  
  Args:
      dataset_dir (str): root directory for dataset.
      image_dir (str): directory for images.
      anno_path (str): annotation file path.
      data_fields (list): key name of data dictionary, at least have 'image'.
      sample_num (int): number of samples to load, -1 means all.
      use_default_label (bool): whether to load default label list.
      repeat (int): repeat times for dataset, use in benchmark.
class_docstr_tok: ["Load","detection","dataset",".","Args",":","dataset_dir","(","str",")",":","root","directory","for","dataset",".","image_dir","(","str",")",":","directory","for","images",".","anno_path","(","str",")",":","annotation","file","path",".","data_fields","(","list",")",":","key","name","of","data","dictionary",",","at","least","have","'image","'",".","sample_num","(","int",")",":","number","of","samples","to","load",",","-1","means","all",".","use_default_label","(","bool",")",":","whether","to","load","default","label","list",".","repeat","(","int",")",":","repeat","times","for","dataset",",","use","in","benchmark","."]
code: def get_anno(self):
      if self.anno_path is None:
          return
      return os.path.join(self.dataset_dir, self.anno_path)
  
imports: import os
  import copy
  import numpy
  from paddle.io import Dataset
  from ppdet.core.workspace import register, serializable
  from ppdet.utils.download import get_dataset_path
  from ppdet.data import source
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 10 for calling the DetDataset obj's underlying member method code with example usage: obj.get_anno() and returns: os