class_docstr: 读取解析PASCAL VOC2007/2012数据集
class_docstr_tok: ["\u8bfb\u53d6\u89e3\u6790PASCAL","VOC2007\/2012\u6570\u636e\u96c6"]
code: def get_height_and_width(self, idx):
      # read xml
      xml_path = self.xml_list[idx]
      with open(xml_path) as fid:
          xml_str = fid.read()
      xml = etree.fromstring(xml_str)
      data = self.parse_xml_to_dict(xml)["annotation"]
      data_height = int(data["size"]["height"])
      data_width = int(data["size"]["width"])
      return data_height, data_width
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/retinaNet/my_dataset.py
imports: from torch.utils.data import Dataset
  import os
  import torch
  import json
  from PIL import Image
  from lxml import etree
label_desc: Use image node_id 4 for calling the VOCDataSet obj's underlying member method code with example usage: obj.get_height_and_width(idx) and returns: data_height,  data_width