code: def __init__(self, data_path, image_path, args) -> None:
      obj_list = [json.loads(line) for line in tqdm(open(data_path))]
      # question_prompt = "Is the object in <location> of the image a <obj1> or a <obj2>?"
      question_prompt = (
          "Is the object <location> of the image a <obj1> or a <obj2>?"
      )
  
      for idx, i in enumerate(obj_list):
          i["image_path"] = os.path.join(
              image_path, i["image_path"].split("/")[-1]
          )
          ratio_w = VOCAB_IMAGE_W * 1.0 / i["width"]
          ratio_h = VOCAB_IMAGE_H * 1.0 / i["height"]
  
          point_x_textvocab = int(i["sample_point"][0] * ratio_w)
          point_y_textvocab = int(i["sample_point"][1] * ratio_h)
  
          box_x1 = int(i["bbox_norm"][0] * i["width"])
          box_y1 = int(i["bbox_norm"][1] * i["height"])
          box_x2 = int(i["bbox_norm"][2] * i["width"])
          box_y2 = int(i["bbox_norm"][3] * i["height"])
  
          box_x1_textvocab = int(i["bbox_norm"][0] * VOCAB_IMAGE_W)
          box_y1_textvocab = int(i["bbox_norm"][1] * VOCAB_IMAGE_H)
          box_x2_textvocab = int(i["bbox_norm"][2] * VOCAB_IMAGE_W)
          box_y2_textvocab = int(i["bbox_norm"][3] * VOCAB_IMAGE_H)
  
          if args.region_format == "point":
              region_coordinate_raw = [
                  i["sample_point"][0],
                  i["sample_point"][1],
              ]
              if args.no_coor:
                  assert args.add_region_feature
                  i["question"] = question_prompt.replace(
                      "<location>", ""
                  )
              else:
                  i["question"] = question_prompt.replace(
                      "<location>",
                      "[{}, {}]".format(
                          point_x_textvocab, point_y_textvocab
                      ),
                  )
              segment_mask = None
  
          elif (
              args.region_format == "box"
              or args.region_format == "free_shape"
          ):
              if args.region_format == "free_shape":
                  region_coordinate_raw = i["free_shape_bbox_raw"]
                  box_x1_textvocab = int(
                      i["free_shape_bbox_raw"][0] * ratio_w
                  )
                  box_y1_textvocab = int(
                      i["free_shape_bbox_raw"][1] * ratio_h
                  )
                  box_x2_textvocab = int(
                      i["free_shape_bbox_raw"][2] * ratio_w
                  )
                  box_y2_textvocab = int(
                      i["free_shape_bbox_raw"][3] * ratio_h
                  )
              else:
                  region_coordinate_raw = [
                      box_x1,
                      box_y1,
                      box_x2,
                      box_y2,
                  ]
  
              if args.no_coor:
                  assert args.add_region_feature
                  i["question"] = question_prompt.replace(
                      "<location>", ""
                  )
              else:
                  i["question"] = question_prompt.replace(
                      "<location>",
                      "[{}, {}, {}, {}]".format(
                          box_x1_textvocab,
                          box_y1_textvocab,
                          box_x2_textvocab,
                          box_y2_textvocab,
                      ),
                  )
  
              if args.region_format == "free_shape":
                  segment_mask = np.array(i["free_shape_segment_mask"])
              else:
                  segment_mask = None
          else:
              raise NotImplementedError(
                  f"{args.region_format} is not supported."
              )
  
          if args.add_region_feature:
              i["question"] = i["question"].replace(
                  "of the image",
                  f"{DEFAULT_REGION_FEA_TOKEN} of the image",
              )
              generated_mask = generate_mask_for_feature(
                  region_coordinate_raw,
                  raw_w=i["width"],
                  raw_h=i["height"],
                  mask=segment_mask,
              )
              i["region_masks"] = [generated_mask]
          else:
              i["region_masks"] = None
  
          if idx % 2 == 0:
              i["question"] = i["question"].replace("<obj1>", i["name"])
              i["question"] = i["question"].replace(
                  "<obj2>", i["neg_class"]
              )
          else:
              i["question"] = i["question"].replace("<obj2>", i["name"])
              i["question"] = i["question"].replace(
                  "<obj1>", i["neg_class"]
              )
  
      self.obj_list = obj_list
      self._ids = range(len(self.obj_list))
  
imports: import argparse
  import torch
  import os
  import json
  from tqdm import tqdm
  from ferret.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
  from ferret.model.builder import load_pretrained_model
  from ferret.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
  from ferret.conversation import conv_templates, SeparatorStyle
  from ferret.utils import disable_torch_init
  from PIL import Image
  import math
  import pdb
  import numpy
  from copy import deepcopy
  from functools import partial
label_desc: Use image node_id 1 to create a new LVISData_V1 object  with example: obj = LVISData_V1(data_path, image_path, args)