code: def summarize(
      self,
      prediction_file: str,
      verbose: bool = False,
  ):
      # get the predictions
      if os.path.isfile(prediction_file):
          predictions = [
              json.loads(line) for line in open(prediction_file)
          ]
      elif os.path.isdir(prediction_file):
          predictions = [
              json.loads(line)
              for pred_file in os.listdir(prediction_file)
              for line in open(os.path.join(prediction_file, pred_file))
          ]
      else:
          raise NotImplementedError("Not supported file format.")
  
      # sort the predictions based on 'image_id'
      predictions = sorted(predictions, key=lambda x: x["image_id"])
  
      predict_index = 0
  
      dataset2score = {
          "refcoco": {k: 0.0 for k in self.k},
          "refcoco+": {k: 0.0 for k in self.k},
          "refcocog": {k: 0.0 for k in self.k},
      }
      dataset2count = {"refcoco": 0.0, "refcoco+": 0.0, "refcocog": 0.0}
      for item_img, item_ann in tqdm(
          zip(self.refexp_gt["images"], self.refexp_gt["annotations"])
      ):
          # quit when evaluating all predictions
          if predict_index == len(predictions):
              break
  
          if item_img["id"] != item_ann["image_id"]:
              raise ValueError(
                  f"Ann\n{item_ann} \nis not matched\n {item_img}"
              )
  
          dataset_name = item_img["dataset_name"]
          img_height = item_img["height"]
          img_width = item_img["width"]
          caption = item_img["caption"]
          target_bbox = item_ann["bbox"]
          converted_bbox = [
              target_bbox[0],
              target_bbox[1],
              target_bbox[2] + target_bbox[0],
              target_bbox[3] + target_bbox[1],
          ]
          target_bbox = torch.as_tensor(converted_bbox).view(-1, 4)
  
          prediction = predictions[predict_index]["text"]
          try:
              entities, boxes = decode_bbox_from_caption(
                  prediction, img_width, img_height, verbose=verbose
              )
          except ValueError as e:
              entities, boxes = [], []
  
          predict_boxes = []
          for entity, box in zip(entities, boxes):
              if not are_phrases_similar(entity, caption):
                  if len(box) > 0:
                      predict_boxes.append(box)
              else:
                  predict_boxes.append(box)
  
          if len(predict_boxes) == 0:
              print(
                  f"Can't find valid bbox for the given phrase {caption}, \n{entities, boxes}"
              )
              print(f"We set a 0-area box to calculate result")
              predict_boxes = [[0.0, 0.0, 0.0, 0.0]]
  
          predict_boxes = (
              torch.as_tensor(predict_boxes)
              .view(-1, 4)
              .to(dtype=torch.float32)
          )
  
          iou, _ = box_iou(predict_boxes, target_bbox)
          mean_iou, _ = box_iou(
              predict_boxes.mean(0).view(-1, 4), target_bbox
          )
          for k in self.k:
              if k == "upper bound":
                  if max(iou) >= self.thresh_iou:
                      dataset2score[dataset_name][k] += 1.0
              elif k == "mean":
                  if max(mean_iou) >= self.thresh_iou:
                      dataset2score[dataset_name][k] += 1.0
              else:
                  if max(iou[0, :k]) >= self.thresh_iou:
                      dataset2score[dataset_name][k] += 1.0
  
          dataset2count[dataset_name] += 1.0
          predict_index += 1
  
      for key, value in dataset2score.items():
          for k in self.k:
              try:
                  value[k] /= dataset2count[key]
              except:
                  pass
  
      results = {}
      for key, value in dataset2score.items():
          results[key] = sorted([v for k, v in value.items()])
          print(
              f" Dataset: {key} - Precision @ 1, mean, all: {results[key]} \n"
          )
  
      return results
  
file_path: ml-ferret/ferret/eval/eval_refexp.py
imports: import os
  import copy
  from collections import defaultdict
  from pathlib import Path
  from tqdm import tqdm
  import torch
  import torch.utils.data
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  from prettytable import PrettyTable
  import re
  import json
  from misc.refcoco.box_ops import generalized_box_iou, box_iou
label_desc: Use image node_id 2 for calling the RefExpEvaluatorFromJsonl obj's underlying member method code with example usage: obj.summarize(prediction_file, verbose) and returns: results