code: def evaluate(
      self,
      annotation_file: str,
      prediction_file: str,
      verbose: bool = False,
  ):
      recall_tracker = RecallTracker(self.topk)
  
      gt_json = json.load(open(annotation_file, "r", encoding="utf-8"))
  
      # 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 sorted(os.listdir(prediction_file))
              for line in open(os.path.join(prediction_file, pred_file))
          ]
      else:
          raise NotImplementedError("Not supported file format.")
  
      predict_index = 0
  
      valid_cnt = 0
      for item in tqdm(gt_json["images"]):
          file_name = item["file_name"]
          caption = item["caption"]
          img_height = float(item["height"])
          img_width = float(item["width"])
          postive_item_pos = item["tokens_positive_eval"]
  
          # to verify
          phrases_from_self = self.imgid2sentences[
              str(item["original_img_id"])
          ][int(item["sentence_id"])]
          for pos in postive_item_pos:
              # pdb.set_trace()
              if predict_index == len(predictions):
                  break
  
              pos_start, pos_end = pos[0]
              phrase = caption[pos_start:pos_end]
              phrase_from_self = [
                  p for p in phrases_from_self if p["phrase"] == phrase
              ]
              if len(phrase_from_self) == 0:
                  raise ValueError(
                      f"Can't find the corresponding gt from two file {phrase} vs. {phrases_from_self}"
                  )
              else:
                  phrase_from_self = phrase_from_self[0]
  
              # get the prediction from text line
              try:
                  prediction = predictions[predict_index]["text"]
              except IndexError as e:
                  print("Raise Indexerror.")
                  print(
                      f"prediction index / length: {predict_index} / {len(predictions)}"
                  )
                  import sys
  
                  sys.exit(0)
              try:
                  entities, boxes = decode_bbox_from_caption(
                      prediction, img_width, img_height, verbose=verbose
                  )
                  assert len(entities) == len(boxes)
              except ValueError as e:
                  entities, boxes = [], []
  
              predict_boxes = []
  
              for entity, box in zip(entities, boxes):
                  if not are_phrases_similar(
                      entity, phrase
                  ):  # get the matched noun phrase
                      # print(f"{entity} | {phrase}")
                      continue
                  else:
                      predict_boxes.append(box)
  
              if len(predict_boxes) == 0:
                  print(
                      f"Can't find valid bbox for the given phrase ({phrase}) in caption ({caption}), \n{prediction}"
                  )
                  print(
                      f"We set a 0-area box to calculate recall result"
                  )
                  predict_boxes = [[0.0, 0.0, 0.0, 0.0]]
                  # exit(0)
  
              # evaluate
              target_boxes = self.imgid2boxes[
                  str(item["original_img_id"])
              ][phrase_from_self["phrase_id"]]
              ious = box_iou(
                  np.asarray(predict_boxes), np.asarray(target_boxes)
              )
              for k in self.topk:
                  maxi = 0
                  if k == -1:
                      maxi = ious.max()
                  else:
                      assert k > 0
                      maxi = ious[:k].max()
                  if maxi >= self.iou_thresh:
                      recall_tracker.add_positive(k, "all")
                      for phrase_type in phrase_from_self[
                          "phrase_type"
                      ]:
                          recall_tracker.add_positive(k, phrase_type)
                  else:
                      recall_tracker.add_negative(k, "all")
                      for phrase_type in phrase_from_self[
                          "phrase_type"
                      ]:
                          recall_tracker.add_negative(k, phrase_type)
  
              # pdb.set_trace()
              valid_cnt += 1
          predict_index += 1
  
      print(f"Valid prediction {valid_cnt}/{len(predictions)}")
      self.results = recall_tracker.report()
      return self.results
  
imports: import xml.etree.ElementTree
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  from prettytable import PrettyTable
  from tqdm import tqdm
  import json
  import os
  import re
label_desc: Use image node_id 1 for calling the Flickr30kEntitiesRecallEvaluatorFromJsonl obj's underlying member method code with example usage: obj.evaluate(annotation_file, prediction_file, verbose) and returns: self