docstr: TODO: add other evaluation metrics for GQA
docstr_tok: ["TODO",":","add","other","evaluation","metrics","for","GQA"]
code: def _report_metrics(self, result_file, split):
      """
      TODO: add other evaluation metrics for GQA
      """
  
      results = json.load(open(result_file, "r"))
      acc = []
      vqa_tool = VQATool()
  
      for res in results:
          if res["gt_ans"] is None:
              # prepare test results for leaderboard evaluation
              self._save_result_leaderboard(results)
              return
  
          gt_ans = res["gt_ans"]
          pred = res["pred_ans"]
  
          if self.inference_method == "generate":
              pred = vqa_tool.processPunctuation(pred)
              pred = vqa_tool.processDigitArticle(pred)
  
          vqa_acc = 1 if pred == gt_ans else 0
  
          acc.append(vqa_acc)
  
      accuracy = sum(acc) / len(acc) * 100
      metrics = {"agg_metrics": accuracy, "acc": accuracy}
  
      with open(
          os.path.join(registry.get_path("output_dir"), "evaluate.txt"),
          "a",
      ) as f:
          f.write(json.dumps(metrics) + "\n")
  
      logging.info(metrics)
  
      return metrics
  
imports: import logging
  import json
  import os
  import torch
  import torch.distributed
  from itertools import chain
  import lavis.common.dist_utils
  from lavis.common.dist_utils import get_rank, get_world_size, is_main_process
  from lavis.common.registry import registry
  from lavis.common.vqa_tools.vqa_eval import VQAEval
  from lavis.tasks.vqa import VQATask
label_desc: Use image node_id 2 for calling the GQARCTask obj's underlying member method code with example usage: obj._report_metrics(result_file, split) and returns: metrics