code: def accumulate(self):
      if len(self.results["bbox"]) > 0:
          output = "bbox.json"
          if self.output_eval:
              output = os.path.join(self.output_eval, output)
          with open(output, "w") as f:
              json.dump(self.results["bbox"], f)
              logger.info("The bbox result is saved to bbox.json.")
  
          if self.save_prediction_only:
              logger.info(
                  "The bbox result is saved to {} and do not "
                  "evaluate the mAP.".format(output)
              )
          else:
              bbox_stats = cocoapi_eval(
                  output,
                  "bbox",
                  anno_file=self.anno_file,
                  classwise=self.classwise,
              )
              self.eval_results["bbox"] = bbox_stats
              sys.stdout.flush()
  
      if len(self.results["mask"]) > 0:
          output = "mask.json"
          if self.output_eval:
              output = os.path.join(self.output_eval, output)
          with open(output, "w") as f:
              json.dump(self.results["mask"], f)
              logger.info("The mask result is saved to mask.json.")
  
          if self.save_prediction_only:
              logger.info(
                  "The mask result is saved to {} and do not "
                  "evaluate the mAP.".format(output)
              )
          else:
              seg_stats = cocoapi_eval(
                  output,
                  "segm",
                  anno_file=self.anno_file,
                  classwise=self.classwise,
              )
              self.eval_results["mask"] = seg_stats
              sys.stdout.flush()
  
      if len(self.results["segm"]) > 0:
          output = "segm.json"
          if self.output_eval:
              output = os.path.join(self.output_eval, output)
          with open(output, "w") as f:
              json.dump(self.results["segm"], f)
              logger.info("The segm result is saved to segm.json.")
  
          if self.save_prediction_only:
              logger.info(
                  "The segm result is saved to {} and do not "
                  "evaluate the mAP.".format(output)
              )
          else:
              seg_stats = cocoapi_eval(
                  output,
                  "segm",
                  anno_file=self.anno_file,
                  classwise=self.classwise,
              )
              self.eval_results["mask"] = seg_stats
              sys.stdout.flush()
  
      if len(self.results["keypoint"]) > 0:
          output = "keypoint.json"
          if self.output_eval:
              output = os.path.join(self.output_eval, output)
          with open(output, "w") as f:
              json.dump(self.results["keypoint"], f)
              logger.info(
                  "The keypoint result is saved to keypoint.json."
              )
  
          if self.save_prediction_only:
              logger.info(
                  "The keypoint result is saved to {} and do not "
                  "evaluate the mAP.".format(output)
              )
          else:
              style = "keypoints"
              use_area = True
              sigmas = COCO_SIGMAS
              if self.iou_type == "keypoints_crowd":
                  style = "keypoints_crowd"
                  use_area = False
                  sigmas = CROWD_SIGMAS
              keypoint_stats = cocoapi_eval(
                  output,
                  style,
                  anno_file=self.anno_file,
                  classwise=self.classwise,
                  sigmas=sigmas,
                  use_area=use_area,
              )
              self.eval_results["keypoint"] = keypoint_stats
              sys.stdout.flush()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import json
  import paddle
  import numpy
  import typing
  from collections import defaultdict
  from pathlib import Path
  from .map_utils import prune_zero_padding, DetectionMAP
  from .coco_utils import get_infer_results, cocoapi_eval
  from .widerface_utils import face_eval_run
  from ppdet.data.source.category import get_categories
  from ppdet.modeling.rbox_utils import poly2rbox_np
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 4 for calling the COCOMetric obj's underlying member method code with example usage: obj.accumulate() without return types