class_docstr: Evaluate object proposal and instance detection/segmentation outputs using
  LVIS's metrics and evaluation API.
class_docstr_tok: ["Evaluate","object","proposal","and","instance","detection\/segmentation","outputs","using","LVIS","'s","metrics","and","evaluation","API","."]
code: def evaluate(self):
      if self._distributed:
          comm.synchronize()
          predictions = comm.gather(self._predictions, dst=0)
          predictions = list(itertools.chain(*predictions))
  
          if not comm.is_main_process():
              return
      else:
          predictions = self._predictions
  
      if len(predictions) == 0:
          self._logger.warning(
              "[LVISEvaluator] Did not receive valid predictions."
          )
          return {}
  
      if self._output_dir:
          PathManager.mkdirs(self._output_dir)
          file_path = os.path.join(
              self._output_dir, "instances_predictions.pth"
          )
          with PathManager.open(file_path, "wb") as f:
              torch.save(predictions, f)
  
      self._results = OrderedDict()
      if "proposals" in predictions[0]:
          self._eval_box_proposals(predictions)
      if "instances" in predictions[0]:
          self._eval_predictions(predictions)
      # Copy so the caller can do whatever with results
      return copy.deepcopy(self._results)
  
imports: import copy
  import itertools
  import json
  import logging
  import os
  import pickle
  from collections import OrderedDict
  import torch
  import detectron2.utils.comm
  from detectron2.config import CfgNode
  from detectron2.data import MetadataCatalog
  from detectron2.structures import Boxes, BoxMode, pairwise_iou
  from detectron2.utils.file_io import PathManager
  from detectron2.utils.logger import create_small_table
  from .coco_evaluation import instances_to_coco_json
  from .evaluator import DatasetEvaluator
label_desc: Use image node_id 4 for calling the LVISEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: copy, dict