class_docstr: Evaluate AR for object proposals, AP for instance detection/segmentation, AP
  for keypoint detection outputs using COCO's metrics.
  See http://cocodataset.org/#detection-eval and
  http://cocodataset.org/#keypoints-eval to understand its metrics.
  
  In addition to COCO, this evaluator is able to support any bounding box detection,
  instance segmentation, or keypoint detection dataset.
class_docstr_tok: ["Evaluate","AR","for","object","proposals",",","AP","for","instance","detection\/segmentation",",","AP","for","keypoint","detection","outputs","using","COCO","'s","metrics",".","See","http",":","\/\/cocodataset.org\/","#","detection-eval","and","http",":","\/\/cocodataset.org\/","#","keypoints-eval","to","understand","its","metrics",".","In","addition","to","COCO",",","this","evaluator","is","able","to","support","any","bounding","box","detection",",","instance","segmentation",",","or","keypoint","detection","dataset","."]
code: def reset(self):
      self._predictions = []
  
imports: import contextlib
  import copy
  import io
  import itertools
  import json
  import logging
  import os
  import pickle
  from collections import OrderedDict
  import numpy
  import torch
  import pandas
  from ..None import comm
  from ...data.registry.catalog import MetadataCatalog
  from ...data.registry.mscoco import convert_to_coco_json
  from ...data.structures import Boxes, BoxMode, pairwise_iou
  from ...data.detection.detection_utils import try_import_pycocotools
label_desc: Use image node_id 2 for calling the COCOEvaluator obj's underlying member method code with example usage: obj.reset() without return types