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","."]
docstr: Args:
      inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN).
          It is a list of dict. Each dict corresponds to an image and
          contains keys like "height", "width", "file_name", "image_id".
      outputs: the outputs of a LVIS model. It is a list of dicts with key
          "instances" that contains :class:`Instances`.
docstr_tok: ["Args",":","inputs",":","the","inputs","to","a","LVIS","model","(","e.g.",",","GeneralizedRCNN",")",".","It","is","a","list","of","dict",".","Each","dict","corresponds","to","an","image","and","contains","keys","like","``","height","''",",","``","width","''",",","``","file_name","''",",","``","image_id","''",".","outputs",":","the","outputs","of","a","LVIS","model",".","It","is","a","list","of","dicts","with","key","``","instances","''","that","contains",":","class",":","`","Instances","`","."]
code: def process(self, inputs, outputs):
      """
      Args:
          inputs: the inputs to a LVIS model (e.g., GeneralizedRCNN).
              It is a list of dict. Each dict corresponds to an image and
              contains keys like "height", "width", "file_name", "image_id".
          outputs: the outputs of a LVIS model. It is a list of dicts with key
              "instances" that contains :class:`Instances`.
      """
      for input, output in zip(inputs, outputs):
          prediction = {"image_id": input["image_id"]}
  
          if "instances" in output:
              instances = output["instances"].to(self._cpu_device)
              prediction["instances"] = instances_to_coco_json(
                  instances, input["image_id"]
              )
          if "proposals" in output:
              prediction["proposals"] = output["proposals"].to(
                  self._cpu_device
              )
          self._predictions.append(prediction)
  
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 3 for calling the LVISEvaluator obj's underlying member method code with example usage: obj.process(inputs, outputs) without return types