code: import json
  import tempfile
  
  import numpy as np
  import copy
  import time
  import torch
  import torch._six
  
  from pycocotools.cocoeval import COCOeval
  from pycocotools.coco import COCO
  import pycocotools.mask as mask_util
  
  from collections import defaultdict
  
  import helpers.utils as utils
  
  
  class CocoEvaluator(object):
      def __init__(self, coco_gt, iou_types):
          assert isinstance(iou_types, (list, tuple))
          coco_gt = copy.deepcopy(coco_gt)
          self.coco_gt = coco_gt
  
          self.iou_types = iou_types
          self.coco_eval = {}
          for iou_type in iou_types:
              self.coco_eval[iou_type] = COCOeval(coco_gt, iouType=iou_type)
  
          self.img_ids = []
          self.eval_imgs = {k: [] for k in iou_types}
  
      def update(self, predictions):
          img_ids = list(np.unique(list(predictions.keys())))
          self.img_ids.extend(img_ids)
  
          for iou_type in self.iou_types:
              results = self.prepare(predictions, iou_type)
              coco_dt = loadRes(self.coco_gt, results) if results else COCO()
              coco_eval = self.coco_eval[iou_type]
  
              coco_eval.cocoDt = coco_dt
              coco_eval.params.imgIds = list(img_ids)
              img_ids, eval_imgs = evaluate(coco_eval)
  
              self.eval_imgs[iou_type].append(eval_imgs)
  
      def synchronize_between_processes(self):
          for iou_type in self.iou_types:
              self.eval_imgs[iou_type] = np.concatenate(self.eval_imgs[iou_type], 2)
              create_common_coco_eval(self.coco_eval[iou_type], self.img_ids, self.eval_imgs[iou_type])
  
      def accumulate(self):
          for coco_eval in self.coco_eval.values():
              coco_eval.accumulate()
  
      def summarize(self):
          for iou_type, coco_eval in self.coco_eval.items():
              print("IoU metric: {}".format(iou_type))
              coco_eval.summarize()
  
      def prepare(self, predictions, iou_type):
          if iou_type == "bbox":
              return self.prepare_for_coco_detection(predictions)
          elif iou_type == "segm":
              return self.prepare_for_coco_segmentation(predictions)
          elif iou_type == "keypoints":
              return self.prepare_for_coco_keypoint(predictions)
          else:
              raise ValueError("Unknown iou type {}".format(iou_type))
  
      def prepare_for_coco_detection(self, predictions):
          coco_results = []
          for original_id, prediction in predictions.items():
              if len(prediction) == 0:
                  continue
  
              boxes = prediction["boxes"]
              boxes = convert_to_xywh(boxes).tolist()
              scores = prediction["scores"].tolist()
              labels = prediction["labels"].tolist()
  
              coco_results.extend(
                  [
                      {
                          "image_id": original_id,
                          "category_id": labels[k],
                          "bbox": box,
                          "score": scores[k],
                      }
                      for k, box in enumerate(boxes)
                  ]
              )
          return coco_results
  
      def prepare_for_coco_segmentation(self, predictions):
          coco_results = []
          for original_id, prediction in predictions.items():
              if len(prediction) == 0:
                  continue
  
              scores = prediction["scores"]
              labels = prediction["labels"]
              masks = prediction["masks"]
  
              masks = masks > 0.5
  
              scores = prediction["scores"].tolist()
              labels = prediction["labels"].tolist()
  
              rles = [
                  mask_util.encode(np.array(mask[0, :, :, np.newaxis], dtype=np.uint8, order="F"))[0]
                  for mask in masks
              ]
              for rle in rles:
                  rle["counts"] = rle["counts"].decode("utf-8")
  
              coco_results.extend(
                  [
                      {
                          "image_id": original_id,
                          "category_id": labels[k],
                          "segmentation": rle,
                          "score": scores[k],
                      }
                      for k, rle in enumerate(rles)
                  ]
              )
          return coco_results
  
      def prepare_for_coco_keypoint(self, predictions):
          coco_results = []
          for original_id, prediction in predictions.items():
              if len(prediction) == 0:
                  continue
  
              boxes = prediction["boxes"]
              boxes = convert_to_xywh(boxes).tolist()
              scores = prediction["scores"].tolist()
              labels = prediction["labels"].tolist()
              keypoints = prediction["keypoints"]
              keypoints = keypoints.flatten(start_dim=1).tolist()
  
              coco_results.extend(
                  [
                      {
                          "image_id": original_id,
                          "category_id": labels[k],
                          'keypoints': keypoint,
                          "score": scores[k],
                      }
                      for k, keypoint in enumerate(keypoints)
                  ]
              )
          return coco_results
  
  
  def convert_to_xywh(boxes):
      xmin, ymin, xmax, ymax = boxes.unbind(1)
      return torch.stack((xmin, ymin, xmax - xmin, ymax - ymin), dim=1)
  
  
  def merge(img_ids, eval_imgs):
      all_img_ids = utils.all_gather(img_ids)
      all_eval_imgs = utils.all_gather(eval_imgs)
  
      merged_img_ids = []
      for p in all_img_ids:
          merged_img_ids.extend(p)
  
      merged_eval_imgs = []
      for p in all_eval_imgs:
          merged_eval_imgs.append(p)
  
      merged_img_ids = np.array(merged_img_ids)
      merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
  
      # keep only unique (and in sorted order) images
      merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
      merged_eval_imgs = merged_eval_imgs[..., idx]
  
      return merged_img_ids, merged_eval_imgs
  
  
  def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
      img_ids, eval_imgs = merge(img_ids, eval_imgs)
      img_ids = list(img_ids)
      eval_imgs = list(eval_imgs.flatten())
  
      coco_eval.evalImgs = eval_imgs
      coco_eval.params.imgIds = img_ids
      coco_eval._paramsEval = copy.deepcopy(coco_eval.params)
  
  
  #################################################################
  # From pycocotools, just removed the prints and fixed
  # a Python3 bug about unicode not defined
  #################################################################
  
  # Ideally, pycocotools wouldn't have hard-coded prints
  # so that we could avoid copy-pasting those two functions
  
  def createIndex(self):
      # create index
      # print('creating index...')
      anns, cats, imgs = {}, {}, {}
      imgToAnns, catToImgs = defaultdict(list), defaultdict(list)
      if 'annotations' in self.dataset:
          for ann in self.dataset['annotations']:
              imgToAnns[ann['image_id']].append(ann)
              anns[ann['id']] = ann
  
      if 'images' in self.dataset:
          for img in self.dataset['images']:
              imgs[img['id']] = img
  
      if 'categories' in self.dataset:
          for cat in self.dataset['categories']:
              cats[cat['id']] = cat
  
      if 'annotations' in self.dataset and 'categories' in self.dataset:
          for ann in self.dataset['annotations']:
              catToImgs[ann['category_id']].append(ann['image_id'])
  
      # print('index created!')
  
      # create class members
      self.anns = anns
      self.imgToAnns = imgToAnns
      self.catToImgs = catToImgs
      self.imgs = imgs
      self.cats = cats
  
  
  maskUtils = mask_util
  
  
  def loadRes(self, resFile):
      """
      Load result file and return a result api object.
      Args:
          self (obj): coco object with ground truth annotations
          resFile (str): file name of result file
      Returns:
      res (obj): result api object
      """
      res = COCO()
      res.dataset['images'] = [img for img in self.dataset['images']]
  
      # print('Loading and preparing results...')
      # tic = time.time()
      if isinstance(resFile, torch._six.string_classes):
          anns = json.load(open(resFile))
      elif type(resFile) == np.ndarray:
          anns = self.loadNumpyAnnotations(resFile)
      else:
          anns = resFile
      assert type(anns) == list, 'results in not an array of objects'
      annsImgIds = [ann['image_id'] for ann in anns]
      assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
          'Results do not correspond to current coco set'
      if 'caption' in anns[0]:
          imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
          res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
          for id, ann in enumerate(anns):
              ann['id'] = id + 1
      elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
          res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
          for id, ann in enumerate(anns):
              bb = ann['bbox']
              x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
              if 'segmentation' not in ann:
                  ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
              ann['area'] = bb[2] * bb[3]
              ann['id'] = id + 1
              ann['iscrowd'] = 0
      elif 'segmentation' in anns[0]:
          res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
          for id, ann in enumerate(anns):
              # now only support compressed RLE format as segmentation results
              ann['area'] = maskUtils.area(ann['segmentation'])
              if 'bbox' not in ann:
                  ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
              ann['id'] = id + 1
              ann['iscrowd'] = 0
      elif 'keypoints' in anns[0]:
          res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
          for id, ann in enumerate(anns):
              s = ann['keypoints']
              x = s[0::3]
              y = s[1::3]
              x1, x2, y1, y2 = np.min(x), np.max(x), np.min(y), np.max(y)
              ann['area'] = (x2 - x1) * (y2 - y1)
              ann['id'] = id + 1
              ann['bbox'] = [x1, y1, x2 - x1, y2 - y1]
      # print('DONE (t={:0.2f}s)'.format(time.time()- tic))
  
      res.dataset['annotations'] = anns
      createIndex(res)
      return res
  
  
  def evaluate(self):
      '''
      Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
      :return: None
      '''
      # tic = time.time()
      # print('Running per image evaluation...')
      p = self.params
      # add backward compatibility if useSegm is specified in params
      if p.useSegm is not None:
          p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
          print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))
      # print('Evaluate annotation type *{}*'.format(p.iouType))
      p.imgIds = list(np.unique(p.imgIds))
      if p.useCats:
          p.catIds = list(np.unique(p.catIds))
      p.maxDets = sorted(p.maxDets)
      self.params = p
  
      self._prepare()
      # loop through images, area range, max detection number
      catIds = p.catIds if p.useCats else [-1]
  
      if p.iouType == 'segm' or p.iouType == 'bbox':
          computeIoU = self.computeIoU
      elif p.iouType == 'keypoints':
          computeIoU = self.computeOks
      self.ious = {
          (imgId, catId): computeIoU(imgId, catId)
          for imgId in p.imgIds
          for catId in catIds}
  
      evaluateImg = self.evaluateImg
      maxDet = p.maxDets[-1]
      evalImgs = [
          evaluateImg(imgId, catId, areaRng, maxDet)
          for catId in catIds
          for areaRng in p.areaRng
          for imgId in p.imgIds
      ]
      # this is NOT in the pycocotools code, but could be done outside
      evalImgs = np.asarray(evalImgs).reshape(len(catIds), len(p.areaRng), len(p.imgIds))
      self._paramsEval = copy.deepcopy(self.params)
      # toc = time.time()
      # print('DONE (t={:0.2f}s).'.format(toc-tic))
      return p.imgIds, evalImgs
  
apis: torch.stack numpy.asarray numpy.max numpy.min numpy.array numpy.concatenate numpy.unique