content: # ------------------------------------------------------------------------
  # UP-DETR
  # Copyright (c) Tencent, Inc. and its affiliates. All Rights Reserved.
  # ------------------------------------------------------------------------
  # Modified from DETR (https://github.com/facebookresearch/detr)
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  # ------------------------------------------------------------------------
  """
  COCO evaluator that works in distributed mode.
  
  Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
  The difference is that there is less copy-pasting from pycocotools
  in the end of the file, as python3 can suppress prints with contextlib
  """
  import os
  import contextlib
  import copy
  import numpy as np
  import torch
  
  from pycocotools.cocoeval import COCOeval
  from pycocotools.coco import COCO
  import pycocotools.mask as mask_util
  
  from util.misc import all_gather
  
  
  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)
  
              # suppress pycocotools prints
              with open(os.devnull, 'w') as devnull:
                  with contextlib.redirect_stdout(devnull):
                      coco_dt = COCO.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 = all_gather(img_ids)
      all_eval_imgs = 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
  #################################################################
  
  
  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
  
  #################################################################
  # end of straight copy from pycocotools, just removing the prints
  #################################################################
  