class_docstr: Evaluate Pascal VOC style AP for Pascal VOC dataset.
  It contains a synchronization, therefore has to be called from all ranks.
  
  Note that the concept of AP can be implemented in different ways and may not
  produce identical results. This class mimics the implementation of the official
  Pascal VOC Matlab API, and should produce similar but not identical results to the
  official API.
class_docstr_tok: ["Evaluate","Pascal","VOC","style","AP","for","Pascal","VOC","dataset",".","It","contains","a","synchronization",",","therefore","has","to","be","called","from","all","ranks",".","Note","that","the","concept","of","AP","can","be","implemented","in","different","ways","and","may","not","produce","identical","results",".","This","class","mimics","the","implementation","of","the","official","Pascal","VOC","Matlab","API",",","and","should","produce","similar","but","not","identical","results","to","the","official","API","."]
docstr: Returns:
      dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75".
docstr_tok: ["Returns",":","dict",":","has","a","key","``","segm","''",",","whose","value","is","a","dict","of","``","AP","''",",","``","AP50","''",",","and","``","AP75","''","."]
code: def evaluate(self):
      """
      Returns:
          dict: has a key "segm", whose value is a dict of "AP", "AP50", and "AP75".
      """
      all_predictions = comm.gather(self._predictions, dst=0)
      if not comm.is_main_process():
          return
      predictions = defaultdict(list)
      for predictions_per_rank in all_predictions:
          for clsid, lines in predictions_per_rank.items():
              predictions[clsid].extend(lines)
      del all_predictions
  
      self._logger.info(
          "Evaluating {} using {} metric. "
          "Note that results do not use the official Matlab API.".format(
              self._dataset_name, 2007 if self._is_2007 else 2012
          )
      )
  
      with tempfile.TemporaryDirectory(
          prefix="pascal_voc_eval_"
      ) as dirname:
          res_file_template = os.path.join(dirname, "{}.txt")
  
          aps = defaultdict(list)  # iou -> ap per class
          for cls_id, cls_name in enumerate(self._class_names):
              lines = predictions.get(cls_id, [""])
  
              with open(res_file_template.format(cls_name), "w") as f:
                  f.write("\n".join(lines))
  
              for thresh in range(50, 100, 5):
                  rec, prec, ap = voc_eval(
                      res_file_template,
                      self._anno_file_template,
                      self._image_set_path,
                      cls_name,
                      ovthresh=thresh / 100.0,
                      use_07_metric=self._is_2007,
                  )
                  aps[thresh].append(ap * 100)
  
      ret = OrderedDict()
      mAP = {iou: np.mean(x) for iou, x in aps.items()}
      ret["bbox"] = {
          "AP": np.mean(list(mAP.values())),
          "AP50": mAP[50],
          "AP75": mAP[75],
      }
      return ret
  
imports: import logging
  import numpy
  import os
  import tempfile
  import xml.etree.ElementTree
  from collections import OrderedDict, defaultdict
  from functools import lru_cache
  import torch
  from detectron2.data import MetadataCatalog
  from detectron2.utils import comm
  from detectron2.utils.file_io import PathManager
  from .evaluator import DatasetEvaluator
label_desc: Use image node_id 4 for calling the PascalVOCDetectionEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: ret