docstr: Mean Average Precision Metric. Wraps torchmetrics implementation, see
  https://torchmetrics.readthedocs.io/en/stable/detection/mean_average_precision.html
  
  :map_value: specific metric to return. Default: "map"
              Change `to one of the values in the list below to return a different value
              ['mar_1', 'mar_10', 'mar_100', 'mar_small', 'mar_medium', 'mar_large',
               'map', 'map_50', 'map_75', 'map_small', 'map_medium', 'map_large']
              See torchmetrics documentation for more details.
code: def __init__(self, map_value="map", conf_thres=0.001, iou_thres=0.6):
      """
      Mean Average Precision Metric. Wraps torchmetrics implementation, see
      https://torchmetrics.readthedocs.io/en/stable/detection/mean_average_precision.html
  
      :map_value: specific metric to return. Default: "map"
                  Change `to one of the values in the list below to return a different value
                  ['mar_1', 'mar_10', 'mar_100', 'mar_small', 'mar_medium', 'mar_large',
                   'map', 'map_50', 'map_75', 'map_small', 'map_medium', 'map_large']
                  See torchmetrics documentation for more details.
      """
  
      self._name = map_value
      self.metric = MeanAveragePrecision(box_format="xyxy")
      self.conf_thres = conf_thres
      self.iou_thres = iou_thres
      super().__init__()
  
imports: import os
  import time
  import json
  import copy
  import cv2
  import numpy
  import torch
  from torchmetrics.detection.mean_ap import MeanAveragePrecision
  import torchvision
  from pycocotools.coco import COCO
  from pycocotools.cocoeval import COCOeval
  from compression.api import DataLoader, Metric
label_desc: Use image node_id 1 to create a new MAPMetric object from inherited base classes: Metric with example: obj = MAPMetric(map_value, conf_thres, iou_thres)