class_docstr: Wrapper of a mmdetection detector model, for detection and instance segmentation.
  Input/output formats of this class follow detectron2's convention, so a
  mmdetection model can be trained and evaluated in detectron2.
class_docstr_tok: ["Wrapper","of","a","mmdetection","detector","model",",","for","detection","and","instance","segmentation",".","Input\/output","formats","of","this","class","follow","detectron2","'s","convention",",","so","a","mmdetection","model","can","be","trained","and","evaluated","in","detectron2","."]
code: def device(self):
      return self.pixel_mean.device
  
imports: import itertools
  import logging
  import numpy
  from collections import OrderedDict
  from collections.abc import Mapping
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from omegaconf import DictConfig, OmegaConf
  from torch import Tensor, nn
  from detectron2.layers import ShapeSpec
  from detectron2.structures import BitMasks, Boxes, ImageList, Instances
  from detectron2.utils.events import get_event_storage
  from .backbone import Backbone
label_desc: Use image node_id 3 for calling the MMDetDetector obj's underlying member method code with example usage: obj.device() and returns: self