class_docstr: Mask R-CNN network, see https://arxiv.org/abs/1703.06870
  
  Args:
      backbone (object): backbone instance
      rpn_head (object): `RPNHead` instance
      bbox_head (object): `BBoxHead` instance
      mask_head (object): `MaskHead` instance
      bbox_post_process (object): `BBoxPostProcess` instance
      mask_post_process (object): `MaskPostProcess` instance
      neck (object): 'FPN' instance
class_docstr_tok: ["Mask","R-CNN","network",",","see","https",":","\/\/arxiv.org\/abs\/1703.06870","Args",":","backbone","(","object",")",":","backbone","instance","rpn_head","(","object",")",":","`","RPNHead","`","instance","bbox_head","(","object",")",":","`","BBoxHead","`","instance","mask_head","(","object",")",":","`","MaskHead","`","instance","bbox_post_process","(","object",")",":","`","BBoxPostProcess","`","instance","mask_post_process","(","object",")",":","`","MaskPostProcess","`","instance","neck","(","object",")",":","'FPN","'","instance"]
code: def get_loss(
      self,
  ):
      bbox_loss, mask_loss, rpn_loss = self._forward()
      loss = {}
      loss.update(rpn_loss)
      loss.update(bbox_loss)
      loss.update(mask_loss)
      total_loss = paddle.add_n(list(loss.values()))
      loss.update({"loss": total_loss})
      return loss
  
file_path: PaddleDetection/ppdet/modeling/architectures/mask_rcnn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from ppdet.core.workspace import register, create
  from .meta_arch import BaseArch
label_desc: Use image node_id 4 for calling the MaskRCNN obj's underlying member method code with example usage: obj.get_loss() and returns: loss