class_docstr: TTFNet network, see https://arxiv.org/abs/1909.00700
  
  Args:
      backbone (object): backbone instance
      neck (object): 'TTFFPN' instance
      ttf_head (object): 'TTFHead' instance
      post_process (object): 'BBoxPostProcess' instance
class_docstr_tok: ["TTFNet","network",",","see","https",":","\/\/arxiv.org\/abs\/1909.00700","Args",":","backbone","(","object",")",":","backbone","instance","neck","(","object",")",":","'TTFFPN","'","instance","ttf_head","(","object",")",":","'TTFHead","'","instance","post_process","(","object",")",":","'BBoxPostProcess","'","instance"]
code: def get_loss(
      self,
  ):
      loss = {}
      heatmap = self.inputs["ttf_heatmap"]
      box_target = self.inputs["ttf_box_target"]
      reg_weight = self.inputs["ttf_reg_weight"]
      hm, wh = self._forward()
      head_loss = self.ttf_head.get_loss(
          hm, wh, heatmap, box_target, reg_weight
      )
      loss.update(head_loss)
      total_loss = paddle.add_n(list(loss.values()))
      loss.update({"loss": total_loss})
      return loss
  
file_path: PaddleDetection/ppdet/modeling/architectures/ttfnet.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 TTFNet obj's underlying member method code with example usage: obj.get_loss() and returns: loss