class_docstr: PoseHigherResolutionNet
  Composed of several HighResolutionModule tied together with ConvNets
  Adapted from the GitHub version to fit with HRFPN and the Detectron2 infrastructure
  arXiv: https://arxiv.org/abs/1908.10357
class_docstr_tok: ["PoseHigherResolutionNet","Composed","of","several","HighResolutionModule","tied","together","with","ConvNets","Adapted","from","the","GitHub","version","to","fit","with","HRFPN","and","the","Detectron2","infrastructure","arXiv",":","https",":","\/\/arxiv.org\/abs\/1908.10357"]
code: def forward(self, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.conv2(x)
      x = self.bn2(x)
      x = self.relu(x)
      x = self.layer1(x)
  
      x_list = []
      for i in range(self.stage2_cfg.NUM_BRANCHES):
          if self.transition1[i] is not None:
              x_list.append(self.transition1[i](x))
          else:
              x_list.append(x)
      y_list = self.stage2(x_list)
  
      x_list = []
      for i in range(self.stage3_cfg.NUM_BRANCHES):
          if self.transition2[i] is not None:
              x_list.append(self.transition2[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      y_list = self.stage3(x_list)
  
      x_list = []
      for i in range(self.stage4_cfg.NUM_BRANCHES):
          if self.transition3[i] is not None:
              x_list.append(self.transition3[i](y_list[-1]))
          else:
              x_list.append(y_list[i])
      y_list = self.stage4(x_list)
  
      assert len(self._out_features) == len(y_list)
      return dict(zip(self._out_features, y_list))
  
imports: from __future__ import absolute_import, division, print_function
  import logging
  import torch.nn
  from detectron2.layers import ShapeSpec
  from detectron2.modeling.backbone import BACKBONE_REGISTRY
  from detectron2.modeling.backbone.backbone import Backbone
label_desc: Use image node_id 6 for calling the PoseHigherResolutionNet obj's underlying member method code with example usage: obj.forward(x) and returns: dict