class_docstr: RPN Head
  
  Args:
      anchor_generator (object): `AnchorGenerator` instance
      rpn_target_assign (object): `RPNTargetAssign` instance
      train_proposal (object): `GenerateProposals` instance for training
      test_proposal (object): `GenerateProposals` instance for testing
      num_classes (int): number of classes in rpn output
docstr: Get anchor and RPN head output.
  
  Args:
      input(Variable): feature map from backbone with shape of [N, C, H, W]
  
  Returns:
      rpn_cls_score(Variable): Output of rpn head with shape of [N, num_anchors, H, W].
      rpn_bbox_pred(Variable): Output of rpn head with shape of [N, num_anchors * 4, H, W].
docstr_tok: ["Get","anchor","and","RPN","head","output",".","Args",":","input","(","Variable",")",":","feature","map","from","backbone","with","shape","of","[","N",",","C",",","H",",","W","]","Returns",":","rpn_cls_score","(","Variable",")",":","Output","of","rpn","head","with","shape","of","[","N",",","num_anchors",",","H",",","W","]",".","rpn_bbox_pred","(","Variable",")",":","Output","of","rpn","head","with","shape","of","[","N",",","num_anchors","*","4",",","H",",","W","]","."]
code: def _get_output(self, input):
      """
      Get anchor and RPN head output.
  
      Args:
          input(Variable): feature map from backbone with shape of [N, C, H, W]
  
      Returns:
          rpn_cls_score(Variable): Output of rpn head with shape of [N, num_anchors, H, W].
          rpn_bbox_pred(Variable): Output of rpn head with shape of [N, num_anchors * 4, H, W].
      """
      dim_out = input.shape[1]
      rpn_conv = fluid.layers.conv2d(
          input=input,
          num_filters=dim_out,
          filter_size=3,
          stride=1,
          padding=1,
          act="relu",
          name="conv_rpn",
          param_attr=ParamAttr(
              name="conv_rpn_w", initializer=Normal(loc=0.0, scale=0.01)
          ),
          bias_attr=ParamAttr(
              name="conv_rpn_b",
              learning_rate=2.0,
              regularizer=L2Decay(0.0),
          ),
      )
      # Generate anchors self.anchor_generator
      self.anchor, self.anchor_var = fluid.layers.anchor_generator(
          input=rpn_conv,
          anchor_sizes=self.anchor_generator.anchor_sizes,
          aspect_ratios=self.anchor_generator.aspect_ratios,
          variance=self.anchor_generator.variance,
          stride=self.anchor_generator.stride,
      )
  
      num_anchor = self.anchor.shape[2]
      # Proposal classification scores
      self.rpn_cls_score = fluid.layers.conv2d(
          rpn_conv,
          num_filters=num_anchor * self.num_classes,
          filter_size=1,
          stride=1,
          padding=0,
          act=None,
          name="rpn_cls_score",
          param_attr=ParamAttr(
              name="rpn_cls_logits_w",
              initializer=Normal(loc=0.0, scale=0.01),
          ),
          bias_attr=ParamAttr(
              name="rpn_cls_logits_b",
              learning_rate=2.0,
              regularizer=L2Decay(0.0),
          ),
      )
      # Proposal bbox regression deltas
      self.rpn_bbox_pred = fluid.layers.conv2d(
          rpn_conv,
          num_filters=4 * num_anchor,
          filter_size=1,
          stride=1,
          padding=0,
          act=None,
          name="rpn_bbox_pred",
          param_attr=ParamAttr(
              name="rpn_bbox_pred_w",
              initializer=Normal(loc=0.0, scale=0.01),
          ),
          bias_attr=ParamAttr(
              name="rpn_bbox_pred_b",
              learning_rate=2.0,
              regularizer=L2Decay(0.0),
          ),
      )
      return self.rpn_cls_score, self.rpn_bbox_pred
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Normal
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 2 for calling the RPNHead obj's underlying member method code with example usage: obj._get_output(input) and returns: self,  self