docstr: Instantiates all convolutional modules used in the RPN.
docstr_tok: ["Instantiates","all","convolutional","modules","used","in","the","RPN","."]
code: def _instantiate_layers(self):
      """Instantiates all convolutional modules used in the RPN."""
      self._rpn = Conv2D(
          output_channels=self._num_channels,
          kernel_shape=self._kernel_shape,
          initializers={"w": self._rpn_initializer},
          regularizers={"w": self._regularizer},
          name="conv",
      )
  
      self._rpn_cls = Conv2D(
          output_channels=self._num_anchors * 2,
          kernel_shape=[1, 1],
          initializers={"w": self._cls_initializer},
          regularizers={"w": self._regularizer},
          padding="VALID",
          name="cls_conv",
      )
  
      # BBox prediction is 4 values * number of anchors.
      self._rpn_bbox = Conv2D(
          output_channels=self._num_anchors * 4,
          kernel_shape=[1, 1],
          initializers={"w": self._bbox_initializer},
          regularizers={"w": self._regularizer},
          padding="VALID",
          name="bbox_conv",
      )
  
file_path: luminoth/luminoth/models/fasterrcnn/rpn.py
imports: import sonnet
  import tensorflow
  from sonnet.python.modules.conv import Conv2D
  from .rpn_target import RPNTarget
  from .rpn_proposal import RPNProposal
  from luminoth.utils.losses import smooth_l1_loss
  from luminoth.utils.vars import get_initializer, layer_summaries, variable_summaries, get_activation_function
label_desc: Use image node_id 2 for calling the RPN obj's underlying member method code with example usage: obj._instantiate_layers() without return types