import tensorflow as tf

      self._add_forward_graph()
      self._add_yolo_interpret_graph()
      #self._add_yolo_loss_graph()
      #self._add_train_graph()
      #self._add_viz_graph()

  def _add_forward_graph(self):
    """Build the VGG-16 model."""

    if self.mc.LOAD_PRETRAINED_MODEL:
      assert tf.gfile.Exists(self.mc.PRETRAINED_MODEL_PATH), \
          'Cannot find pretrained model at the given path:' \
          '  {}'.format(self.mc.PRETRAINED_MODEL_PATH)
      self.caffemodel_weight = joblib.load(self.mc.PRETRAINED_MODEL_PATH)

    with tf.variable_scope('darknet19') as scope:
      conv1 = self._conv_layer(
          'conv1', self.image_input, filters=32, size=3, stride=1, bn=self.BN, act='lrelu', freeze=True)
      pool1 = self._pooling_layer(
          'pool1', conv1, size=2, stride=2)
      conv2 = self._conv_layer(
          'conv2', pool1, filters=64, size=3, stride=1, bn=self.BN, act='lrelu', freeze=True)
      pool2 = self._pooling_layer(
          'pool2', conv2, size=2, stride=2)
      conv3 = self._conv_layer(
          'conv3', pool2, filters=128, size=3, stride=1, bn=self.BN, act='lrelu')
      conv4 = self._conv_layer(
          'conv4', conv3, filters=64, size=1, stride=1, bn=self.BN, act='lrelu')
      conv5 = self._conv_layer(
          'conv5', conv4, filters=128, size=3, stride=1, bn=self.BN, act='lrelu')
