nn_skeleton.py 文件源码

def _add_loss_graph(self):
    """Define the loss operation."""
    mc = self.mc

    with tf.variable_scope('class_regression') as scope:
      # cross-entropy: q * -log(p) + (1-q) * -log(1-p)
      # add a small value into log to prevent blowing up
      self.class_loss = tf.truediv(
          tf.reduce_sum(
              (self.labels*(-tf.log(self.pred_class_probs+mc.EPSILON))
               + (1-self.labels)*(-tf.log(1-self.pred_class_probs+mc.EPSILON)))
              * self.input_mask * mc.LOSS_COEF_CLASS),
          self.num_objects,
          name='class_loss'
      )
      tf.add_to_collection('losses', self.class_loss)

    with tf.variable_scope('confidence_score_regression') as scope:
      input_mask = tf.reshape(self.input_mask, [mc.BATCH_SIZE, mc.ANCHORS])
      self.conf_loss = tf.reduce_mean(
          tf.reduce_sum(
              tf.square((self.ious - self.pred_conf)) 
              * (input_mask*mc.LOSS_COEF_CONF_POS/self.num_objects
                 +(1-input_mask)*mc.LOSS_COEF_CONF_NEG/(mc.ANCHORS-self.num_objects)),
              reduction_indices=[1]
          ),
          name='confidence_loss'
      )
      tf.add_to_collection('losses', self.conf_loss)
      tf.summary.scalar('mean iou', tf.reduce_sum(self.ious)/self.num_objects)

    with tf.variable_scope('bounding_box_regression') as scope:
      self.bbox_loss = tf.truediv(
          tf.reduce_sum(
              mc.LOSS_COEF_BBOX * tf.square(
                  self.input_mask*(self.pred_box_delta-self.box_delta_input))),
          self.num_objects,
          name='bbox_loss'
      )
      tf.add_to_collection('losses', self.bbox_loss)

    # add above losses as well as weight decay losses to form the total loss
    self.loss = tf.add_n(tf.get_collection('losses'), name='total_loss')