EvaluationRunHook.py 文件源码

def _run_eval(self):
        """Run model evaluation and generate summaries."""
        coord = tf.train.Coordinator(clean_stop_exception_types=(
            tf.errors.CancelledError, tf.errors.OutOfRangeError))

        with tf.Session(graph=self._graph) as session:
            # Restores previously saved variables from latest checkpoint
            self._saver.restore(session, self._latest_checkpoint)

            session.run([tf.tables_initializer(), tf.local_variables_initializer()])
            tf.train.start_queue_runners(coord=coord, sess=session)
            train_step = session.run(self._gs)

            tf.logging.info('Starting evaluation')
            d = {key: [] for key in ['loss', 'accuracy', 'dice_coefficient', 'hausdorff_distance',
                                     'average_symmetric_surface_distance']}
            with coord.stop_on_exception():
                while not coord.should_stop():
                    metric_dict = session.run(self._metric_dict)

                    prediction = metric_dict.pop('prediction')
                    ground_truth = metric_dict.pop('ground_truth')

                    d['loss'].append(metric_dict.pop('loss'))
                    d['accuracy'].append(metric_dict.pop('accuracy'))
                    d['dice_coefficient'].append(metric_dict.pop('dice_coefficient'))
                    d['hausdorff_distance'].append(hd(prediction, ground_truth))
                    d['average_symmetric_surface_distance'].append(assd(prediction, ground_truth))

            # Save histogram, mean and std for each variable
            for key, value in d.iteritems():
                self.logger.log_histogram(tag=key, values=value, step=train_step, bins=15)
                self.logger.log_random_variable(tag='eval_'+key, var=value, step=train_step)
            tf.logging.info('Finished evaluation')