bbbc006_eval.py 文件源码

def evaluate():
    """Eval BBBC006 for a number of steps."""
    with tf.Graph().as_default() as g:
        # Get images and labels for BBBC006.
        eval_data = FLAGS.eval_data == 'test'
        images, labels = bbbc006.inputs(eval_data=eval_data)

        # Build a Graph that computes the logits predictions from the
        # inference model.
        c_fuse, s_fuse = bbbc006.inference(images, train=False)

        dice_op = bbbc006.dice_op(c_fuse, s_fuse, labels)

        # Restore the moving average version of the learned variables for eval.
        variable_averages = tf.train.ExponentialMovingAverage(
            bbbc006.MOVING_AVERAGE_DECAY)
        variables_to_restore = variable_averages.variables_to_restore()
        saver = tf.train.Saver(variables_to_restore)

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.summary.merge_all()

        summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)

        while True:
            eval_once(saver, dice_op, summary_writer, summary_op)
            if FLAGS.run_once:
                break
            time.sleep(FLAGS.eval_interval_secs)