train_classifier_mgr.py 文件源码

def _get_init_fn():
    """Returns a function run by the chief worker to warm-start the training.

    Note that the init_fn is only run when initializing the model during the very
    first global step.

    Returns:
        An init function run by the supervisor.
    """
    if FLAGS.checkpoint_path is None:
        return None

    # Warn the user if a checkpoint exists in the train_dir. Then we'll be
    # ignoring the checkpoint anyway.
    if tf.train.latest_checkpoint(FLAGS.train_dir):
        tf.logging.info(
                'Ignoring --checkpoint_path because a checkpoint already exists in %s'
                % FLAGS.train_dir)
        return None

    exclusions = []
    if FLAGS.checkpoint_exclude_scopes:
        exclusions = [scope.strip()
                                    for scope in FLAGS.checkpoint_exclude_scopes.split(',')]

    # TODO(sguada) variables.filter_variables()
    variables_to_restore = []
    for var in slim.get_model_variables():
        excluded = False
        for exclusion in exclusions:
            if var.op.name.startswith(exclusion):
                excluded = True
                break
        if not excluded:
            variables_to_restore.append(var)

    if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
        checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
    else:
        checkpoint_path = FLAGS.checkpoint_path

    tf.logging.info('Fine-tuning from %s' % checkpoint_path)

    return slim.assign_from_checkpoint_fn(
            checkpoint_path,
            variables_to_restore,
            ignore_missing_vars=FLAGS.ignore_missing_vars)