train_model.py 文件源码

def __get_init_fn(self):
        """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 self.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(self.train_dir):
            tf.logging.info(
                    'Ignoring --checkpoint_path because a checkpoint already exists in %s'
                    % self.train_dir)
            return None

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

        # TODO(sguada) variables.filter_variables()
        variables_to_restore = []
        all_variables = slim.get_model_variables()
        if self.fine_tune_vgg16:
            global_step = slim.get_or_create_global_step()
            all_variables.append(global_step)
        for var in all_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(self.checkpoint_path):
            checkpoint_path = tf.train.latest_checkpoint(self.checkpoint_path)
        else:
            checkpoint_path = self.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=self.ignore_missing_vars)