如何使用.ckpt.data和.ckpt.index加载模型

Question

在代码中，我一直使用它使用像incption_v4.ckpt这样的.ckpt来加载模型。我尝试使用预先训练好的pnesnet模型，它是由两个独立的文件.ckpt.data和.ckpt.index组成的。谁能告诉我如何从这两个文件加载。

在评估模型的代码中，它使用目录的路径作为加载模型的checkpoint_path。所以，我试着给出这样的路径，但它不起作用。

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)

上面是从.ckpt文件加载的代码。

Answer 1

只要在model.ckpt工作时使用模型的名称即可。不必关心.data和.index部分