Python saver.get_checkpoint_state方法代碼示例

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import get_checkpoint_state [as 別名]
def _restore_checkpoint(self,
                          master,
                          saver=None,
                          checkpoint_dir=None,
                          checkpoint_filename_with_path=None,
                          wait_for_checkpoint=False,
                          max_wait_secs=7200,
                          config=None):
    """Creates a `Session`, and tries to restore a checkpoint.


    Args:
      master: `String` representation of the TensorFlow master to use.
      saver: A `Saver` object used to restore a model.
      checkpoint_dir: Path to the checkpoint files. The latest checkpoint in the
        dir will be used to restore.
      checkpoint_filename_with_path: Full file name path to the checkpoint file.
      wait_for_checkpoint: Whether to wait for checkpoint to become available.
      max_wait_secs: Maximum time to wait for checkpoints to become available.
      config: Optional `ConfigProto` proto used to configure the session.

    Returns:
      A pair (sess, is_restored) where 'is_restored' is `True` if
      the session could be restored, `False` otherwise.

    Raises:
      ValueError: If both checkpoint_dir and checkpoint_filename_with_path are
        set.
    """
    self._target = master
    sess = session.Session(self._target, graph=self._graph, config=config)

    if checkpoint_dir and checkpoint_filename_with_path:
      raise ValueError("Can not provide both checkpoint_dir and "
                       "checkpoint_filename_with_path.")
    # If either saver or checkpoint_* is not specified, cannot restore. Just
    # return.
    if not saver or not (checkpoint_dir or checkpoint_filename_with_path):
      return sess, False

    if checkpoint_filename_with_path:
      saver.restore(sess, checkpoint_filename_with_path)
      return sess, True

    # Waits up until max_wait_secs for checkpoint to become available.
    wait_time = 0
    ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
    while not ckpt or not ckpt.model_checkpoint_path:
      if wait_for_checkpoint and wait_time < max_wait_secs:
        logging.info("Waiting for checkpoint to be available.")
        time.sleep(self._recovery_wait_secs)
        wait_time += self._recovery_wait_secs
        ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
      else:
        return sess, False

    # Loads the checkpoint.
    saver.restore(sess, ckpt.model_checkpoint_path)
    saver.recover_last_checkpoints(ckpt.all_model_checkpoint_paths)
    return sess, True 

# 需要導入模塊: from tensorflow.python.training import saver [as 別名]
# 或者: from tensorflow.python.training.saver import get_checkpoint_state [as 別名]
def _restore_checkpoint(self,
                          master,
                          saver=None,
                          checkpoint_dir=None,
                          wait_for_checkpoint=False,
                          max_wait_secs=7200,
                          config=None):
    """Creates a `Session`, and tries to restore a checkpoint.


    Args:
      master: `String` representation of the TensorFlow master to use.
      saver: A `Saver` object used to restore a model.
      checkpoint_dir: Path to the checkpoint files.
      wait_for_checkpoint: Whether to wait for checkpoint to become available.
      max_wait_secs: Maximum time to wait for checkpoints to become available.
      config: Optional `ConfigProto` proto used to configure the session.

    Returns:
      A pair (sess, is_restored) where 'is_restored' is `True` if
      the session could be restored, `False` otherwise.
    """
    self._target = master
    sess = session.Session(self._target, graph=self._graph, config=config)

    # If either saver or checkpoint_dir is not specified, cannot restore. Just
    # return.
    if not saver or not checkpoint_dir:
      return sess, False

    # Waits up until max_wait_secs for checkpoint to become available.
    wait_time = 0
    ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
    while not ckpt or not ckpt.model_checkpoint_path:
      if wait_for_checkpoint and wait_time < max_wait_secs:
        logging.info("Waiting for checkpoint to be available.")
        time.sleep(self._recovery_wait_secs)
        wait_time += self._recovery_wait_secs
        ckpt = saver_mod.get_checkpoint_state(checkpoint_dir)
      else:
        return sess, False

    # Loads the checkpoint.
    saver.restore(sess, ckpt.model_checkpoint_path)
    saver.recover_last_checkpoints(ckpt.all_model_checkpoint_paths)
    return sess, True