path: root/tensorflow/python/training/saver.py

# pylint: disable=invalid-name
"""Save and restore variables."""
import collections
import numbers
import os.path
import time

from google.protobuf import text_format

from tensorflow.python.client import graph_util
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_io_ops
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import gfile
from tensorflow.python.platform import logging
from tensorflow.python.training import saver_pb2
from tensorflow.python.training import training_util
from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState


class BaseSaverBuilder(object):
  """Base class for Savers.

  Can be extended to create different Ops.
  """

  class VarToSave(object):
    """Class used to describe variable slices that need to be saved."""

    def __init__(self, var, slice_spec, name):
      self.var = var
      self.slice_spec = slice_spec
      self.name = name

  def __init__(self):
    pass

  def save_op(self, filename_tensor, vars_to_save):
    """Create an Op to save 'vars_to_save'.

    This is intended to be overridden by subclasses that want to generate
    different Ops.

    Args:
      filename_tensor: String Tensor.
      vars_to_save: a list of BaseSaverBuilder.VarToSave objects.

    Returns:
      An Operation that save the variables.
    """
    return io_ops._save(
        filename=filename_tensor,
        tensor_names=[vs.name for vs in vars_to_save],
        tensors=[vs.var for vs in vars_to_save],
        tensor_slices=[vs.slice_spec for vs in vars_to_save])

  def restore_op(self, filename_tensor, var_to_save, preferred_shard):
    """Create an Op to read the variable 'var_to_save'.

    This is intended to be overridden by subclasses that want to generate
    different Ops.

    Args:
      filename_tensor: String Tensor.
      var_to_save: a BaseSaverBuilder.VarToSave object.
      preferred_shard: Int.  Shard to open first when loading a sharded file.

    Returns:
      A Tensor resulting from reading 'var_to_save' from 'filename'.
    """
    return io_ops._restore_slice(
        filename_tensor,
        var_to_save.name,
        var_to_save.slice_spec,
        var_to_save.var.dtype,
        preferred_shard=preferred_shard)

  def sharded_filename(self, filename_tensor, shard, num_shards):
    """Append sharding information to a filename.

    Args:
      filename_tensor: a string tensor.
      shard: integer.  The shard for the filename.
      num_shards: an int Tensor for the number of shards.

    Returns:
      A string tensor.
    """
    return gen_io_ops._sharded_filename(filename_tensor, shard, num_shards)

  def _AddSaveOps(self, filename_tensor, vars_to_save):
    """Add ops to save variables that are on the same shard.

    Args:
      filename_tensor: String Tensor.
      vars_to_save: a list of _VarToSave objects.

    Returns:
      A tensor with the filename used to save.
    """
    save = self.save_op(filename_tensor, vars_to_save)
    return control_flow_ops.with_dependencies([save], filename_tensor)

  def _AddShardedSaveOps(self, filename_tensor, per_device):
    """Add ops to save the params per shard.

    Args:
      filename_tensor: String Tensor.
      per_device: A list of (device, BaseSaverBuilder.VarToSave) pairs, as
        returned by _GroupByDevices().

    Returns:
      An op to save the variables.
    """
    num_shards = len(per_device)
    sharded_saves = []
    num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
    for shard, (device, vars_to_save) in enumerate(per_device):
      with ops.device(device):
        sharded_filename = self.sharded_filename(
            filename_tensor, shard, num_shards_tensor)
        sharded_saves.append(self._AddSaveOps(sharded_filename, vars_to_save))
    # Return the sharded name for the save path.
    with ops.control_dependencies([x.op for x in sharded_saves]):
      return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor)

  def _AddRestoreOps(self,
                     filename_tensor,
                     vars_to_save,
                     restore_sequentially,
                     reshape,
                     preferred_shard=-1,
                     name="restore_all"):
    """Add operations to restore vars_to_save.

    Args:
      filename_tensor: Tensor for the path of the file to load.
      vars_to_save: a list of _VarToSave objects.
      restore_sequentially: True if we want to restore variables sequentially
        within a shard.
      reshape: True if we want to reshape loaded tensors to the shape of
        the corresponding variable.
      preferred_shard: Shard to open first when loading a sharded file.
      name: Name for the returned op.

    Returns:
      An Operation that restores the variables.
    """
    assign_ops = []
    for vs in vars_to_save:
      v = vs.var
      restore_control_inputs = assign_ops[-1:] if restore_sequentially else []
      # Load and optionally reshape on the CPU, as string tensors are not
      # available on the GPU.
      # TODO(mdevin): Re-enable restore on GPU when we can support annotating
      # string tensors as "HostMemory" inputs.
      with ops.device(graph_util.set_cpu0(v.device) if v.device else None):
        with ops.control_dependencies(restore_control_inputs):
          values = self.restore_op(filename_tensor, vs, preferred_shard)
        if reshape:
          shape = v.get_shape()
          if not shape.is_fully_defined():
            shape = array_ops.shape(v)
          values = array_ops.reshape(values, shape)

      # Assign on the same device as the variable.
      with ops.device(v.device):
        assign_ops.append(state_ops.assign(v,
                                           values,
                                           validate_shape=not reshape))

    # Create a Noop that has control dependencies from all the updates.
    return control_flow_ops.group(*assign_ops, name=name)

  def _AddShardedRestoreOps(self, filename_tensor, per_device,
                            restore_sequentially, reshape):
    """Add Ops to save variables from multiple devices.

    Args:
      filename_tensor: Tensor for the path of the file to load.
      per_device: A list of (device, _VarToSave) pairs, as
        returned by _GroupByDevices().
      restore_sequentially: True if we want to restore variables sequentially
        within a shard.
      reshape: True if we want to reshape loaded tensors to the shape of
        the corresponding variable.

    Returns:
      An Operation that restores the variables.
    """
    sharded_restores = []
    for shard, (device, vars_to_save) in enumerate(per_device):
      with ops.device(device):
        sharded_restores.append(self._AddRestoreOps(
            filename_tensor,
            vars_to_save,
            restore_sequentially,
            reshape,
            preferred_shard=shard,
            name="restore_shard"))
    return control_flow_ops.group(*sharded_restores, name="restore_all")

  def _IsVariable(self, v):
    return isinstance(v, ops.Tensor) and (
        v.op.type == "Variable" or v.op.type == "AutoReloadVariable")

  def _GroupByDevices(self, vars_to_save):
    """Group Variable tensor slices per device.

    TODO(mdevin): Make sure that all the devices found are on different
    job/replica/task/cpu|gpu.  It would be bad if 2 were on the same device.
    It can happen if the devices as unspecified.

    Args:
      vars_to_save: a list of BaseSaverBuilder.VarToSave objects.

    Returns:
      A list of tuples: (device_name, BaseSaverBuilder.VarToSave) tuples.
      The list is sorted by ascending device_name.
    """
    per_device = collections.defaultdict(lambda: [])
    for var_to_save in vars_to_save:
      per_device[var_to_save.var.device].append(var_to_save)
    return sorted([(dev, tup) for dev, tup in per_device.iteritems()],
                  key=lambda t: t[0])

  def _VarListToDict(self, var_list):
    """Create a dictionary of names to variable lists.

    Args:
      var_list: A list, tuple, or set of Variables.

    Returns:
      A dictionary of variable names to the variables that must be saved under
      that name.  Variables with save_slice_info are grouped together under the
      same key in no particular order.

    Raises:
      TypeError: If the type of var_list or its elements is not supported.
      ValueError: If at least two variables share the same name.
    """
    if not isinstance(var_list, (list, tuple, set)):
      raise TypeError("Variables to save should be passed in a dict or a "
                      "list: %s" % var_list)
    var_list = set(var_list)
    names_to_variables = {}
    for var in var_list:
      # pylint: disable=protected-access
      if isinstance(var, variables.Variable) and var._save_slice_info:
        name = var._save_slice_info.name
        if name in names_to_variables:
          if not isinstance(names_to_variables[name], list):
            raise ValueError("Mixing slices and non-slices with the same name: "
                             "%s" % name)
          names_to_variables[name].append(var)
        else:
          names_to_variables[name] = [var]
      else:
        var = ops.convert_to_tensor(var)
        if not self._IsVariable(var):
          raise TypeError("Variable to save is not a Variable: %s" % var)
        name = var.op.name
        if name in names_to_variables:
          raise ValueError("At least two variables have the same name: %s" %
                           name)
        names_to_variables[name] = var
      # pylint: enable=protected-access
    return names_to_variables

  def _ValidateAndSliceInputs(self, names_to_variables):
    """Returns the variables and names that will be used for a Saver.

    Args:
      names_to_variables: A dict (k, v) where k is the name of a variable and v
         is a Variable to save or a BaseSaverBuilder.Saver.

    Returns:
      A list of BaseSaverBuilder.VarToSave objects.

    Raises:
      TypeError: if any of the keys are not strings or any of the
        values are not one of Tensor or Variable.
      ValueError: if the same variable is given in more than one value
        (this also applies to slices of SlicedVariables).
    """
    if not isinstance(names_to_variables, dict):
      names_to_variables = self._VarListToDict(names_to_variables)

    vars_to_save = []
    seen_variables = set()
    for name in sorted(names_to_variables.iterkeys()):
      if not isinstance(name, basestring):
        raise TypeError("names_to_variables must be a dict mapping string "
                        "names to variable Tensors. Name is not a string: %s" %
                        name)
      v = names_to_variables[name]
      if isinstance(v, (list, tuple)):
        # A set of slices.
        slice_name = None
        # pylint: disable=protected-access
        for variable in v:
          if not isinstance(variable, variables.Variable):
            raise ValueError("Slices must all be Variables: %s" % variable)
          if not variable._save_slice_info:
            raise ValueError("Slices must all be slices: %s" % variable)
          if slice_name is None:
            slice_name = variable._save_slice_info.name
          elif slice_name != variable._save_slice_info.name:
            raise variable("Slices must all be from the same tensor: %s != %s"
                           % (slice_name, variable._save_slice_info.name))
          self._AddVarToSave(vars_to_save, seen_variables,
                             variable, variable._save_slice_info.spec, name)
        # pylint: enable=protected-access
      else:
        # A variable or tensor.
        variable = ops.convert_to_tensor(v)
        if not self._IsVariable(variable):
          raise TypeError("names_to_variables must be a dict mapping string "
                          "names to Tensors/Variables. Not a variable: %s" %
                          variable)
        self._AddVarToSave(vars_to_save, seen_variables, variable, "", name)
    return vars_to_save

  def _AddVarToSave(self, vars_to_save, seen_variables, variable, slice_spec,
                    name):
    """Create a VarToSave and add it  to the vars_to_save list.

    Args:
      vars_to_save: List to append the new VarToSave to.
      seen_variables: Set of variables already processed.  Used to check
        that each variable is only saved once.
      variable: Variable to save.
      slice_spec: String.  Slice spec for the variable.
      name: Name to use to save the variable.

    Raises:
      ValueError: If the variable has already been processed.
    """
    if variable in seen_variables:
      raise ValueError("The same variable will be restored with two names: %s",
                       variable)
    vars_to_save.append(BaseSaverBuilder.VarToSave(variable, slice_spec, name))
    seen_variables.add(variable)

  def build(self,
            names_to_variables,
            reshape=False,
            sharded=False,
            max_to_keep=5,
            keep_checkpoint_every_n_hours=10000.0,
            name=None,
            restore_sequentially=False):
    """Adds save/restore nodes to the graph and creates a SaverDef proto.

    Args:
      names_to_variables: A dictionary mapping name to a Variable.
        Each name will be associated with the
        corresponding variable in the checkpoint.
      reshape: If True, allow restoring parameters from a checkpoint
        that where the parameters have a different shape.  This is
        only needed when you try to restore from a Dist-Belief checkpoint,
        and only some times.
      sharded: If True, shard the checkpoints, one per device that has
        Parameters nodes.
      max_to_keep: maximum number of checkpoints to keep.  As new checkpoints
        are created, old ones are deleted.  If None or 0, no checkpoints are
        deleted.  Presently the number is only roughly enforced.  For example
        in case of restarts more than max_to_keep checkpoints may be kept.
      keep_checkpoint_every_n_hours: How often checkpoints should be kept.
        Defaults to 10,000 hours.
      name: string.  Optional name to use as a prefix when adding operations.
      restore_sequentially: A Bool, which if true, causes restore of different
        variables to happen sequentially within each device.

    Returns:
      A SaverDef proto.

    Raises:
      TypeError: If 'names_to_variables' is not a dictionary mapping string
        keys to variable Tensors.
      ValueError: If any of the keys or values in 'names_to_variables' is not
        unique.
    """
    vars_to_save = self._ValidateAndSliceInputs(names_to_variables)
    if max_to_keep is None:
      max_to_keep = 0

    with ops.op_scope([vs.var for vs in vars_to_save], name, "save") as name:
      # Add the Constant string tensor for the filename.
      filename_tensor = constant_op.constant("model")

      # Add the save ops.
      if sharded:
        per_device = self._GroupByDevices(vars_to_save)
        save_tensor = self._AddShardedSaveOps(filename_tensor, per_device)
        restore_op = self._AddShardedRestoreOps(
            filename_tensor, per_device, restore_sequentially, reshape)
      else:
        save_tensor = self._AddSaveOps(filename_tensor, vars_to_save)
        restore_op = self._AddRestoreOps(
            filename_tensor, vars_to_save, restore_sequentially, reshape)

    assert restore_op.name.endswith("restore_all"), restore_op.name

    return saver_pb2.SaverDef(
        filename_tensor_name=filename_tensor.name,
        save_tensor_name=save_tensor.name,
        restore_op_name=restore_op.name,
        max_to_keep=max_to_keep,
        keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
        sharded=sharded)

def _GetCheckpointFilename(save_dir, latest_filename):
  """Returns a filename for storing the CheckpointState.

  Args:
    save_dir: The directory for saving and restoring checkpoints.
    latest_filename: Name of the file in 'save_dir' that is used
      to store the CheckpointState.

  Returns:
    The path of the file that contains the CheckpointState proto.
  """
  if latest_filename is None:
    latest_filename = "checkpoint"
  return os.path.join(save_dir, latest_filename)


def update_checkpoint_state(save_dir,
                            model_checkpoint_path,
                            all_model_checkpoint_paths=None,
                            latest_filename=None):
  """Updates the content of the 'checkpoint' file.

  This updates the checkpoint file containing a CheckpointState
  proto.

  Args:
    save_dir: Directory where the model was saved.
    model_checkpoint_path: The checkpoint file.
    all_model_checkpoint_paths: list of strings.  Paths to all not-yet-deleted
      checkpoints, sorted from oldest to newest.  If this is a non-empty list,
      the last element must be equal to model_checkpoint_path.  These paths
      are also saved in the CheckpointState proto.
    latest_filename: Optional name of the checkpoint file.  Default to
      'checkpoint'.

  Raises:
    RuntimeError: If the save paths conflict.
  """
  if all_model_checkpoint_paths is None:
    all_model_checkpoint_paths = []
  elif all_model_checkpoint_paths[-1] != model_checkpoint_path:
    logging.warning(
        "%s is not in all_model_checkpoint_paths! Manually adding it.",
        model_checkpoint_path)
    all_model_checkpoint_paths.append(model_checkpoint_path)
  # Writes the "checkpoint" file for the coordinator for later restoration.
  coord_checkpoint_filename = _GetCheckpointFilename(save_dir, latest_filename)
  if coord_checkpoint_filename == model_checkpoint_path:
    raise RuntimeError("Save path '%s' conflicts with path used for "
                       "checkpoint state.  Please use a different save path." %
                       model_checkpoint_path)
  coord_checkpoint_proto = CheckpointState(
      model_checkpoint_path=model_checkpoint_path,
      all_model_checkpoint_paths=all_model_checkpoint_paths)
  f = gfile.FastGFile(coord_checkpoint_filename, mode="w")
  f.write(text_format.MessageToString(coord_checkpoint_proto))
  f.close()


def get_checkpoint_state(checkpoint_dir, latest_filename=None):
  """Returns CheckpointState proto from the "checkpoint" file.

  If the "checkpoint" file contains a valid CheckpointState
  proto, returns it.

  Args:
    checkpoint_dir: The directory of checkpoints.
    latest_filename: Optional name of the checkpoint file.  Default to
      'checkpoint'.

  Returns:
    A CheckpointState if the state was available, None
    otherwise.
  """
  ckpt = None
  coord_checkpoint_filename = _GetCheckpointFilename(
      checkpoint_dir, latest_filename)
  f = None
  try:
    # Check that the file exists before opeining it to avoid
    # many lines of errors from colossus in the logs.
    if gfile.Exists(coord_checkpoint_filename):
      f = gfile.FastGFile(coord_checkpoint_filename, mode="r")
      ckpt = CheckpointState()
      text_format.Merge(f.read(), ckpt)
  except gfile.FileError:
    # It's ok if the file cannot be read
    return None
  except text_format.ParseError, e:
    logging.warning(str(e))
    logging.warning("%s: Checkpoint ignored", coord_checkpoint_filename)
    return None
  finally:
    if f:
      f.close()
  return ckpt


class Saver(object):
  """Saves and restores variables.

  See [Variables](../../how_tos/variables/index.md)
  for an overview of variables, saving and restoring.

  The `Saver` class adds ops to save and restore variables to and from
  *checkpoints*.  It also provides convenience methods to run these ops.

  Checkpoints are binary files in a proprietary format which map variable names
  to tensor values.  The best way to examine the contents of a checkpoint is to
  load it using a `Saver`.

  Savers can automatically number checkpoint filenames with a provided counter.
  This lets you keep multiple checkpoints at different steps while training a
  model.  For example you can number the checkpoint filenames with the training
  step number.  To avoid filling up disks, savers manage checkpoint files
  automatically. For example, they can keep only the N most recent files, or
  one checkpoint for every N hours of training.

  You number checkpoint filenames by passing a value to the optional
  `global_step` argument to `save()`:

  ```python
  saver.save('my-model', global_step=0) ==> filename: 'my-model-0'
  ...
  saver.save('my-model', global_step=1000) ==> filename: 'my-model-1000'
  ```

  Additionally, optional arguments to the `Saver()` constructor let you control
  the proliferation of checkpoint files on disk:

  * `max_to_keep` indicates the maximum number of recent checkpoint files to
    keep.  As new files are created, older files are deleted.  If None or 0,
    all checkpoint files are kept.  Defaults to 5 (that is, the 5 most recent
    checkpoint files are kept.)

  * `keep_checkpoint_every_n_hours`: In addition to keeping the most recent
    `max_to_keep` checkpoint files, you might want to keep one checkpoint file
    for every N hours of training.  This can be useful if you want to later
    analyze how a model progressed during a long training session.  For
    example, passing `keep_checkpoint_every_n_hours=2` ensures that you keep
    one checkpoint file for every 2 hours of training.  The default value of
    10,000 hours effectively disables the feature.

  Note that you still have to call the `save()` method to save the model.
  Passing these arguments to the constructor will not save variables
  automatically for you.

  A training program that saves regularly looks like:

  ```python
  ...
  # Create a saver.
  saver = tf.train.Saver(...variables...)
  # Launch the graph and train, saving the model every 1,000 steps.
  sess = tf.Session()
  for step in xrange(1000000):
      sess.run(..training_op..)
      if step % 1000 == 0:
          # Append the step number to the checkpoint name:
          saver.save(sess, 'my-model', global_step=step)
  ```

  In addition to checkpoint files, savers keep a protocol buffer on disk with
  the list of recent checkpoints. This is used to manage numbered checkpoint
  files and by `latest_checkpoint()`, which makes it easy to discover the path
  to the most recent checkpoint. That protocol buffer is stored in a file named
  'checkpoint' next to the checkpoint files.

  If you create several savers, you can specify a different filename for the
  protocol buffer file in the call to `save()`.

  @@__init__
  @@save
  @@restore

  Other utility methods.

  @@last_checkpoints
  @@set_last_checkpoints
  @@as_saver_def
  """

  def __init__(self,
               var_list=None,
               reshape=False,
               sharded=False,
               max_to_keep=5,
               keep_checkpoint_every_n_hours=10000.0,
               name=None,
               restore_sequentially=False,
               saver_def=None,
               builder=None):
    """Creates a `Saver`.

    The constructor adds ops to save and restore variables.

    `var_list` specifies the variables that will be saved and restored. It can
    be passed as a `dict` or a list:

    * A `dict` of names to variables: The keys are the names that will be
      used to save or restore the variables in the checkpoint files.
    * A list of variables: The variables will be keyed with their op name in
      the checkpoint files.

    For example:

    ```python
    v1 = tf.Variable(..., name='v1')
    v2 = tf.Variable(..., name='v2')

    # Pass the variables as a dict:
    saver = tf.train.Saver({'v1': v1, 'v2': v2})

    # Or pass them as a list.
    saver = tf.train.Saver([v1, v2])
    # Passing a list is equivalent to passing a dict with the variable op names
    # as keys:
    saver = tf.train.Saver({v.op.name: v for v in [v1, v2]})
    ```

    The optional `reshape` argument, if True, allows restoring a variable from
    a save file where the variable had a different shape, but the same number
    of elements and type.  This is useful if you have reshaped a variable and
    want to reload it from an older checkpoint.

    The optional `sharded` argument, if True, instructs the saver to shard
    checkpoints per device.

    Args:
      var_list: A list of Variables or a dictionary mapping names to
        Variables.  If None, defaults to the list of all variables.
      reshape: If True, allows restoring parameters from a checkpoint
        where the variables have a different shape.
      sharded: If True, shard the checkpoints, one per device.
      max_to_keep: maximum number of recent checkpoints to keep.
        Defaults to 10,000 hours.
      keep_checkpoint_every_n_hours: How often to keep checkpoints.
        Defaults to 10,000 hours.
      name: string.  Optional name to use as a prefix when adding operations.
      restore_sequentially: A Bool, which if true, causes restore of different
        variables to happen sequentially within each device.  This can lower
        memory usage when restoring very large models.
      saver_def: Optional SaverDef proto to use instead of running the builder.
        This is only useful for specialty code that wants to recreate a Saver
        object for a previously built Graph that had a Saver.  The saver_def
        proto should be the one returned by the as_saver_def() call of the
        Saver that was created for that Graph.
      builder: Optional SaverBuilder to use if a saver_def was not provided.
        Defaults to BaseSaverBuilder().

    Raises:
      TypeError: If `var_list` is invalid.
      ValueError: If any of the keys or values in `var_list` is not unique.
    """
    if saver_def is None:
      if builder is None:
        builder = BaseSaverBuilder()
      if var_list is None:
        var_list = variables.all_variables()
      if not var_list:
        raise ValueError("No variables to save")
      saver_def = builder.build(
          var_list,
          reshape=reshape,
          sharded=sharded,
          max_to_keep=max_to_keep,
          keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours,
          name=name,
          restore_sequentially=restore_sequentially)
    if not isinstance(saver_def, saver_pb2.SaverDef):
      raise ValueError("saver_def must if a saver_pb2.SaverDef: %s" % saver_def)
    if not saver_def.save_tensor_name:
      raise ValueError("saver_def must specify the save_tensor_name: %s"
                       % str(saver_def))
    if not saver_def.restore_op_name:
      raise ValueError("saver_def must specify the restore_op_name: %s"
                       % str(saver_def))
    self._filename_tensor_name = saver_def.filename_tensor_name
    self._save_tensor_name = saver_def.save_tensor_name
    self._restore_op_name = saver_def.restore_op_name
    self._max_to_keep = saver_def.max_to_keep
    # If keep_checkpoint_every_n_hours is not set, set it to 10000 hours.
    self._keep_checkpoint_every_n_hours = (
        saver_def.keep_checkpoint_every_n_hours if
        saver_def.keep_checkpoint_every_n_hours else 10000)
    self._next_checkpoint_time = (
        time.time() + self._keep_checkpoint_every_n_hours * 3600)
    self._sharded = saver_def.sharded
    self._last_checkpoints = []

  def _CheckpointFilename(self, p):
    """Returns the checkpoint file name.

    If p is (filename, time) pair, return p[0]; else return p.

    Args:
      p: (filename, time) pair or just checkpoint filename.

    Returns:
      Checkpoint file name.
    """
    return p[0] if isinstance(p, tuple) else p

  def _MaybeDeleteOldCheckpoints(self, latest_save_path):
    """Deletes old checkpoints if necessary.

    Always keep the last max_to_keep checkpoints.  If
    keep_checkpoint_every_n_hours was specified, keep an additional checkpoint
    every N hours. For example, if N is 0.5, an additional checkpoint is kept
    for every 0.5 hours of training; if N is 10, an additional checkpoint is
    kept for every 10 hours of training.

    Args:
      latest_save_path: Name including path of checkpoint file to save.
    """
    if not self._max_to_keep:
      return
    # Remove first from list if the same name was used before.
    for p in self._last_checkpoints:
      if latest_save_path == self._CheckpointFilename(p):
        self._last_checkpoints.remove(p)
    # Append new path to list
    self._last_checkpoints.append((latest_save_path, time.time()))
    # If more than max_to_keep, remove oldest.
    if len(self._last_checkpoints) > self._max_to_keep:
      p = self._last_checkpoints.pop(0)
      # Do not delete the file if we keep_checkpoint_every_n_hours is set and we
      # have reached N hours of training.
      should_keep = p[1] > self._next_checkpoint_time
      if should_keep:
        self._next_checkpoint_time += (
            self._keep_checkpoint_every_n_hours * 3600)
        return
      # Otherwise delete the files.
      for f in gfile.Glob(self._CheckpointFilename(p)):
        try:
          gfile.Remove(f)
        except gfile.GOSError, e:
          logging.warning("Ignoring: %s", str(e))

  def as_saver_def(self):
    """Generates a `SaverDef` representation of this saver.

    Returns:
      A `SaverDef` proto.
    """
    return saver_pb2.SaverDef(
        filename_tensor_name=self._filename_tensor_name,
        save_tensor_name=self._save_tensor_name,
        restore_op_name=self._restore_op_name,
        max_to_keep=self._max_to_keep,
        keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours,
        sharded=self._sharded)

  @property
  def last_checkpoints(self):
    """List of not-yet-deleted checkpoint filenames.

    You can pass any of the returned values to `restore()`.

    Returns:
      A list of checkpoint filenames, sorted from oldest to newest.
    """
    return list(self._CheckpointFilename(p) for p in self._last_checkpoints)

  def set_last_checkpoints(self, last_checkpoints):
    """Sets the list of not-yet-deleted checkpoint filenames.

    Args:
      last_checkpoints: a list of checkpoint filenames.

    Raises:
      AssertionError: if the list of checkpoint filenames has already been set.
    """
    assert not self._last_checkpoints
    assert isinstance(last_checkpoints, list)
    self._last_checkpoints = list(last_checkpoints)

  def save(self, sess, save_path, global_step=None, latest_filename=None):
    """Saves variables.

    This method runs the ops added by the constructor for saving variables.
    It requires a session in which the graph was launched.  The variables to
    save must also have been initialized.

    The method returns the path of the newly created checkpoint file.  This
    path can be passed directly to a call to `restore()`.

    Args:
      sess: A Session to use to save the variables..
      save_path: string.  Path to the checkpoint filename.  If the saver is
        `sharded`, this is the prefix of the sharded checkpoint filename.
      global_step: If provided the global step number is appended to
        `save_path` to create the checkpoint filename. The optional argument
        can be a Tensor, a Tensor name or an integer.
      latest_filename: Optional name for the protocol buffer file that will
        contains the list of most recent checkpoint filenames.  That file,
        kept in the same directory as the checkpoint files, is automatically
        managed by the saver to keep track of recent checkpoints.  Defaults to
        'checkpoint'.

    Returns:
      A string: path at which the variables were saved.  If the saver is
        sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
        is the number of shards created.

    Raises:
      TypeError: If `sess` is not a Session.
    """
    if latest_filename is None:
      latest_filename = "checkpoint"
    if global_step is not None:
      if not isinstance(global_step, numbers.Number):
        global_step = training_util.global_step(sess, global_step)
      checkpoint_file = "%s-%d" % (save_path, global_step)
    else:
      checkpoint_file = save_path
    save_path = os.path.dirname(save_path)
    if not isinstance(sess, session.SessionInterface):
      raise TypeError("'sess' must be a Session; %s" % sess)

    model_checkpoint_path = sess.run(
        self._save_tensor_name, {self._filename_tensor_name: checkpoint_file})
    model_checkpoint_path = str(model_checkpoint_path)
    self._MaybeDeleteOldCheckpoints(model_checkpoint_path)
    update_checkpoint_state(save_path, model_checkpoint_path,
                            self.last_checkpoints, latest_filename)
    return model_checkpoint_path

  def restore(self, sess, save_path):
    """Restores previously saved variables.

    This method runs the ops added by the constructor for restoring variables.
    It requires a session in which the graph was launched.  The variables to
    restore do not have to have been initialized, as restoring is itself a way
    to initialize variables.

    The `save_path` argument is typically a value previously returned from a
    `save()` call, or a call to `latest_checkpoint()`.

    Args:
      sess: A Session to use to restore the parameters.
      save_path: Path where parameters were previously saved.
    """
    sess.run([self._restore_op_name], {self._filename_tensor_name: save_path})


def latest_checkpoint(checkpoint_dir, latest_filename=None):
  """Finds the filename of latest saved checkpoint file.

  Args:
    checkpoint_dir: Directory where the variables were saved.
    latest_filename: Optional name for the protocol buffer file that
      contains the list of most recent checkpoint filenames.
      See the corresponding argument to `Saver.save()`.

  Returns:
    The full path to the latest checkpoint or None if no checkpoint was found.
  """
  # Pick the latest checkpoint based on checkpoint state.
  ckpt = get_checkpoint_state(checkpoint_dir, latest_filename)
  if ckpt and ckpt.model_checkpoint_path:
    checkpoint_full_path = os.path.join(
        checkpoint_dir, ckpt.model_checkpoint_path)
    if gfile.Exists(checkpoint_full_path):
      return checkpoint_full_path

  return None