Python saver_pb2.SaverDef方法代碼示例

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def _prefix_to_checkpoint_path(prefix, format_version):
  """Returns the pathname of a checkpoint file, given the checkpoint prefix.

  For V1 checkpoint, simply returns the prefix itself (the data file).  For V2,
  returns the pathname to the index file.

  Args:
    prefix: a string, the prefix of a checkpoint.
    format_version: the checkpoint format version that corresponds to the
      prefix.
  Returns:
    The pathname of a checkpoint file, taking into account the checkpoint
      format version.
  """
  if format_version == saver_pb2.SaverDef.V2:
    return prefix + ".index"  # The index file identifies a checkpoint.
  return prefix  # Just the data file. 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def checkpoint_exists(checkpoint_prefix):
  """Checks whether a V1 or V2 checkpoint exists with the specified prefix.

  This is the recommended way to check if a checkpoint exists, since it takes
  into account the naming difference between V1 and V2 formats.

  Args:
    checkpoint_prefix: the prefix of a V1 or V2 checkpoint, with V2 taking
      priority.  Typically the result of `Saver.save()` or that of
      `tf.train.latest_checkpoint()`, regardless of sharded/non-sharded or
      V1/V2.
  Returns:
    A bool, true iff a checkpoint referred to by `checkpoint_prefix` exists.
  """
  pathname = _prefix_to_checkpoint_path(checkpoint_prefix,
                                        saver_pb2.SaverDef.V2)
  if file_io.get_matching_files(pathname):
    return True
  elif file_io.get_matching_files(checkpoint_prefix):
    return True
  else:
    return False 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def main(unused_args, flags):
  if flags.checkpoint_version == 1:
    checkpoint_version = saver_pb2.SaverDef.V1
  elif flags.checkpoint_version == 2:
    checkpoint_version = saver_pb2.SaverDef.V2
  else:
    print("Invalid checkpoint version (must be '1' or '2'): %d" %
          flags.checkpoint_version)
    return -1
  freeze_graph(flags.input_graph, flags.input_saver, flags.input_binary,
               flags.input_checkpoint, flags.output_node_names,
               flags.restore_op_name, flags.filename_tensor_name,
               flags.output_graph, flags.clear_devices, flags.initializer_nodes,
               flags.variable_names_whitelist, flags.variable_names_blacklist,
               flags.input_meta_graph, flags.input_saved_model_dir,
               flags.saved_model_tags, checkpoint_version) 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def _parse_input_saver_proto(input_saver, input_binary):
  """Parser input tensorflow Saver into SaverDef proto."""
  if not gfile.Exists(input_saver):
    print("Input saver file '" + input_saver + "' does not exist!")
    return -1
  mode = "rb" if input_binary else "r"
  with gfile.FastGFile(input_saver, mode) as f:
    saver_def = saver_pb2.SaverDef()
    if input_binary:
      saver_def.ParseFromString(f.read())
    else:
      text_format.Merge(f.read(), saver_def)
  return saver_def 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def __init__(self, write_version=saver_pb2.SaverDef.V2):
    self._write_version = write_version 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def save_op(self, filename_tensor, saveables):
    """Create an Op to save 'saveables'.

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

    Args:
      filename_tensor: String Tensor.
      saveables: A list of BaseSaverBuilder.SaveableObject objects.

    Returns:
      An Operation that save the variables.

    Raises:
      RuntimeError: (implementation detail) if "self._write_version" is an
        unexpected value.
    """
    # pylint: disable=protected-access
    tensor_names = []
    tensors = []
    tensor_slices = []
    for saveable in saveables:
      for spec in saveable.specs:
        tensor_names.append(spec.name)
        tensors.append(spec.tensor)
        tensor_slices.append(spec.slice_spec)
    if self._write_version == saver_pb2.SaverDef.V1:
      return io_ops._save(
          filename=filename_tensor,
          tensor_names=tensor_names,
          tensors=tensors,
          tensor_slices=tensor_slices)
    elif self._write_version == saver_pb2.SaverDef.V2:
      # "filename_tensor" is interpreted *NOT AS A FILENAME*, but as a prefix
      # of a V2 checkpoint: e.g. "/fs/train/ckpt-<step>/tmp/worker<i>-<step>".
      return io_ops.save_v2(filename_tensor, tensor_names, tensor_slices,
                            tensors)
    else:
      raise RuntimeError("Unexpected write_version: " + self._write_version)

  # pylint: disable=unused-argument 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def _AddShardedSaveOps(self, filename_tensor, per_device):
    """Add ops to save the params per shard.

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

    Returns:
      An op to save the variables.
    """
    if self._write_version == saver_pb2.SaverDef.V2:
      return self._AddShardedSaveOpsForV2(filename_tensor, per_device)

    num_shards = len(per_device)
    sharded_saves = []
    num_shards_tensor = constant_op.constant(num_shards, name="num_shards")
    for shard, (device, saveables) 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, saveables))
    # Return the sharded name for the save path.
    with ops.control_dependencies([x.op for x in sharded_saves]):
      # pylint: disable=protected-access
      return gen_io_ops._sharded_filespec(filename_tensor, num_shards_tensor) 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def _check_saver_def(self):
    if not isinstance(self.saver_def, saver_pb2.SaverDef):
      raise ValueError("saver_def must be a saver_pb2.SaverDef: %s" %
                       self.saver_def)
    if not self.saver_def.save_tensor_name:
      raise ValueError("saver_def must specify the save_tensor_name: %s" %
                       str(self.saver_def))
    if not self.saver_def.restore_op_name:
      raise ValueError("saver_def must specify the restore_op_name: %s" %
                       str(self.saver_def)) 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def to_proto(self, export_scope=None):
    """Converts this `Saver` to a `SaverDef` protocol buffer.

    Args:
      export_scope: Optional `string`. Name scope to remove.

    Returns:
      A `SaverDef` protocol buffer.
    """
    if export_scope is None:
      return self.saver_def

    if not (self.saver_def.filename_tensor_name.startswith(export_scope) and
            self.saver_def.save_tensor_name.startswith(export_scope) and
            self.saver_def.restore_op_name.startswith(export_scope)):
      return None

    saver_def = saver_pb2.SaverDef()
    saver_def.CopyFrom(self.saver_def)
    saver_def.filename_tensor_name = ops.strip_name_scope(
        saver_def.filename_tensor_name, export_scope)
    saver_def.save_tensor_name = ops.strip_name_scope(
        saver_def.save_tensor_name, export_scope)
    saver_def.restore_op_name = ops.strip_name_scope(
        saver_def.restore_op_name, export_scope)
    return saver_def 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
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:
    # Look for either a V2 path or a V1 path, with priority for V2.
    v2_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
                                         saver_pb2.SaverDef.V2)
    v1_path = _prefix_to_checkpoint_path(ckpt.model_checkpoint_path,
                                         saver_pb2.SaverDef.V1)
    if file_io.get_matching_files(v2_path) or file_io.get_matching_files(
        v1_path):
      return ckpt.model_checkpoint_path
    else:
      logging.error("Couldn't match files for checkpoint %s",
                    ckpt.model_checkpoint_path)
  return None 

# 需要導入模塊: from tensorflow.core.protobuf import saver_pb2 [as 別名]
# 或者: from tensorflow.core.protobuf.saver_pb2 import SaverDef [as 別名]
def to_proto(self, export_scope=None):
    """Converts this `Saver` to a `SaverDef` protocol buffer.

    Args:
      export_scope: Optional `string`. Name scope to remove.

    Returns:
      A `SaverDef` protocol buffer.
    """
    if (export_scope is None or
        self._name.startswith(export_scope)):
      return self.saver_def
    else:
      return None