Python variables._all_saveable_objects方法代码示例

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import _all_saveable_objects [as 别名]
def _is_graph_frozen() -> bool:
        """
        Checks if graph in current graph is frozen

        :return: `True` or `False`
        """
        from tensorflow.python.ops import variables
        return not bool(variables._all_saveable_objects()) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import _all_saveable_objects [as 别名]
def _get_saveable_variables(exclude_scopes=tuple()):
        # noinspection PyProtectedMember
        all_vars = variables._all_saveable_objects()
        vars_to_train = [var for var in all_vars if all(sc not in var.name for sc in exclude_scopes)]
        return vars_to_train 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import _all_saveable_objects [as 别名]
def add_meta_graph(self,
                     tags,
                     signature_def_map=None,
                     assets_collection=None,
                     legacy_init_op=None,
                     clear_devices=False,
                     main_op=None):
    """Adds the current meta graph to the SavedModel.

    Creates a Saver in the current scope and uses the Saver to export the meta
    graph def. Invoking this API requires the `add_meta_graph_and_variables()`
    API to have been invoked before.

    Args:
      tags: The set of tags to annotate the meta graph def with.
      signature_def_map: The map of signature defs to be added to the meta graph
          def.
      assets_collection: Assets collection to be saved with SavedModel. Note
          that this collection should be a subset of the assets saved as part of
          the first meta graph in the SavedModel.
      legacy_init_op: Legacy support for op or group of ops to execute after the
          restore op upon a load.
      clear_devices: Set to true if the device info on the default graph should
          be cleared.
      main_op: Op or group of ops to execute when the graph is loaded.

    Raises:
      AssertionError: If the variables for the SavedModel have not been saved
          yet.
    """
    if not self._has_saved_variables:
      raise AssertionError(
          "Graph state including variables and assets has not been saved yet. "
          "Please invoke `add_meta_graph_and_variables()` first.")

    # Validate the signature def map to ensure all included TensorInfos are
    # properly populated.
    self._validate_signature_def_map(signature_def_map)

    # Save asset files and write them to disk, if any.
    self._save_and_write_assets(assets_collection)

    if main_op is None:
      # Add legacy init op to the SavedModel.
      self._maybe_add_legacy_init_op(legacy_init_op)
    else:
      self._add_main_op(main_op)

    # Initialize a saver to generate a sharded output for all saveables in the
    # current scope.
    saver = tf_saver.Saver(
        variables._all_saveable_objects(),  # pylint: disable=protected-access
        sharded=True,
        write_version=saver_pb2.SaverDef.V2,
        allow_empty=True)

    meta_graph_def = saver.export_meta_graph(clear_devices=clear_devices)

    # Tag the meta graph def and add it to the SavedModel.
    self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map) 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import _all_saveable_objects [as 别名]
def build(self):
    """Builds saver_def."""
    if self._is_built:
      return
    self._is_built = True
    if not self.saver_def:
      if self._builder is None:
        self._builder = BaseSaverBuilder(self._write_version)
      if self._var_list is None:
        # pylint: disable=protected-access
        self._var_list = variables._all_saveable_objects()
      if not self._var_list:
        if self._allow_empty:
          self._is_empty = True
          return
        else:
          raise ValueError("No variables to save")
      self._is_empty = False
      self.saver_def = self._builder.build(
          self._var_list,
          reshape=self._reshape,
          sharded=self._sharded,
          max_to_keep=self._max_to_keep,
          keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours,
          name=self._name,
          restore_sequentially=self._restore_sequentially)
    elif self.saver_def and self._name:
      # Since self._name is used as a name_scope by builder(), we are
      # overloading the use of this field to represent the "import_scope" as
      # well.
      self.saver_def.filename_tensor_name = ops.prepend_name_scope(
          self.saver_def.filename_tensor_name, self._name)
      self.saver_def.save_tensor_name = ops.prepend_name_scope(
          self.saver_def.save_tensor_name, self._name)
      self.saver_def.restore_op_name = ops.prepend_name_scope(
          self.saver_def.restore_op_name, self._name)

    self._check_saver_def()
    # Updates next checkpoint time.
    self._next_checkpoint_time = (
        time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600)
    self._last_checkpoints = [] 

# 需要导入模块: from tensorflow.python.ops import variables [as 别名]
# 或者: from tensorflow.python.ops.variables import _all_saveable_objects [as 别名]
def _build(self, checkpoint_path, build_save, build_restore):
    """Builds saver_def."""
    if context.in_graph_mode():
      if self._is_built:
        return
      self._is_built = True

    if not self.saver_def or context.in_eager_mode():
      if self._builder is None:
        self._builder = BaseSaverBuilder(self._write_version)
      if self._var_list is None:
        # pylint: disable=protected-access
        self._var_list = variables._all_saveable_objects()
      if not self._var_list:
        if self._allow_empty:
          self._is_empty = True
          return
        else:
          raise ValueError("No variables to save")
      self._is_empty = False

      self.saver_def = self._builder._build_internal(  # pylint: disable=protected-access
          self._var_list,
          reshape=self._reshape,
          sharded=self._sharded,
          max_to_keep=self._max_to_keep,
          keep_checkpoint_every_n_hours=self._keep_checkpoint_every_n_hours,
          name=self._name,
          restore_sequentially=self._restore_sequentially,
          filename=checkpoint_path,
          build_save=build_save, build_restore=build_restore)
    elif self.saver_def and self._name:
      # Since self._name is used as a name_scope by builder(), we are
      # overloading the use of this field to represent the "import_scope" as
      # well.
      self.saver_def.filename_tensor_name = ops.prepend_name_scope(
          self.saver_def.filename_tensor_name, self._name)
      self.saver_def.save_tensor_name = ops.prepend_name_scope(
          self.saver_def.save_tensor_name, self._name)
      self.saver_def.restore_op_name = ops.prepend_name_scope(
          self.saver_def.restore_op_name, self._name)

    self._check_saver_def()
    # Updates next checkpoint time.
    self._next_checkpoint_time = (
        time.time() + self.saver_def.keep_checkpoint_every_n_hours * 3600)
    self._last_checkpoints = []
    self._checkpoints_to_be_deleted = []