Python ops.register_proto_function函数代码示例

    sess: `Session` used to run the queue ops.  Defaults to the
      default session.
    coord: Optional `Coordinator` for coordinating the started threads.
    daemon: Whether the threads should be marked as `daemons`, meaning
      they don't block program exit.
    start: Set to `False` to only create the threads, not start them.
    collection: A `GraphKey` specifying the graph collection to
      get the queue runners from.  Defaults to `GraphKeys.QUEUE_RUNNERS`.

  Returns:
    A list of threads.
  """
  if sess is None:
    sess = ops.get_default_session()
    if not sess:
      raise ValueError("Cannot start queue runners: No default session is "
                       "registered. Use `with sess.as_default()` or pass an "
                       "explicit session to tf.start_queue_runners(sess=sess)")
  with sess.graph.as_default():
    threads = []
    for qr in ops.get_collection(collection):
      threads.extend(qr.create_threads(sess, coord=coord, daemon=daemon,
                                       start=start))
  return threads


ops.register_proto_function(ops.GraphKeys.QUEUE_RUNNERS,
                            proto_type=queue_runner_pb2.QueueRunnerDef,
                            to_proto=QueueRunner.to_proto,
                            from_proto=QueueRunner.from_proto)

def _to_proto_fn(v, export_scope=None):
  """Converts Variable and ResourceVariable to VariableDef for collections."""
  return v.to_proto(export_scope=export_scope)


def _from_proto_fn(v, import_scope=None):
  """Creates Variable or ResourceVariable from VariableDef as needed."""
  if v.is_resource:
    return ResourceVariable.from_proto(v, import_scope=import_scope)
  return variables.Variable.from_proto(v, import_scope=import_scope)


ops.register_proto_function(
    ops.GraphKeys.GLOBAL_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=_to_proto_fn,
    from_proto=_from_proto_fn)
ops.register_proto_function(
    ops.GraphKeys.TRAINABLE_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=_to_proto_fn,
    from_proto=_from_proto_fn)
ops.register_proto_function(
    ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=_to_proto_fn,
    from_proto=_from_proto_fn)
ops.register_proto_function(
    ops.GraphKeys.LOCAL_VARIABLES,
    proto_type=variable_pb2.VariableDef,

    hparam_proto = hparam_pb2.HParamDef()
    for name in self._hparam_types:
      # Parse the values.
      param_type, is_list = self._hparam_types.get(name, (None, None))
      kind = HParams._get_kind_name(param_type, is_list)

      if is_list:
        if kind.startswith('bytes'):
          v_list = [compat.as_bytes(v) for v in getattr(self, name)]
        else:
          v_list = [v for v in getattr(self, name)]
        getattr(hparam_proto.hparam[name], kind).value.extend(v_list)
      else:
        v = getattr(self, name)
        if kind.startswith('bytes'):
          v = compat.as_bytes(getattr(self, name))
        setattr(hparam_proto.hparam[name], kind, v)

    return hparam_proto

  @staticmethod
  def from_proto(hparam_def, import_scope=None):  # pylint: disable=unused-argument
    return HParams(hparam_def=hparam_def)


ops.register_proto_function(
    'hparams',
    proto_type=hparam_pb2.HParamDef,
    to_proto=HParams.to_proto,
    from_proto=HParams.from_proto)

            variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
            # Return a 1-D tensor containing all the names of uninitialized variables.
            return array_ops.boolean_mask(variable_names_tensor, variables_mask)


# pylint: disable=protected-access
ops.register_tensor_conversion_function(Variable, Variable._TensorConversionFunction)
Variable._OverloadAllOperators()

ops.register_tensor_conversion_function(PartitionedVariable, PartitionedVariable._TensorConversionFunction)
# pylint: enable=protected-access

ops.register_dense_tensor_like_type(Variable)
ops.register_proto_function(
    ops.GraphKeys.GLOBAL_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=Variable.to_proto,
    from_proto=Variable.from_proto,
)
ops.register_proto_function(
    ops.GraphKeys.TRAINABLE_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=Variable.to_proto,
    from_proto=Variable.from_proto,
)
ops.register_proto_function(
    ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
    proto_type=variable_pb2.VariableDef,
    to_proto=Variable.to_proto,
    from_proto=Variable.from_proto,
)

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intension of it being imported
  at a later time or location to restart training, run inference, or be
  a subgraph.

  Args:
    filename: Optional filename including the path for writing the
      generated `MetaGraphDef` protocol buffer.
    meta_info_def: `MetaInfoDef` protocol buffer.
    graph_def: `GraphDef` protocol buffer.
    saver_def: `SaverDef` protocol buffer.
    collection_list: List of string keys to collect.

  Returns:
    A `MetaGraphDef` proto.
  """
  meta_graph_def = _as_meta_graph_def(meta_info_def=meta_info_def,
                                      graph_def=graph_def,
                                      saver_def=saver_def,
                                      collection_list=collection_list)
  if filename:
    training_util.write_graph(meta_graph_def, os.path.dirname(filename),
                              os.path.basename(filename))
  return meta_graph_def

ops.register_proto_function(ops.GraphKeys.SAVERS,
                            proto_type=saver_pb2.SaverDef,
                            to_proto=Saver.to_proto,
                            from_proto=Saver.from_proto)