Python tensorflow.python.framework.meta_graph.export_scoped_meta_graph() Examples

def _testScopedImportWithQueue(self, test_dir, exported_filename,
                                 new_exported_filename):
    graph = tf.Graph()
    meta_graph.import_scoped_meta_graph(
        os.path.join(test_dir, exported_filename),
        graph=graph,
        import_scope="new_queue1")
    graph.as_graph_element("new_queue1/dequeue:0")
    graph.as_graph_element("new_queue1/close")
    with graph.as_default():
      new_meta_graph, _ = meta_graph.export_scoped_meta_graph(
          filename=os.path.join(test_dir, new_exported_filename),
          graph=graph, export_scope="new_queue1")

    return new_meta_graph

  # Verifies that we can export the subgraph containing a FIFOQueue under
  # "queue1" and import it into "new_queue1" in a new graph. 

def testPotentialCycle(self):
    graph1 = tf.Graph()
    with graph1.as_default():
      a = tf.constant(1.0, shape=[2, 2])
      b = tf.constant(2.0, shape=[2, 2])
      matmul = tf.matmul(a, b)
      with tf.name_scope("hidden1"):
        c = tf.nn.relu(matmul)
        d = tf.constant(3.0, shape=[2, 2])
        matmul = tf.matmul(c, d)

    orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(
        export_scope="hidden1", graph=graph1)

    graph2 = tf.Graph()
    with graph2.as_default():
      with self.assertRaisesRegexp(ValueError, "Graph contains unbound inputs"):
        meta_graph.import_scoped_meta_graph(
            orig_meta_graph, import_scope="new_hidden1")

      meta_graph.import_scoped_meta_graph(
          orig_meta_graph, import_scope="new_hidden1",
          input_map={"$unbound_inputs_MatMul": tf.constant(4.0, shape=[2, 2])}) 

def testExportGraphDefWithScope(self):
    test_dir = _TestDir("export_graph_def")
    saver0_ckpt = os.path.join(test_dir, "saver0.ckpt")
    graph1 = tf.Graph()
    with graph1.as_default():
      with tf.name_scope("hidden1"):
        images = tf.constant(1.0, tf.float32, shape=[3, 2], name="images")
        weights1 = tf.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
                               name="weights")
        biases1 = tf.Variable([0.1] * 3, name="biases")
        tf.nn.relu(tf.matmul(images, weights1) + biases1, name="relu")

    # Run the graph and save scoped checkpoint.
    with self.test_session(graph=graph1) as sess:
      sess.run(tf.global_variables_initializer())
      _, var_list_1 = meta_graph.export_scoped_meta_graph(
          graph_def=graph1.as_graph_def(), export_scope="hidden1")
      saver = saver_module.Saver(var_list=var_list_1, max_to_keep=1)
      saver.save(sess, saver0_ckpt, write_state=False)

    expected = np.reshape([[5.0999999, 7.0999999, 9.10000038] * 3], (3, 3))

    # Verifies that we can run successfully after restoring.
    graph2 = tf.Graph()
    new_var_list_1 = meta_graph.copy_scoped_meta_graph(
        from_scope="hidden1", to_scope="new_hidden1",
        from_graph=graph1, to_graph=graph2)

    with self.test_session(graph=graph2) as sess:
      saver3 = saver_module.Saver(var_list=new_var_list_1, max_to_keep=1)
      saver3.restore(sess, saver0_ckpt)
      self.assertAllClose(expected, sess.run("new_hidden1/relu:0")) 

def testExportNestedNames(self):
    graph1 = tf.Graph()
    with graph1.as_default():
      with tf.name_scope("hidden1/hidden2/hidden3"):
        images = tf.constant(1.0, tf.float32, shape=[3, 2], name="images")
        weights1 = tf.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
                               name="weights")
        biases1 = tf.Variable([0.1] * 3, name="biases")
        tf.nn.relu(tf.matmul(images, weights1) + biases1, name="relu")

    orig_meta_graph, var_list = meta_graph.export_scoped_meta_graph(
        export_scope="hidden1/hidden2", graph=graph1)
    var_names = [v.name for _, v in var_list.items()]
    self.assertEqual(["hidden3/biases:0", "hidden3/weights:0"],
                     sorted(var_list.keys()))
    self.assertEqual(["hidden1/hidden2/hidden3/biases:0",
                      "hidden1/hidden2/hidden3/weights:0"],
                     sorted(var_names))
    for node in orig_meta_graph.graph_def.node:
      self.assertTrue(node.name.startswith("hidden3"))

    graph2 = tf.Graph()
    new_var_list = meta_graph.import_scoped_meta_graph(
        orig_meta_graph, import_scope="new_hidden1/new_hidden2",
        graph=graph2)
    self.assertEqual(["hidden3/biases:0", "hidden3/weights:0"],
                     sorted(new_var_list.keys()))
    new_var_names = [v.name for _, v in new_var_list.items()]
    self.assertEqual(["new_hidden1/new_hidden2/hidden3/biases:0",
                      "new_hidden1/new_hidden2/hidden3/weights:0"],
                     sorted(new_var_names))

    nodes = ["new_hidden1/new_hidden2/hidden3/biases/Assign",
             "new_hidden1/new_hidden2/hidden3/weights/Assign"]
    expected = [b"loc:@new_hidden1/new_hidden2/hidden3/biases",
                b"loc:@new_hidden1/new_hidden2/hidden3/weights"]
    for n, e in zip(nodes, expected):
      self.assertEqual([e], graph2.get_operation_by_name(n).get_attr("_class")) 

def meta_graph_def(self):
    meta_graph_def = None
    with self._g.as_default():
      with tf.device(self._device):
        for compiler in self._compilers:
          if hasattr(compiler, "finish"):
            compiler.finish()

      meta_graph_def, _ = meta_graph.export_scoped_meta_graph()
    return meta_graph_def 

def export_meta_graph(filename=None,
                      meta_info_def=None,
                      graph_def=None,
                      saver_def=None,
                      collection_list=None,
                      as_text=False,
                      graph=None,
                      export_scope=None,
                      clear_devices=False,
                      **kwargs):
  """Returns `MetaGraphDef` proto. Optionally writes it to filename.

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intention 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.
    as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto.
    graph: The `Graph` to import into. If `None`, use the default graph.
    export_scope: Optional `string`. Name scope under which to extract
      the subgraph. The scope name will be striped from the node definitions
      for easy import later into new name scopes. If `None`, the whole graph
      is exported. graph_def and export_scope cannot both be specified.
    clear_devices: Whether or not to clear the device field for an `Operation`
      or `Tensor` during export.
    **kwargs: Optional keyed arguments.

  Returns:
    A `MetaGraphDef` proto.

  Raises:
    ValueError: When the `GraphDef` is larger than 2GB.
  """
  meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
      filename=filename,
      meta_info_def=meta_info_def,
      graph_def=graph_def,
      saver_def=saver_def,
      collection_list=collection_list,
      as_text=as_text,
      graph=graph,
      export_scope=export_scope,
      clear_devices=clear_devices,
      **kwargs)
  return meta_graph_def 

def export_meta_graph(filename=None,
                      meta_info_def=None,
                      graph_def=None,
                      saver_def=None,
                      collection_list=None,
                      as_text=False,
                      graph=None,
                      export_scope=None,
                      clear_devices=False,
                      **kwargs):
  """Returns `MetaGraphDef` proto. Optionally writes it to filename.

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intention 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.
    as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto.
    graph: The `Graph` to import into. If `None`, use the default graph.
    export_scope: Optional `string`. Name scope under which to extract
      the subgraph. The scope name will be striped from the node definitions
      for easy import later into new name scopes. If `None`, the whole graph
      is exported. graph_def and export_scope cannot both be specified.
    clear_devices: Whether or not to clear the device field for an `Operation`
      or `Tensor` during export.
    **kwargs: Optional keyed arguments.

  Returns:
    A `MetaGraphDef` proto.

  Raises:
    ValueError: When the `GraphDef` is larger than 2GB.
  """
  meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
      filename=filename,
      meta_info_def=meta_info_def,
      graph_def=graph_def,
      saver_def=saver_def,
      collection_list=collection_list,
      as_text=as_text,
      graph=graph,
      export_scope=export_scope,
      clear_devices=clear_devices,
      **kwargs)
  return meta_graph_def 

def export_meta_graph(filename=None,
                      meta_info_def=None,
                      graph_def=None,
                      saver_def=None,
                      collection_list=None,
                      as_text=False,
                      graph=None,
                      export_scope=None,
                      clear_devices=False,
                      **kwargs):
  """Returns `MetaGraphDef` proto. Optionally writes it to filename.

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intention 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.
    as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto.
    graph: The `Graph` to import into. If `None`, use the default graph.
    export_scope: Optional `string`. Name scope under which to extract
      the subgraph. The scope name will be striped from the node definitions
      for easy import later into new name scopes. If `None`, the whole graph
      is exported. graph_def and export_scope cannot both be specified.
    clear_devices: Whether or not to clear the device field for an `Operation`
      or `Tensor` during export.
    **kwargs: Optional keyed arguments.

  Returns:
    A `MetaGraphDef` proto.

  Raises:
    ValueError: When the `GraphDef` is larger than 2GB.
  """
  meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
      filename=filename,
      meta_info_def=meta_info_def,
      graph_def=graph_def,
      saver_def=saver_def,
      collection_list=collection_list,
      as_text=as_text,
      graph=graph,
      export_scope=export_scope,
      clear_devices=clear_devices,
      **kwargs)
  return meta_graph_def 

def _testScopedSave(self, test_dir, exported_filename, ckpt_filename):
    graph = tf.Graph()
    with graph.as_default():
      # Creates an inference graph.
      # Hidden 1
      images = tf.constant(1.2, tf.float32, shape=[100, 28], name="images")
      with tf.name_scope("hidden1"):
        weights1 = tf.Variable(
            tf.truncated_normal([28, 128],
                                stddev=1.0 / math.sqrt(float(28))),
            name="weights")
        # The use of control_flow_ops.cond here is purely for adding test
        # coverage the save and restore of control flow context (which doesn't
        # make any sense here from a machine learning perspective).  The typical
        # biases is a simple Variable without the conditions.
        biases1 = tf.Variable(
            control_flow_ops.cond(tf.less(random.random(), 0.5),
                                  lambda: tf.ones([128]),
                                  lambda: tf.zeros([128])),
            name="biases")
        hidden1 = tf.nn.relu(tf.matmul(images, weights1) + biases1)

      # Hidden 2
      with tf.name_scope("hidden2"):
        weights2 = tf.Variable(
            tf.truncated_normal([128, 32],
                                stddev=1.0 / math.sqrt(float(128))),
            name="weights")
        # The use of control_flow_ops.while_loop here is purely for adding test
        # coverage the save and restore of control flow context (which doesn't
        # make any sense here from a machine learning perspective).  The typical
        # biases is a simple Variable without the conditions.
        def loop_cond(it, _):
          return it < 2
        def loop_body(it, biases2):
          biases2 += tf.constant(0.1, shape=[32])
          return it + 1, biases2
        _, biases2 = control_flow_ops.while_loop(
            loop_cond, loop_body,
            [tf.constant(0), tf.Variable(tf.zeros([32]))])
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights2) + biases2)
      # Linear
      with tf.name_scope("softmax_linear"):
        weights3 = tf.Variable(
            tf.truncated_normal([32, 10],
                                stddev=1.0 / math.sqrt(float(32))),
            name="weights")
        biases3 = tf.Variable(tf.zeros([10]), name="biases")
        logits = tf.matmul(hidden2, weights3) + biases3
        tf.add_to_collection("logits", logits)
      _, var_list = meta_graph.export_scoped_meta_graph(
          filename=os.path.join(test_dir, exported_filename),
          graph=tf.get_default_graph(), export_scope="hidden1")
      self.assertEqual(["biases:0", "weights:0"], sorted(var_list.keys()))

    with self.test_session(graph=graph) as sess:
      sess.run(tf.global_variables_initializer())
      saver = saver_module.Saver(var_list=var_list, max_to_keep=1)
      saver.save(sess, os.path.join(test_dir, ckpt_filename),
                 write_state=False) 

def testCopyScopedGraph(self):
    test_dir = _TestDir("scoped_copy")
    saver0_ckpt = os.path.join(test_dir, "saver0.ckpt")
    graph1 = tf.Graph()
    with graph1.as_default():
      with tf.name_scope("hidden1"):
        images = tf.constant(1.0, tf.float32, shape=[3, 2], name="images")
        weights1 = tf.Variable([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
                               name="weights")
        biases1 = tf.Variable([0.1] * 3, name="biases")
        tf.nn.relu(tf.matmul(images, weights1) + biases1, name="relu")

    # Run the graph and save scoped checkpoint.
    with self.test_session(graph=graph1) as sess:
      sess.run(tf.global_variables_initializer())
      _, var_list_1 = meta_graph.export_scoped_meta_graph(
          export_scope="hidden1")
      saver = saver_module.Saver(var_list=var_list_1, max_to_keep=1)
      saver.save(sess, saver0_ckpt, write_state=False)

    expected = np.reshape([[5.0999999, 7.0999999, 9.10000038] * 3], (3, 3))

    # Verifies copy to the same graph with the same name fails.
    with graph1.as_default():
      with self.assertRaisesWithPredicateMatch(
          ValueError, lambda e: "need to be different" in str(e)):
        meta_graph.copy_scoped_meta_graph(from_scope="hidden1",
                                          to_scope="hidden1")

    # Verifies copy to the same graph.
    with graph1.as_default():
      var_list_2 = meta_graph.copy_scoped_meta_graph(from_scope="hidden1",
                                                     to_scope="hidden2")

    with self.test_session(graph=graph1) as sess:
      saver1 = saver_module.Saver(var_list=var_list_1, max_to_keep=1)
      saver1.restore(sess, saver0_ckpt)
      saver2 = saver_module.Saver(var_list=var_list_2, max_to_keep=1)
      saver2.restore(sess, saver0_ckpt)
      self.assertAllClose(expected, sess.run("hidden1/relu:0"))
      self.assertAllClose(expected, sess.run("hidden2/relu:0"))

    # Verifies copy to differen graph.
    graph2 = tf.Graph()
    new_var_list_1 = meta_graph.copy_scoped_meta_graph(
        from_scope="hidden1", to_scope="new_hidden1",
        from_graph=graph1, to_graph=graph2)

    with self.test_session(graph=graph2) as sess:
      saver3 = saver_module.Saver(var_list=new_var_list_1, max_to_keep=1)
      saver3.restore(sess, saver0_ckpt)
      self.assertAllClose(expected, sess.run("new_hidden1/relu:0")) 

def testNoVariables(self):
    test_dir = _TestDir("no_variables")
    filename = os.path.join(test_dir, "metafile")

    input_feed_value = -10  # Arbitrary input value for feed_dict.

    orig_graph = tf.Graph()
    with self.test_session(graph=orig_graph) as sess:
      # Create a minimal graph with zero variables.
      input_tensor = tf.placeholder(tf.float32, shape=[], name="input")
      offset = tf.constant(42, dtype=tf.float32, name="offset")
      output_tensor = tf.add(input_tensor, offset, name="add_offset")

      # Add input and output tensors to graph collections.
      tf.add_to_collection("input_tensor", input_tensor)
      tf.add_to_collection("output_tensor", output_tensor)

      output_value = sess.run(output_tensor, {input_tensor: input_feed_value})
      self.assertEqual(output_value, 32)

      # Generates MetaGraphDef.
      meta_graph_def, var_list = meta_graph.export_scoped_meta_graph(
          filename=filename,
          graph_def=tf.get_default_graph().as_graph_def(add_shapes=True),
          collection_list=["input_tensor", "output_tensor"],
          saver_def=None)
      self.assertEqual({}, var_list)

    # Create a clean graph and import the MetaGraphDef nodes.
    new_graph = tf.Graph()
    with self.test_session(graph=new_graph) as sess:
      # Import the previously export meta graph.
      meta_graph.import_scoped_meta_graph(filename)

      # Re-exports the current graph state for comparison to the original.
      new_meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
          filename + "_new")
      self.assertProtoEquals(meta_graph_def, new_meta_graph_def)

      # Ensures that we can still get a reference to our graph collections.
      new_input_tensor = tf.get_collection("input_tensor")[0]
      new_output_tensor = tf.get_collection("output_tensor")[0]
      # Verifies that the new graph computes the same result as the original.
      new_output_value = sess.run(
          new_output_tensor, {new_input_tensor: input_feed_value})
      self.assertEqual(new_output_value, output_value) 

def testClearDevices(self):
    graph1 = tf.Graph()
    with graph1.as_default():
      with tf.device("/device:CPU:0"):
        a = tf.Variable(tf.constant(1.0, shape=[2, 2]), name="a")
      with tf.device("/job:ps/replica:0/task:0/gpu:0"):
        b = tf.Variable(tf.constant(2.0, shape=[2, 2]), name="b")
      with tf.device("/job:localhost/replica:0/task:0/cpu:0"):
        tf.matmul(a, b, name="matmul")

    self.assertEqual("/device:CPU:0", str(graph1.as_graph_element("a").device))
    self.assertEqual("/job:ps/replica:0/task:0/device:GPU:0",
                     str(graph1.as_graph_element("b").device))
    self.assertEqual("/job:localhost/replica:0/task:0/device:CPU:0",
                     str(graph1.as_graph_element("matmul").device))

    # Verifies that devices are cleared on export.
    orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(
        graph=graph1, clear_devices=True)

    graph2 = tf.Graph()
    with graph2.as_default():
      meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=False)

    self.assertEqual("", str(graph2.as_graph_element("a").device))
    self.assertEqual("", str(graph2.as_graph_element("b").device))
    self.assertEqual("", str(graph2.as_graph_element("matmul").device))

    # Verifies that devices are cleared on export when passing in graph_def.
    orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(
        graph_def=graph1.as_graph_def(), clear_devices=True)

    graph2 = tf.Graph()
    with graph2.as_default():
      meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=False)

    self.assertEqual("", str(graph2.as_graph_element("a").device))
    self.assertEqual("", str(graph2.as_graph_element("b").device))
    self.assertEqual("", str(graph2.as_graph_element("matmul").device))

    # Verifies that devices are cleared on import.
    orig_meta_graph, _ = meta_graph.export_scoped_meta_graph(
        graph=graph1, clear_devices=False)

    graph2 = tf.Graph()
    with graph2.as_default():
      meta_graph.import_scoped_meta_graph(orig_meta_graph, clear_devices=True)

    self.assertEqual("", str(graph2.as_graph_element("a").device))
    self.assertEqual("", str(graph2.as_graph_element("b").device))
    self.assertEqual("", str(graph2.as_graph_element("matmul").device)) 

def export_meta_graph(filename=None,
                      meta_info_def=None,
                      graph_def=None,
                      saver_def=None,
                      collection_list=None,
                      as_text=False,
                      graph=None,
                      export_scope=None,
                      clear_devices=False,
                      clear_extraneous_savers=False,
                      **kwargs):
  """Returns `MetaGraphDef` proto. Optionally writes it to filename.

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intention 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.
    as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto.
    graph: The `Graph` to import into. If `None`, use the default graph.
    export_scope: Optional `string`. Name scope under which to extract
      the subgraph. The scope name will be striped from the node definitions
      for easy import later into new name scopes. If `None`, the whole graph
      is exported. graph_def and export_scope cannot both be specified.
    clear_devices: Whether or not to clear the device field for an `Operation`
      or `Tensor` during export.
    clear_extraneous_savers: Remove any Saver-related information from the
        graph (both Save/Restore ops and SaverDefs) that are not associated
        with the provided SaverDef.
    **kwargs: Optional keyed arguments.

  Returns:
    A `MetaGraphDef` proto.

  Raises:
    ValueError: When the `GraphDef` is larger than 2GB.
  """
  if context.in_eager_mode():
    raise ValueError("Exporting/importing meta graphs is not supported when "
                     "eager execution is enabled. No graph exists when eager "
                     "execution is enabled.")
  meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
      filename=filename,
      meta_info_def=meta_info_def,
      graph_def=graph_def,
      saver_def=saver_def,
      collection_list=collection_list,
      as_text=as_text,
      graph=graph,
      export_scope=export_scope,
      clear_devices=clear_devices,
      clear_extraneous_savers=clear_extraneous_savers,
      **kwargs)
  return meta_graph_def 

def export_meta_graph(filename=None,
                      meta_info_def=None,
                      graph_def=None,
                      saver_def=None,
                      collection_list=None,
                      as_text=False,
                      graph=None,
                      export_scope=None,
                      clear_devices=False,
                      **kwargs):
  """Returns `MetaGraphDef` proto. Optionally writes it to filename.

  This function exports the graph, saver, and collection objects into
  `MetaGraphDef` protocol buffer with the intention 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.
    as_text: If `True`, writes the `MetaGraphDef` as an ASCII proto.
    graph: The `Graph` to import into. If `None`, use the default graph.
    export_scope: Optional `string`. Name scope under which to extract
      the subgraph. The scope name will be striped from the node definitions
      for easy import later into new name scopes. If `None`, the whole graph
      is exported. graph_def and export_scope cannot both be specified.
    clear_devices: Whether or not to clear the device field for an `Operation`
      or `Tensor` during export.
    **kwargs: Optional keyed arguments.

  Returns:
    A `MetaGraphDef` proto.

  Raises:
    ValueError: When the `GraphDef` is larger than 2GB.
  """
  meta_graph_def, _ = meta_graph.export_scoped_meta_graph(
      filename=filename,
      meta_info_def=meta_info_def,
      graph_def=graph_def,
      saver_def=saver_def,
      collection_list=collection_list,
      as_text=as_text,
      graph=graph,
      export_scope=export_scope,
      clear_devices=clear_devices,
      **kwargs)
  return meta_graph_def