def freeze_graph(input_graph,
                 input_saver,
                 input_binary,
                 input_checkpoint,
                 output_node_names,
                 restore_op_name,
                 filename_tensor_name,
                 output_graph,
                 clear_devices,
                 initializer_nodes,
                 variable_names_blacklist=""):
  """Converts all variables in a graph and checkpoint into constants."""

  del restore_op_name, filename_tensor_name  # Unused by updated loading code.

  if not gfile.Exists(input_graph):
    print("Input graph file '" + input_graph + "' does not exist!")
    return -1

  if input_saver and not gfile.Exists(input_saver):
    print("Input saver file '" + input_saver + "' does not exist!")
    return -1

  # 'input_checkpoint' may be a prefix if we're using Saver V2 format
  if not saver_lib.checkpoint_exists(input_checkpoint):
    print("Input checkpoint '" + input_checkpoint + "' doesn't exist!")
    return -1

  if not output_node_names:
    print("You need to supply the name of a node to --output_node_names.")
    return -1

  input_graph_def = graph_pb2.GraphDef()
  mode = "rb" if input_binary else "r"
  with gfile.FastGFile(input_graph, mode) as f:
    if input_binary:
      input_graph_def.ParseFromString(f.read())
    else:
      text_format.Merge(f.read(), input_graph_def)
  # Remove all the explicit device specifications for this node. This helps to
  # make the graph more portable.
  if clear_devices:
    for node in input_graph_def.node:
      node.device = ""

  _ = importer.import_graph_def(input_graph_def, name="")

  with session.Session() as sess:
    if input_saver:
      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)
        saver = saver_lib.Saver(saver_def=saver_def)
        saver.restore(sess, input_checkpoint)
    else:
      var_list = {}
      reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
      var_to_shape_map = reader.get_variable_to_shape_map()
      for key in var_to_shape_map:
        try:
          tensor = sess.graph.get_tensor_by_name(key + ":0")
        except KeyError:
          # This tensor doesn't exist in the graph (for example it's
          # 'global_step' or a similar housekeeping element) so skip it.
          continue
        var_list[key] = tensor
      saver = saver_lib.Saver(var_list=var_list)
      saver.restore(sess, input_checkpoint)
      if initializer_nodes:
        sess.run(initializer_nodes)

    variable_names_blacklist = (variable_names_blacklist.split(",") if
                                variable_names_blacklist else None)
    output_graph_def = graph_util.convert_variables_to_constants(
        sess,
        input_graph_def,
        output_node_names.split(","),
        variable_names_blacklist=variable_names_blacklist)

  with gfile.GFile(output_graph, "wb") as f:
    f.write(output_graph_def.SerializeToString())
  print("%d ops in the final graph." % len(output_graph_def.node))
Converts all variables in a graph and checkpoint into constants.
legacy/freeze_graph.py