def load_frozen_model(path_to_model):
    path_to_model = check_path(path_to_model, mode="frozen")
    with tf.gfile.GFile(path_to_model, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Session() as sess:
        tf.import_graph_def(graph_def, name="")
        return sess

builder = tf.saved_model.builder.SavedModelBuilder(saved_model_path="non existing
                                                                     folder")
session = load_frozen_model("path/to/something.pb")

# create tensorboard logs, run with $ tensorboard --log_dir path_to_log_folder
# check for the names of the input and output you need.
tf.summary.FileWriter(path_to_existing_folder, session.graph)

input_tensor = session.graph.get_tensor_by_name("name_you_need:0")
output_tensor = session.graph.get_tensor_by_name("name_you_need:0")
input_tensor_info = tf.saved_model.utils.build_tensor_info(input_tensor )
output_tensor_info = tf.saved_model.utils.build_tensor_info(output_tensor )
signature = tf.saved_model.signature_def_utils.build_signature_def(
             inputs={'input_image': input_tensor_info},
             outputs={'final_result': output_tensor_info},
             method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)

# save as SavedModel
builder.add_meta_graph_and_variables(session,
                                         [tf.saved_model.tag_constants.SERVING],
                                         signature_def_map={'serving_default':
                                                            signature})
builder.save()

def load_saved_model(path_to_model):
    with tf.Session() as sess:
        tf.saved_model.loader.load(sess, [tag_constants.SERVING], path_to_model)
        return sess