import gflags
import os
import sys
import tensorflow as tf

from tensorflow.python.framework import graph_util

gflags.DEFINE_string("model_folder", "/tmp/model",
                     "Model folder to export")
gflags.DEFINE_string("output_folder", "/tmp",
                     "Folder on which the frozen model will be exported.")
gflags.DEFINE_string("output_graph_name", "frozen_model.pb",
                     "Name of the exported file.")
gflags.DEFINE_string("output_node_name", "logits",
                     "Name of the node executed.")

FLAGS = gflags.FLAGS


def freeze_graph(model_folder):
    # We retrieve our checkpoint fullpath
    checkpoint = tf.train.get_checkpoint_state(model_folder)
    input_checkpoint = checkpoint.model_checkpoint_path

    # We precise the file fullname of our freezed graph
    output_graph = os.path.join(FLAGS.output_folder,
                                FLAGS.output_graph_name)

    # Before exporting our graph, we need to precise what is our output node
    # This is how TF decides what part of the Graph he has to keep
    # and what part it can dump
    # NOTE: this variable is plural, because you can have multiple output nodes
    output_node_names = FLAGS.output_node_name

    # We clear devices to allow TensorFlow to control on which device it will
    # load operations
    clear_devices = True

    # We import the meta graph and retrieve a Saver
    saver = tf.train.import_meta_graph(
            input_checkpoint + '.meta', clear_devices=clear_devices)

    # We retrieve the protobuf graph definition
    graph = tf.get_default_graph()
    input_graph_def = graph.as_graph_def()

    # We start a session and restore the graph weights
    with tf.Session() as sess:
        saver.restore(sess, input_checkpoint)

        # We use a built-in TF helper to export variables to constants
        output_graph_def = graph_util.convert_variables_to_constants(
            sess,  # The session is used to retrieve the weights
            input_graph_def,  # The graph_def is used to retrieve the nodes
            # The output node names are used to select the usefull nodes
            output_node_names.split(",")
        )

        # Finally we serialize and dump the output graph to the filesystem
        with tf.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))


if __name__ == '__main__':
    FLAGS(sys.argv)
    freeze_graph(FLAGS.model_folder)
