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#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Fangjun Kuang)
# Please see ./run.sh for usages
import argparse
import os
import tensorflow as tf
# Code in the following function is modified from
# https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
def freeze_graph(model_dir, output_node_names, output_filename):
"""Extract the sub graph defined by the output nodes and convert all its
variables into constant
Args:
model_dir:
the root folder containing the checkpoint state file
output_node_names:
a string, containing all the output node's names, comma separated
output_filename:
Filename to save the graph.
"""
if not tf.compat.v1.gfile.Exists(model_dir):
raise AssertionError(
"Export directory doesn't exists. Please specify an export "
"directory: %s" % model_dir
)
if not output_node_names:
print("You need to supply the name of a node to --output_node_names.")
return -1
# We retrieve our checkpoint fullpath
checkpoint = tf.train.get_checkpoint_state(model_dir)
input_checkpoint = checkpoint.model_checkpoint_path
# We precise the file fullname of our freezed graph
absolute_model_dir = "/".join(input_checkpoint.split("/")[:-1])
output_graph = output_filename
# We clear devices to allow TensorFlow to control on which device it will load operations
clear_devices = True
# We start a session using a temporary fresh Graph
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
# We import the meta graph in the current default Graph
saver = tf.compat.v1.train.import_meta_graph(
input_checkpoint + ".meta", clear_devices=clear_devices
)
# We restore the weights
saver.restore(sess, input_checkpoint)
# We use a built-in TF helper to export variables to constants
output_graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
tf.compat.v1.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes
output_node_names.split(
","
), # The output node names are used to select the usefull nodes
)
# Finally we serialize and dump the output graph to the filesystem
with tf.compat.v1.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))
return output_graph_def
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-dir", type=str, default="", help="Model folder to export"
)
parser.add_argument(
"--output-node-names",
type=str,
default="vocals_spectrogram/mul,accompaniment_spectrogram/mul",
help="The name of the output nodes, comma separated.",
)
parser.add_argument(
"--output-filename",
type=str,
)
args = parser.parse_args()
freeze_graph(args.model_dir, args.output_node_names, args.output_filename)
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