import os
import argparse
import sys
import logging
try:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
except ImportError:
import tensorflow as tf
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import ops
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger(__name__)
[docs]def freeze_model(model_folder,
output,
output_node_names="o_energy,o_forces"):
# 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
absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1])
output_graph = absolute_model_folder + "/" + output
# 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 = "energy_test,force_test,virial_test,t_rcut"
# 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())
logger.debug("%d ops in the final graph." % len(output_graph_def.node))
if __name__ == '__main__':
default_frozen_nodes = "o_energy,o_forces"
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--folder", type=str, default=".",
help="path to checkpoint folder")
parser.add_argument("-o", "--output", type=str, default="frozen_model.pb",
help="name of graph, will output to the checkpoint folder")
parser.add_argument("-n", "--nodes", type=str, default=default_frozen_nodes,
help="the frozen nodes, defaults is " + default_frozen_nodes)
args = parser.parse_args()
freeze_model(args.folder, args.output, args.nodes)