morgangiraud/medium-tffreeze-1.py Secret

## medium-tffreeze-1.py
import os, argparse

import tensorflow as tf

# The original freeze_graph function
# from tensorflow.python.tools.freeze_graph import freeze_graph

dir = os.path.dirname(os.path.realpath(__file__))

def freeze_graph(model_dir, output_node_names):
    """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
    """
    if not tf.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 = absolute_model_dir + "/frozen_model.pb"

    # 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.Session(graph=tf.Graph()) as sess:
        # We import the meta graph in the current default Graph
        saver = tf.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.graph_util.convert_variables_to_constants(
            sess, # The session is used to retrieve the weights
            tf.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.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="", help="The name of the output nodes, comma separated.")
    args = parser.parse_args()

    freeze_graph(args.model_dir, args.output_node_names)
	import os, argparse

	import tensorflow as tf

	# The original freeze_graph function
	# from tensorflow.python.tools.freeze_graph import freeze_graph

	dir = os.path.dirname(os.path.realpath(__file__))

	def freeze_graph(model_dir, output_node_names):
	"""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
	"""
	if not tf.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 = absolute_model_dir + "/frozen_model.pb"

	# 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.Session(graph=tf.Graph()) as sess:
	# We import the meta graph in the current default Graph
	saver = tf.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.graph_util.convert_variables_to_constants(
	sess, # The session is used to retrieve the weights
	tf.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.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="", help="The name of the output nodes, comma separated.")
	args = parser.parse_args()

	freeze_graph(args.model_dir, args.output_node_names)