NVIDIA-AI-Image-Classification/scripts/models_to_frozen_graphs.py

# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
# Full license terms provided in LICENSE.md file.

import tensorflow as tf
import sys
sys.path.append("third_party/models/research/")
sys.path.append("third_party/models")
sys.path.append("third_party/")
sys.path.append("third_party/models/research/slim/")
sys.path.append("scripts")
import tensorflow.contrib.slim as tf_slim
import slim.nets as nets
import slim.nets.vgg
from model_meta import NETS, CHECKPOINT_DIR, FROZEN_GRAPHS_DIR
from convert_relu6 import convertRelu6
import os



if __name__ == '__main__':

    if not os.path.exists(CHECKPOINT_DIR):
        print("%s does not exist.  Exiting." % CHECKPOINT_DIR)
        exit()

    if not os.path.exists(FROZEN_GRAPHS_DIR):
        print("%s does not exist.  Creating it now." % FROZEN_GRAPHS_DIR)
        os.makedirs(FROZEN_GRAPHS_DIR)

    for net_name, net_meta in NETS.items():

        if 'exclude' in net_meta.keys() and net_meta['exclude'] is True:
            continue

        print("Converting %s" % net_name)
        print(net_meta)

        tf.reset_default_graph()
        tf_config = tf.ConfigProto()
        tf_config.gpu_options.allow_growth = True
        tf_sess = tf.Session(config=tf_config)
        tf_input = tf.placeholder(
            tf.float32, 
            (
                None, 
                net_meta['input_height'], 
                net_meta['input_width'], 
                net_meta['input_channels']
            ),
            name=net_meta['input_name']
        )

        with tf_slim.arg_scope(net_meta['arg_scope']()):
            tf_net, tf_end_points = net_meta['model'](
                tf_input, 
                is_training=False,
                num_classes=net_meta['num_classes']
            )

        tf_saver = tf.train.Saver()
        tf_saver.restore(
            save_path=net_meta['checkpoint_filename'], 
            sess=tf_sess
        )
        frozen_graph = tf.graph_util.convert_variables_to_constants(
            tf_sess,
            tf_sess.graph_def,
            output_node_names=net_meta['output_names']
        )

        frozen_graph = convertRelu6(frozen_graph)

        with open(net_meta['frozen_graph_filename'], 'wb') as f:
            f.write(frozen_graph.SerializeToString())
    
        f.close()

        del tf_config
        del tf_sess
        del tf_input
        del tf_saver
        del frozen_graph