convert.py

import os, argparse
import tensorflow.contrib.tensorrt as trt
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')
 
        # 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
        )

        trt_graph = trt.create_inference_graph(
            input_graph_def=output_graph_def,
            outputs=['result/ArgMax:0'],
            max_batch_size=1,
            max_workspace_size_bytes=4<<25,
            precision_mode='FP16',
            minimum_segment_size=10
        ) 

        tf.import_graph_def(
            graph_def=trt_graph,
            return_elements=['result/ArgMax'],
        )

        with tf.gfile.GFile('./tensorrt/frozen_model.pb', 'wb') as f:
            f.write(trt_graph.SerializeToString())
        # 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="./tensorboard", help="Model folder to export")
    parser.add_argument("--output_node_names", type=str, default="result/ArgMax", help="The name of the output nodes, comma separated.")
    args = parser.parse_args()

freeze_graph(args.model_dir, args.output_node_names)