uff_parser_enet.py

import os, argparse

import tensorflow as tf
from tensorflow.python.framework import graph_util
import tensorrt as trt
from tensorrt.parsers import uffparser
import uff

## Usage:  python uff_parser_enet.py --model_folder ./checkpoint

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

def freeze_graph(model_folder):
    # We retrieve our checkpoint fullpath
    checkpoint = tf.train.get_checkpoint_state(model_folder)
    input_checkpoint = checkpoint.model_checkpoint_path
    
    # the file fullname of our freezed graph
    absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1])
    output_graph = absolute_model_folder + "/frozen_model.pb"

    # Before exporting our graph, we need to define what is our output node
    # This is how TF decides what part of the Graph has to be kept and what part it can dump
    # NOTE: this variable is plural, because you can have multiple output nodes
    output_node_names = "ENet/logits_to_softmax"

    # 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 
            output_node_names.split(",") # The output node names are used to select the usefull nodes
        ) 

        # Finally we serialize and dump the frozen 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 tf.graph_util.remove_training_nodes(output_graph_def)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_folder", type=str, help="Model folder to export")
    args = parser.parse_args()

    tf_model = freeze_graph(args.model_folder)
    uff_model = uff.from_tensorflow(tf_model, ["ENet/logits_to_softmax"])