tensorflow convert savedmodel to checkpoint

tf_savedmodel_to_checkpoint.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ================================
"""Imports a protobuf model as a graph in Tensorboard."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

import tensorflow as tf
from tensorflow.core.framework import graph_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.platform import app
from tensorflow.python.platform import gfile
from tensorflow.python.summary import summary
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.util import compat

# Try importing TensorRT ops if available
# TODO(aaroey): ideally we should import everything from contrib, but currently
# tensorrt module would cause build errors when being imported in
# tensorflow/contrib/__init__.py. Fix it.
# pylint: disable=unused-import,g-import-not-at-top,wildcard-import
try:
    from tensorflow.contrib.tensorrt.ops.gen_trt_engine_op import *
except ImportError:
    pass


# pylint: enable=unused-import,g-import-not-at-top,wildcard-import

def import_to_tensorboard(model_dir, log_dir):
    """View an imported protobuf model (`.pb` file) as a graph in Tensorboard.

    Args:
      model_dir: The location of the protobuf (`pb`) model to visualize
      log_dir: The location for the Tensorboard log to begin visualization from.

    Usage:
      Call this function with your model location and desired log directory.
      Launch Tensorboard by pointing it to the log directory.
      View your imported `.pb` model as a graph.
    """
    with tf.Session() as sess:
        with gfile.FastGFile(model_dir, 'rb') as f:
            data = compat.as_bytes(f.read())
            sm = saved_model_pb2.SavedModel()
            sm.ParseFromString(data)
            # print(sm)
            if 1 != len(sm.meta_graphs):
                print('More than one graph found. Not sure which to write')
                sys.exit(1)

            tf.import_graph_def(sm.meta_graphs[0].graph_def, name='')

    train_writer = tf.summary.FileWriter(log_dir)
    train_writer.add_graph(sess.graph)
    print("Model Imported. Visualize by running: "
          "tensorboard --logdir={}".format(log_dir))


def main(unused_args):
    import_to_tensorboard(FLAGS.model_dir, FLAGS.log_dir)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.register("type", "bool", lambda v: v.lower() == "true")
    parser.add_argument(
        "--model_dir",
        type=str,
        default="",
        required=True,
        help="The location of the protobuf (\'pb\') model to visualize.")
    parser.add_argument(
        "--log_dir",
        type=str,
        default="",
        required=True,
        help="The location for the Tensorboard log to begin visualization from.")
    FLAGS, unparsed = parser.parse_known_args()
    app.run(main=main, argv=[sys.argv[0]] + unparsed)