Import TensorFlow SavedModel to TensorBoard

import_savedmodel_to_tensorboard.py

"""Imports a SavedModel as a graph in Tensorboard."""

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

import argparse
import sys

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 import saved_model

# 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 SavedModel model as a graph in Tensorboard.

    Args:
      model_dir: The location of the SavedModel 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 SavedModel model as a graph.
    """
    with session.Session(graph=ops.Graph()) as sess:
        # Restore model from the saved_model file, that is exported by TensorFlow estimator.
        saved_model.loader.load(sess, ["serve"], model_dir)
        pb_visual_writer = summary.FileWriter(log_dir)
        pb_visual_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 SavedModel 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)

Hey - thanks for sharing. Do you have time to post a TF2 script for this same purpose?