+Saved Model model generation and running

"""

Next is tag for pb (pb == protocol buffer) model

"""

PB_TAG = "model_pb" 

PB_TAGS = ["model_pb"]

print ("Copying config files to results directory:\n ('%s' -> '%s')"%(conf_file,dirs['result']))

try:

except:

    pass

with tf.Session()  as sess:

    # default option

    use_saved_model = False

    if os.path.isdir(dirs['exportdir']):

        # check if dir contains "Saved Model" model

        use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])

    if use_saved_model:

        print("Model restore: using Saved_Model model MetaGraph protocol buffer")

        meta_graph_source = tf.saved_model.loader.load(sess, PB_TAGS, dirs['exportdir'])

    else:

        print("Model restore: using conventionally saved model, but saving Saved Model for the next run")

        meta_graph_source = files["inference"]+'.meta'

        shutil.rmtree(dirs['exportdir'], ignore_errors=True)

        builder = tf.saved_model.builder.SavedModelBuilder(dirs['exportdir'])

with tf.Session()  as sess:

    infer_saver = tf.train.import_meta_graph(files["inference"]+'.meta')

    infer_saver = tf.train.import_meta_graph(meta_graph_source)

    graph=tf.get_default_graph()

    ph_corr2d =           graph.get_tensor_by_name('ph_corr2d:0') 

        """

        image_data[nimg] = None

    # when saved tags can be read with saved_model_cli which is built from tensorflow source using bazel

    builder.add_meta_graph_and_variables(sess,[PB_TAG])

    if not use_saved_model:

        builder.add_meta_graph_and_variables(sess,PB_TAGS)

        builder.save()

    if lf:

        lf.close()

    writer.close()

builder.save()