Convert a frozen tensorflow model to Tensorflow serving format

convert_pb_to_tf_serving.py

import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
import argparse


export_dir = 'models/1'
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)


def convertFrozenModel(model_protobuf):
    with tf.gfile.GFile(model_protobuf, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    with tf.Session(graph=tf.Graph()) as sess:
        tf.import_graph_def(graph_def, input_map=None, return_elements=None, name="")
        graph = tf.get_default_graph()

        predTensor = graph.get_tensor_by_name("ImageTensor:0") 
        inputTensor = graph.get_tensor_by_name("SemanticPredictions:0")

        tensor_input = tf.saved_model.utils.build_tensor_info(inputTensor)
        tensorf_output = tf.saved_model.utils.build_tensor_info(predTensor)


        pred_signature = (
            tf.saved_model.signature_def_utils.build_signature_def(
                inputs={"input" : tensor_input},
                outputs= {"outputs": tensorf_output},
                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
            )
        )
        builder.add_meta_graph_and_variables(sess, [tag_constants.SERVING], signature_def_map={
            "prediction":pred_signature
        })

        builder.save()



convertFrozenModel("path/to/.pb/file")