export_saved_model.py

#!/usr/bin/python
# -*- coding: utf-8 -*-
# pylint: disable=C0103
# pylint: disable=E1101

import sys
import time
import numpy as np
import tensorflow as tf
import cv2

sys.path.append("..")

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = './model/frozen_inference_graph_face.pb'

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

with detection_graph.as_default():
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    with tf.Session(graph=detection_graph, config=config) as sess:

        # the array based representation of the image will be used later in order to prepare the
        # result image with boxes and labels on it.
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')

        tf.saved_model.simple_save(
            sess,
            '/var/models/face/1',
            inputs={
                'image': image_tensor,
            },
            outputs={
                'boxes': boxes,
                'scores': scores,
                'classes': classes,
                'num_detections': num_detections,
            },
        )