Tensorflow Serving of Saved Model ssd_mobilenet_v1_coco - tensorflow

Question

I have looked on several posts on stackoverflow and have been at it for a few days now, but alas, I'm not able to properly serve an object detection model through tensorflow serving.
I have visited to the following links:
How to properly serve an object detection model from Tensorflow Object Detection API?
and
https://github.com/tensorflow/tensorflow/issues/11863
Here's what I have done.
I have downloaded the ssd_mobilenet_v1_coco_11_06_2017.tar.gz, which contains the following files:
frozen_inference_graph.pb
graph.pbtxt
model.ckpt.data-00000-of-00001
model.ckpt.index
model.ckpt.meta
Using the following script, I was able successfully convert the frozen_inference_graph.pb to a SavedModel (under directory ssd_mobilenet_v1_coco_11_06_2017/saved)
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
import ipdb
# Specify version 1
export_dir = './saved/1'
graph_pb = 'frozen_inference_graph.pb'
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# name="" is important to ensure we don't get spurious prefixing
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
ipdb.set_trace()
inp = g.get_tensor_by_name("image_tensor:0")
outputs = {}
outputs["detection_boxes"] = g.get_tensor_by_name('detection_boxes:0')
outputs["detection_scores"] = g.get_tensor_by_name('detection_scores:0')
outputs["detection_classes"] = g.get_tensor_by_name('detection_classes:0')
outputs["num_detections"] = g.get_tensor_by_name('num_detections:0')
output_tensor = tf.concat([tf.expand_dims(t, 0) for t in outputs], 0)
# or use tf.gather??
# out = g.get_tensor_by_name("generator/Tanh:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": output_tensor} )
sigs["predict_images"] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": output_tensor} )
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save()
I get the following error:
bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server
--port=9000 --model_base_path=/serving/ssd_mobilenet_v1_coco_11_06_2017/saved
2017-09-17 22:33:21.325087: W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:268] No versions of servable default found under base path /serving/ssd_mobilenet_v1_coco_11_06_2017/saved/1
I understand I will need a client to connect to the server to do the prediction. However, I'm not even able to serve the model properly.

Answer 1

You need to change the export signature somewhat from what the original post did. This script does the necessary changes for you:
$OBJECT_DETECTION_CONFIG=object_detection/samples/configs/ssd_mobilenet_v1_pets.config
$ python object_detection/export_inference_graph.py \ --input_type encoded_image_string_tensor \ --pipeline_config_path ${OBJECT_DETECTION_CONFIG} \ --trained_checkpoint_prefix ${YOUR_LOCAL_CHK_DIR}/model.ckpt-${CHECKPOINT_NUMBER} \ --output_directory ${YOUR_LOCAL_EXPORT_DIR}
For more details on what the program is doing, see:
https://cloud.google.com/blog/big-data/2017/09/performing-prediction-with-tensorflow-object-detection-models-on-google-cloud-machine-learning-engine