Benchmark Keras model using TensforFlow Benchmark - tensorflow

Question

I'm trying to benchmark the performance in the inference phase of my Keras model build with the TensorFlow backend. I was thinking that the the Tensorflow Benchmark tool was the proper way to go.
I've managed to build and run the example on Desktop with the tensorflow_inception_graph.pb and everything seems to work fine.
What I can't seem to figure out is how to save the Keras model as a proper .pbmodel. I'm able to get the TensorFlow Graph from the Keras model as follows:
import keras.backend as K
K.set_learning_phase(0)
trained_model = function_that_returns_compiled_model()
sess = K.get_session()
sess.graph # This works
# Get the input tensor name for TF Benchmark
trained_model.input
> <tf.Tensor 'input_1:0' shape=(?, 360, 480, 3) dtype=float32>
# Get the output tensor name for TF Benchmark
trained_model.output
> <tf.Tensor 'reshape_2/Reshape:0' shape=(?, 360, 480, 12) dtype=float32>
I've now been trying to save the model in a couple of different ways.
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
model = trained_model
export_path = "path/to/folder" # where to save the exported graph
export_version = 1 # version number (integer)
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
signature = exporter.classification_signature(input_tensor=model.input, scores_tensor=model.output)
model_exporter.init(sess.graph.as_graph_def(), default_graph_signature=signature)
model_exporter.export(export_path, tf.constant(export_version), sess)
Which produces a folder with some files I don't know what to do with.
I would now run the Benchmark tool with something like this
bazel-bin/tensorflow/tools/benchmark/benchmark_model \
--graph=tensorflow/tools/benchmark/what_file.pb \
--input_layer="input_1:0" \
--input_layer_shape="1,360,480,3" \
--input_layer_type="float" \
--output_layer="reshape_2/Reshape:0"
But no matter which file I'm trying to use as the what_file.pb I'm getting a Error during inference: Invalid argument: Session was not created with a graph before Run()!

Answer 1

So I got this to work. Just needed to convert all variables in the tensorflow graph to constants and then save graph definition.
Here's a small example:
import tensorflow as tf
from keras import backend as K
from tensorflow.python.framework import graph_util
K.set_learning_phase(0)
model = function_that_returns_your_keras_model()
sess = K.get_session()
output_node_name = "my_output_node" # Name of your output node
with sess as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
graph_def = sess.graph.as_graph_def()
output_graph_def = graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(),
output_node_name.split(","))
tf.train.write_graph(output_graph_def,
logdir="my_dir",
name="my_model.pb",
as_text=False)
Now just call the TensorFlow Benchmark tool with my_model.pb as the graph.

Answer 2

You're saving the parameters of this model and not the graph definition; to save that use tf.get_default_graph().as_graph_def().SerializeToString() and then save that to a file.
That said I don't think the benchmark tool will work since it has no way to initialize the variables your model depends on.