# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""

.. _l-example-profiling:

Profile the execution of a simple model
=======================================

*ONNX Runtime* can profile the execution of the model.
This example shows how to interpret the results.
"""

import onnxruntime as rt
import numpy
from onnxruntime.datasets import get_example

#########################
# Let's load a very simple model and compute some prediction.

example1 = get_example("mul_1.pb")
sess = rt.InferenceSession(example1)
input_name = sess.get_inputs()[0].name

x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
res = sess.run(None, {input_name: x})
print(res)

#########################
# We need to enable to profiling
# before running the predictions.

options = rt.SessionOptions()
options.enable_profiling = True
sess_profile = rt.InferenceSession(example1, options)
input_name = sess.get_inputs()[0].name

x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)

sess.run(None, {input_name: x})
prof_file = sess_profile.end_profiling()
print(prof_file)

###########################
# The results are stored un a file in JSON format.
# Let's see what it contains.
import json
with open(prof_file, "r") as f:
    sess_time = json.load(f)
import pprint
pprint.pprint(sess_time)