2018-11-19 16:48:22 -08:00
|
|
|
# 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.
|
|
|
|
|
"""
|
2025-01-16 11:14:15 -08:00
|
|
|
|
2022-04-26 09:35:16 -07:00
|
|
|
import numpy
|
2020-04-01 22:48:32 +02:00
|
|
|
import onnx
|
2022-04-26 09:35:16 -07:00
|
|
|
|
2018-11-19 16:48:22 -08:00
|
|
|
import onnxruntime as rt
|
|
|
|
|
from onnxruntime.datasets import get_example
|
|
|
|
|
|
2020-04-01 22:48:32 +02:00
|
|
|
|
|
|
|
|
def change_ir_version(filename, ir_version=6):
|
|
|
|
|
"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"
|
|
|
|
|
with open(filename, "rb") as f:
|
|
|
|
|
model = onnx.load(f)
|
|
|
|
|
model.ir_version = 6
|
|
|
|
|
if model.opset_import[0].version <= 7:
|
|
|
|
|
model.opset_import[0].version = 11
|
|
|
|
|
return model
|
|
|
|
|
|
|
|
|
|
|
2018-11-19 16:48:22 -08:00
|
|
|
#########################
|
|
|
|
|
# Let's load a very simple model and compute some prediction.
|
|
|
|
|
|
2019-07-04 13:10:29 +10:00
|
|
|
example1 = get_example("mul_1.onnx")
|
2020-04-01 22:48:32 +02:00
|
|
|
onnx_model = change_ir_version(example1)
|
|
|
|
|
onnx_model_str = onnx_model.SerializeToString()
|
2021-11-30 15:26:10 -08:00
|
|
|
sess = rt.InferenceSession(onnx_model_str, providers=rt.get_available_providers())
|
2018-11-19 16:48:22 -08:00
|
|
|
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
|
2021-11-30 15:26:10 -08:00
|
|
|
sess_profile = rt.InferenceSession(onnx_model_str, options, providers=rt.get_available_providers())
|
2018-11-19 16:48:22 -08:00
|
|
|
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.
|
2023-03-24 15:29:03 -07:00
|
|
|
import json # noqa: E402
|
2022-04-26 09:35:16 -07:00
|
|
|
|
2023-03-24 15:29:03 -07:00
|
|
|
with open(prof_file) as f:
|
2018-11-19 16:48:22 -08:00
|
|
|
sess_time = json.load(f)
|
2023-03-24 15:29:03 -07:00
|
|
|
import pprint # noqa: E402
|
2018-11-19 16:48:22 -08:00
|
|
|
|
2022-04-26 09:35:16 -07:00
|
|
|
pprint.pprint(sess_time)
|