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https://github.com/saymrwulf/onnxruntime.git
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55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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.. _l-example-profiling:
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Profile the execution of a simple model
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=======================================
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*ONNX Runtime* can profile the execution of the model.
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This example shows how to interpret the results.
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"""
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import onnxruntime as rt
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import numpy
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from onnxruntime.datasets import get_example
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#########################
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# Let's load a very simple model and compute some prediction.
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example1 = get_example("mul_1.onnx")
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sess = rt.InferenceSession(example1)
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input_name = sess.get_inputs()[0].name
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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res = sess.run(None, {input_name: x})
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print(res)
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#########################
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# We need to enable to profiling
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# before running the predictions.
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options = rt.SessionOptions()
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options.enable_profiling = True
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sess_profile = rt.InferenceSession(example1, options)
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input_name = sess.get_inputs()[0].name
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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sess.run(None, {input_name: x})
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prof_file = sess_profile.end_profiling()
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print(prof_file)
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###########################
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# The results are stored un a file in JSON format.
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# Let's see what it contains.
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import json
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with open(prof_file, "r") as f:
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sess_time = json.load(f)
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import pprint
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pprint.pprint(sess_time)
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