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https://github.com/saymrwulf/onnxruntime.git
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e210853b0c
* Update python documentation * remove two unnecessary files Co-authored-by: xavier dupré <xavier.dupre@gmail.com>
189 lines
3.4 KiB
ReStructuredText
189 lines
3.4 KiB
ReStructuredText
.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_profiling.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_profiling.py:
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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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.. code-block:: default
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import onnx
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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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def change_ir_version(filename, ir_version=6):
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"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"
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with open(filename, "rb") as f:
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model = onnx.load(f)
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model.ir_version = 6
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if model.opset_import[0].version <= 7:
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model.opset_import[0].version = 11
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return model
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Let's load a very simple model and compute some prediction.
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.. code-block:: default
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example1 = get_example("mul_1.onnx")
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onnx_model = change_ir_version(example1)
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onnx_model_str = onnx_model.SerializeToString()
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sess = rt.InferenceSession(onnx_model_str)
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[array([[ 1., 4.],
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[ 9., 16.],
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[25., 36.]], dtype=float32)]
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We need to enable to profiling
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before running the predictions.
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.. code-block:: default
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options = rt.SessionOptions()
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options.enable_profiling = True
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sess_profile = rt.InferenceSession(onnx_model_str, 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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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onnxruntime_profile__2020-12-03_00-54-48.json
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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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.. code-block:: default
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[{'args': {},
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'cat': 'Session',
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'dur': 205,
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'name': 'model_loading_array',
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'ph': 'X',
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'pid': 22648,
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'tid': 6000,
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'ts': 6},
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{'args': {},
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'cat': 'Session',
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'dur': 737,
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'name': 'session_initialization',
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'ph': 'X',
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'pid': 22648,
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'tid': 6000,
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'ts': 269}]
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 0.038 seconds)
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.. _sphx_glr_download_auto_examples_plot_profiling.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: plot_profiling.py <plot_profiling.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_profiling.ipynb <plot_profiling.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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