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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>
184 lines
3.8 KiB
ReStructuredText
184 lines
3.8 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_load_and_predict.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_load_and_predict.py:
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.. _l-example-simple-usage:
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Load and predict with ONNX Runtime and a very simple model
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==========================================================
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This example demonstrates how to load a model and compute
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the output for an input vector. It also shows how to
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retrieve the definition of its inputs and outputs.
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.. code-block:: default
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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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Let's load a very simple model.
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The model is available on github `onnx...test_sigmoid <https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid>`_.
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.. code-block:: default
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example1 = get_example("sigmoid.onnx")
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sess = rt.InferenceSession(example1)
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Let's see the input name and shape.
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.. code-block:: default
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input_name = sess.get_inputs()[0].name
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print("input name", input_name)
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input_shape = sess.get_inputs()[0].shape
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print("input shape", input_shape)
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input_type = sess.get_inputs()[0].type
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print("input type", input_type)
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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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input name x
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input shape [3, 4, 5]
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input type tensor(float)
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Let's see the output name and shape.
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.. code-block:: default
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output_name = sess.get_outputs()[0].name
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print("output name", output_name)
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output_shape = sess.get_outputs()[0].shape
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print("output shape", output_shape)
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output_type = sess.get_outputs()[0].type
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print("output type", output_type)
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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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output name y
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output shape [3, 4, 5]
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output type tensor(float)
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Let's compute its outputs (or predictions if it is a machine learned model).
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.. code-block:: default
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import numpy.random
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x = numpy.random.random((3,4,5))
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x = x.astype(numpy.float32)
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res = sess.run([output_name], {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([[[0.5723026 , 0.63803464, 0.6668191 , 0.5958905 , 0.6193227 ],
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[0.72006834, 0.6733471 , 0.69727564, 0.677417 , 0.54019606],
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[0.6529879 , 0.6253395 , 0.6622766 , 0.7127938 , 0.5429604 ],
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[0.604758 , 0.7297679 , 0.5023199 , 0.6422848 , 0.72463864]],
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[[0.7272017 , 0.6749091 , 0.6320263 , 0.53652936, 0.5730977 ],
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[0.5092271 , 0.6188758 , 0.7302063 , 0.6986053 , 0.681966 ],
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[0.71297586, 0.5980871 , 0.50415754, 0.5037554 , 0.555519 ],
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[0.66070724, 0.5136699 , 0.61995924, 0.62644744, 0.53362054]],
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[[0.71763974, 0.6305131 , 0.67285264, 0.61491245, 0.62528753],
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[0.6300376 , 0.5060302 , 0.6701227 , 0.6823867 , 0.6090256 ],
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[0.6845094 , 0.69262683, 0.5350911 , 0.7162322 , 0.6441792 ],
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[0.51676244, 0.6735578 , 0.54448766, 0.64972466, 0.66511655]]],
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dtype=float32)]
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 0.050 seconds)
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.. _sphx_glr_download_auto_examples_plot_load_and_predict.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_load_and_predict.py <plot_load_and_predict.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_load_and_predict.ipynb <plot_load_and_predict.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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