mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-16 18:31:27 +00:00
265 lines
7 KiB
ReStructuredText
265 lines
7 KiB
ReStructuredText
.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_common_errors.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_common_errors.py:
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.. _l-example-common-error:
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Common errors with onnxruntime
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==============================
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This example looks into several common situations
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in which *onnxruntime* does not return the model
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prediction but raises an exception instead.
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It starts by loading the model trained in example
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:ref:`l-logreg-example` which produced a logistic regression
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trained on *Iris* datasets. The model takes
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a vector of dimension 2 and returns a class among three.
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.. code-block:: python
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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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example2 = get_example("logreg_iris.onnx")
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sess = rt.InferenceSession(example2)
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input_name = sess.get_inputs()[0].name
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output_name = sess.get_outputs()[0].name
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The first example fails due to *bad types*.
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*onnxruntime* only expects single floats (4 bytes)
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and cannot handle any other kind of floats.
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.. code-block:: python
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try:
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float64)
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sess.run([output_name], {input_name: x})
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except Exception as e:
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print("Unexpected type")
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print("{0}: {1}".format(type(e), e))
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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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Unexpected type
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<class 'RuntimeError'>: Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (class onnxruntime::NonOnnxType<double>) , expected: (class onnxruntime::NonOnnxType<float>)
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The model fails to return an output if the name
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is misspelled.
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.. code-block:: python
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try:
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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(["misspelled"], {input_name: x})
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except Exception as e:
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print("Misspelled output name")
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print("{0}: {1}".format(type(e), e))
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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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Misspelled output name
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<class 'RuntimeError'>: Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Output Names: misspelled Valid output names are: label probabilities
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The output name is optional, it can be replaced by *None*
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and *onnxruntime* will then return all the outputs.
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.. code-block:: python
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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("All outputs")
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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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All outputs
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[array([0, 0, 0], dtype=int64), [{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654}, {0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846}, {0: 0.9997311234474182, 1: 1.1918064757310276e-07, 2: 0.00026869276189245284}]]
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The same goes if the input name is misspelled.
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.. code-block:: python
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try:
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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([output_name], {"misspelled": x})
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except Exception as e:
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print("Misspelled input name")
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print("{0}: {1}".format(type(e), e))
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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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Misspelled input name
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<class 'RuntimeError'>: Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Missing required inputs: float_input
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*onnxruntime* does not necessarily fail if the input
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dimension is a multiple of the expected input dimension.
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.. code-block:: python
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for x in [
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numpy.array([1.0, 2.0, 3.0, 4.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0, 4.0]], dtype=numpy.float32),
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numpy.array([[1.0, 2.0], [3.0, 4.0]], dtype=numpy.float32),
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numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
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]:
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r = sess.run([output_name], {input_name: x})
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print("Shape={0} and predicted labels={1}".format(x.shape, r))
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for x in [
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numpy.array([1.0, 2.0, 3.0, 4.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0, 4.0]], dtype=numpy.float32),
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numpy.array([[1.0, 2.0], [3.0, 4.0]], dtype=numpy.float32),
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numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
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]:
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r = sess.run(None, {input_name: x})
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print("Shape={0} and predicted probabilities={1}".format(x.shape, r[1]))
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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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Shape=(4,) and predicted labels=[array([2], dtype=int64)]
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Shape=(1, 4) and predicted labels=[array([2], dtype=int64)]
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Shape=(2, 2) and predicted labels=[array([0, 0], dtype=int64)]
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Shape=(3,) and predicted labels=[array([0], dtype=int64)]
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Shape=(1, 3) and predicted labels=[array([0], dtype=int64)]
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Shape=(4,) and predicted probabilities=[{0: 0.0009370420593768358, 1: 0.001740509644150734, 2: 0.9973224401473999}]
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Shape=(1, 4) and predicted probabilities=[{0: 0.0009370420593768358, 1: 0.001740509644150734, 2: 0.9973224401473999}]
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Shape=(2, 2) and predicted probabilities=[{0: 0.950599730014801, 1: 0.027834169566631317, 2: 0.02156602405011654}, {0: 0.9974970817565918, 1: 5.6299926654901356e-05, 2: 0.0024466661270707846}]
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Shape=(3,) and predicted probabilities=[{0: 0.7892322540283203, 1: 0.20707039535045624, 2: 0.0036973499227315187}]
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Shape=(1, 3) and predicted probabilities=[{0: 0.7892322540283203, 1: 0.20707039535045624, 2: 0.0036973499227315187}]
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It does not fail either if the number of dimension
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is higher than expects but produces a warning.
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.. code-block:: python
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for x in [
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numpy.array([[[1.0, 2.0], [3.0, 4.0]]], dtype=numpy.float32),
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numpy.array([[[1.0, 2.0, 3.0]]], dtype=numpy.float32),
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numpy.array([[[1.0, 2.0]], [[3.0, 4.0]]], dtype=numpy.float32),
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]:
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r = sess.run([output_name], {input_name: x})
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print("Shape={0} and predicted labels={1}".format(x.shape, r))
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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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Shape=(1, 2, 2) and predicted labels=[array([0], dtype=int64)]
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Shape=(1, 1, 3) and predicted labels=[array([1], dtype=int64)]
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Shape=(2, 1, 2) and predicted labels=[array([1, 1], dtype=int64)]
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**Total running time of the script:** ( 0 minutes 0.013 seconds)
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.. _sphx_glr_download_auto_examples_plot_common_errors.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
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:download:`Download Python source code: plot_common_errors.py <plot_common_errors.py>`
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.. container:: sphx-glr-download
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:download:`Download Jupyter notebook: plot_common_errors.ipynb <plot_common_errors.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.readthedocs.io>`_
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