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< title > ONNX Runtime Backend for ONNX — ONNX Runtime 1.2.0 documentation< / title >
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< div class = "sphx-glr-download-link-note admonition note" >
< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-backend-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< div class = "sphx-glr-example-title section" id = "onnx-runtime-backend-for-onnx" >
< span id = "l-example-backend-api" > < / span > < span id = "sphx-glr-auto-examples-plot-backend-py" > < / span > < h1 > ONNX Runtime Backend for ONNX< a class = "headerlink" href = "#onnx-runtime-backend-for-onnx" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > < em > ONNX Runtime< / em > extends the
< a class = "reference external" href = "https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md" > onnx backend API< / a >
to run predictions using this runtime.
Let’
of a simple logistic regression model.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "kn" > as< / span > < span class = "nn" > np< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnxruntime< / span > < span class = "kn" > import< / span > < span class = "n" > datasets< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnxruntime.capi.onnxruntime_pybind11_state< / span > < span class = "kn" > import< / span > < span class = "n" > InvalidArgument< / span >
< span class = "kn" > import< / span > < span class = "nn" > onnxruntime.backend< / span > < span class = "kn" > as< / span > < span class = "nn" > backend< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnx< / span > < span class = "kn" > import< / span > < span class = "n" > load< / span >
< span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > )< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > )< / span >
< span class = "n" > rep< / span > < span class = "o" > =< / span > < span class = "n" > backend< / span > < span class = "o" > .< / span > < span class = "n" > prepare< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "s1" > ' CPU' < / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "o" > -< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "k" > try< / span > < span class = "p" > :< / span >
< span class = "n" > label< / span > < span class = "p" > ,< / span > < span class = "n" > proba< / span > < span class = "o" > =< / span > < span class = "n" > rep< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " label={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > label< / span > < span class = "p" > ))< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " probabilities={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > proba< / span > < span class = "p" > ))< / span >
< span class = "k" > except< / span > < span class = "p" > (< / span > < span class = "ne" > RuntimeError< / span > < span class = "p" > ,< / span > < span class = "n" > InvalidArgument< / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > e< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
index: 0 Got: 1 Expected: 3
Please fix either the inputs or the model.
< / pre > < / div >
< / div >
< p > The device depends on how the package was compiled,
GPU or CPU.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > onnxruntime< / span > < span class = "kn" > import< / span > < span class = "n" > get_device< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > get_device< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > CPU
< / pre > < / div >
< / div >
< p > The backend can also directly load the model
without using < em > onnx< / em > .< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > rep< / span > < span class = "o" > =< / span > < span class = "n" > backend< / span > < span class = "o" > .< / span > < span class = "n" > prepare< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "s1" > ' CPU' < / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "o" > -< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "k" > try< / span > < span class = "p" > :< / span >
< span class = "n" > label< / span > < span class = "p" > ,< / span > < span class = "n" > proba< / span > < span class = "o" > =< / span > < span class = "n" > rep< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " label={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > label< / span > < span class = "p" > ))< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " probabilities={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > proba< / span > < span class = "p" > ))< / span >
< span class = "k" > except< / span > < span class = "p" > (< / span > < span class = "ne" > RuntimeError< / span > < span class = "p" > ,< / span > < span class = "n" > InvalidArgument< / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > e< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
index: 0 Got: 1 Expected: 3
Please fix either the inputs or the model.
< / pre > < / div >
< / div >
< p > The backend API is implemented by other frameworks
and makes it easier to switch between multiple runtimes
with the same API.< / p >
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< p > < strong > Total running time of the script:< / strong > ( 0 minutes 0.025 seconds)< / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/df88a32237a9b3e764a8da54c1743145/plot_backend.py" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Python< / span > < span class = "pre" > source< / span > < span class = "pre" > code:< / span > < span class = "pre" > plot_backend.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/594159f58c2d97bee3f22ee659067d5a/plot_backend.ipynb" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Jupyter< / span > < span class = "pre" > notebook:< / span > < span class = "pre" > plot_backend.ipynb< / span > < / code > < / a > < / p >
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