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< title > Load and predict with ONNX Runtime and a very simple model — ONNX Runtime 1.13.0 documentation< / title >
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< p class = "admonition-title" > Note< / p >
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< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-load-and-predict-py" > < span class = "std std-ref" > here< / span > < / a >
to download the full example code< / p >
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< section class = "sphx-glr-example-title" id = "load-and-predict-with-onnx-runtime-and-a-very-simple-model" >
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< span id = "l-example-simple-usage" > < / span > < span id = "sphx-glr-auto-examples-plot-load-and-predict-py" > < / span > < h1 > Load and predict with ONNX Runtime and a very simple model< a class = "headerlink" href = "#load-and-predict-with-onnx-runtime-and-a-very-simple-model" title = "Permalink to this heading" > ¶< / a > < / h1 >
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< p > This example demonstrates how to load a model and compute
the output for an input vector. It also shows how to
retrieve the definition of its inputs and outputs.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > numpy< / span >
< span class = "kn" > import< / span > < span class = "nn" > onnxruntime< / span > < span class = "k" > as< / span > < span class = "nn" > rt< / span >
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< span class = "kn" > from< / span > < span class = "nn" > onnxruntime.datasets< / span > < span class = "k" > import< / span > < span class = "n" > get_example< / span >
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< / pre > < / div >
< / div >
< p > Let’
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The model is available on github < a class = "reference external" href = "https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid" > onnx…test_sigmoid< / a > .< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > example1< / span > < span class = "o" > =< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " sigmoid.onnx" < / span > < span class = "p" > )< / span >
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< span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "n" > example1< / span > < span class = "p" > ,< / span > < span class = "n" > providers< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > get_available_providers< / span > < span class = "p" > ())< / span >
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< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > input_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name" < / span > < span class = "p" > ,< / span > < span class = "n" > input_name< / span > < span class = "p" > )< / span >
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< span class = "n" > input_shape< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input shape" < / span > < span class = "p" > ,< / span > < span class = "n" > input_shape< / span > < span class = "p" > )< / span >
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< span class = "n" > input_type< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > type< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input type" < / span > < span class = "p" > ,< / span > < span class = "n" > input_type< / span > < span class = "p" > )< / span >
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< / pre > < / div >
< / div >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > input name x
input shape [3, 4, 5]
input type tensor(float)
< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > output_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name" < / span > < span class = "p" > ,< / span > < span class = "n" > output_name< / span > < span class = "p" > )< / span >
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< span class = "n" > output_shape< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output shape" < / span > < span class = "p" > ,< / span > < span class = "n" > output_shape< / span > < span class = "p" > )< / span >
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< span class = "n" > output_type< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > type< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output type" < / span > < span class = "p" > ,< / span > < span class = "n" > output_type< / span > < span class = "p" > )< / span >
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< / pre > < / div >
< / div >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > output name y
output shape [3, 4, 5]
output type tensor(float)
< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > numpy.random< / span >
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< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > random< / span > < span class = "o" > .< / span > < span class = "n" > random< / span > < span class = "p" > ((< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ))< / span >
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< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "n" > res< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > output_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "p" > })< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > res< / span > < span class = "p" > )< / span >
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< / pre > < / div >
< / div >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [array([[[0.5456743 , 0.72649395, 0.58741295, 0.53622043, 0.730873 ],
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dtype=float32)]
< / pre > < / div >
< / div >
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< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 0 minutes 0.010 seconds)< / p >
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< p > < a class = "reference download internal" download = "" href = "../downloads/7c8424f45d0156abd4d0221c65601124/plot_load_and_predict.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_load_and_predict.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../downloads/290d1103c4874727a37c05b400ffb83c/plot_load_and_predict.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_load_and_predict.ipynb< / span > < / code > < / a > < / p >
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