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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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< div class = "sphx-glr-example-title section" id = "load-and-predict-with-onnx-runtime-and-a-very-simple-model" >
< 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 headline" > ¶< / a > < / h1 >
< 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" > onnxruntime< / span > < span class = "k" > as< / span > < span class = "nn" > rt< / span >
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< span class = "kn" > import< / span > < span class = "nn" > numpy< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnxruntime.datasets< / span > < span class = "kn" > import< / span > < span class = "n" > get_example< / span >
< / pre > < / div >
< / div >
< p > Let’
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 >
< / 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 >
< p class = "sphx-glr-script-out" > Out:< / p >
< 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 >
< p class = "sphx-glr-script-out" > Out:< / p >
< 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 >
< 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 >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [array([[[0.5723026 , 0.63803464, 0.6668191 , 0.5958905 , 0.6193227 ],
[0.72006834, 0.6733471 , 0.69727564, 0.677417 , 0.54019606],
[0.6529879 , 0.6253395 , 0.6622766 , 0.7127938 , 0.5429604 ],
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[[0.7272017 , 0.6749091 , 0.6320263 , 0.53652936, 0.5730977 ],
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[[0.71763974, 0.6305131 , 0.67285264, 0.61491245, 0.62528753],
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[0.51676244, 0.6735578 , 0.54448766, 0.64972466, 0.66511655]]],
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dtype=float32)]
< / pre > < / div >
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< p > < a class = "reference download internal" download = "" href = "../downloads/4412ae3bda7068f45094acb5373c790e/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/dee2ae82948a521867a372a6b9515393/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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