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< title > Train, convert and predict with ONNX Runtime< / title >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../tutorial.html" > Tutorial< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-1-train-a-model-using-your-favorite-framework" > Step 1: Train a model using your favorite framework< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format" > Step 2: Convert or export the model into ONNX format< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime" > Step 3: Load and run the model using ONNX Runtime< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../api_summary.html" > API Summary< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#device" > Device< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#examples-and-datasets" > Examples and datasets< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#load-and-run-a-model" > Load and run a model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#backend" > Backend< / a > < / li >
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< li class = "toctree-l1 current" > < a class = "reference internal" href = "index.html" > Gallery of examples< / a > < ul class = "current" >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_backend.html" > ONNX Runtime Backend for ONNX< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_pipeline.html" > Draw a pipeline< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_load_and_predict.html" > Load and predict with ONNX Runtime and a very simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_profiling.html" > Profile the execution of a simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_metadata.html" > Metadata< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_dl_keras.html" > ONNX Runtime for Keras< / a > < / li >
< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > Train, convert and predict with ONNX Runtime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_common_errors.html" > Common errors with onnxruntime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_train_convert_predict.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
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< div class = "sphx-glr-download-link-note admonition note" >
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< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-convert-pipeline-vectorizer-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< / div >
< div class = "sphx-glr-example-title section" id = "train-convert-and-predict-with-onnx-runtime" >
< span id = "sphx-glr-auto-examples-plot-convert-pipeline-vectorizer-py" > < / span > < h1 > Train, convert and predict with ONNX Runtime< a class = "headerlink" href = "#train-convert-and-predict-with-onnx-runtime" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This example demonstrates an end to end scenario
starting with the training of a scikit-learn pipeline
which takes as inputs not a regular vector but a
dictionary < code class = "docutils literal notranslate" > < span class = "pre" > {< / span > < span class = "pre" > int:< / span > < span class = "pre" > float< / span > < span class = "pre" > }< / span > < / code > as its first step is a
< a class = "reference external" href = "http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html" > DictVectorizer< / a > .< / p >
< div class = "contents local topic" id = "contents" >
< ul class = "simple" >
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< li > < p > < a class = "reference internal" href = "#train-a-pipeline" id = "id1" > Train a pipeline< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#conversion-to-onnx-format" id = "id2" > Conversion to ONNX format< / a > < / p > < / li >
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< / ul >
< / div >
< div class = "section" id = "train-a-pipeline" >
< h2 > < a class = "toc-backref" href = "#id1" > Train a pipeline< / a > < a class = "headerlink" href = "#train-a-pipeline" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > The first step consists in retrieving the boston datset.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > pandas< / span >
< span class = "kn" > from< / span > < span class = "nn" > sklearn.datasets< / span > < span class = "k" > import< / span > < span class = "n" > load_boston< / span >
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< span class = "n" > boston< / span > < span class = "o" > =< / span > < span class = "n" > load_boston< / span > < span class = "p" > ()< / span >
< span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > boston< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "p" > ,< / span > < span class = "n" > boston< / span > < span class = "o" > .< / span > < span class = "n" > target< / span >
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< span class = "kn" > from< / span > < span class = "nn" > sklearn.model_selection< / span > < span class = "k" > import< / span > < span class = "n" > train_test_split< / span >
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< span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_test< / span > < span class = "o" > =< / span > < span class = "n" > train_test_split< / span > < span class = "p" > (< / span > < span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< span class = "n" > X_train_dict< / span > < span class = "o" > =< / span > < span class = "n" > pandas< / span > < span class = "o" > .< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > [:,< / span > < span class = "mi" > 1< / span > < span class = "p" > :])< / span > < span class = "o" > .< / span > < span class = "n" > T< / span > < span class = "o" > .< / span > < span class = "n" > to_dict< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "n" > values< / span > < span class = "p" > ()< / span >
< span class = "n" > X_test_dict< / span > < span class = "o" > =< / span > < span class = "n" > pandas< / span > < span class = "o" > .< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > [:,< / span > < span class = "mi" > 1< / span > < span class = "p" > :])< / span > < span class = "o" > .< / span > < span class = "n" > T< / span > < span class = "o" > .< / span > < span class = "n" > to_dict< / span > < span class = "p" > ()< / span > < span class = "o" > .< / span > < span class = "n" > values< / span > < span class = "p" > ()< / span >
< / pre > < / div >
< / div >
< p > We create a pipeline.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.pipeline< / span > < span class = "k" > import< / span > < span class = "n" > make_pipeline< / span >
< span class = "kn" > from< / span > < span class = "nn" > sklearn.ensemble< / span > < span class = "k" > import< / span > < span class = "n" > GradientBoostingRegressor< / span >
< span class = "kn" > from< / span > < span class = "nn" > sklearn.feature_extraction< / span > < span class = "k" > import< / span > < span class = "n" > DictVectorizer< / span >
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< span class = "n" > pipe< / span > < span class = "o" > =< / span > < span class = "n" > make_pipeline< / span > < span class = "p" > (< / span >
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< span class = "n" > DictVectorizer< / span > < span class = "p" > (< / span > < span class = "n" > sparse< / span > < span class = "o" > =< / span > < span class = "kc" > False< / span > < span class = "p" > ),< / span >
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< span class = "n" > GradientBoostingRegressor< / span > < span class = "p" > ())< / span >
< span class = "n" > pipe< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train_dict< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > We compute the prediction on the test set
and we show the confusion matrix.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.metrics< / span > < span class = "k" > import< / span > < span class = "n" > r2_score< / span >
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< span class = "n" > pred< / span > < span class = "o" > =< / span > < span class = "n" > pipe< / span > < span class = "o" > .< / span > < span class = "n" > predict< / span > < span class = "p" > (< / span > < span class = "n" > X_test_dict< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > r2_score< / span > < span class = "p" > (< / span > < span class = "n" > y_test< / span > < span class = "p" > ,< / span > < span class = "n" > pred< / 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 > 0.9213393016925404
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< / pre > < / div >
< / div >
< / div >
< div class = "section" id = "conversion-to-onnx-format" >
< h2 > < a class = "toc-backref" href = "#id2" > Conversion to ONNX format< / a > < a class = "headerlink" href = "#conversion-to-onnx-format" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We use module
< a class = "reference external" href = "https://github.com/onnx/sklearn-onnx" > sklearn-onnx< / a >
to convert the model into ONNX format.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > skl2onnx< / span > < span class = "k" > import< / span > < span class = "n" > convert_sklearn< / span >
< span class = "kn" > from< / span > < span class = "nn" > skl2onnx.common.data_types< / span > < span class = "k" > import< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ,< / span > < span class = "n" > Int64TensorType< / span > < span class = "p" > ,< / span > < span class = "n" > DictionaryType< / span > < span class = "p" > ,< / span > < span class = "n" > SequenceType< / span >
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< span class = "c1" > # initial_type = [(' float_input' , DictionaryType(Int64TensorType([1]), FloatTensorType([])))]< / span >
< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > DictionaryType< / span > < span class = "p" > (< / span > < span class = "n" > Int64TensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ]),< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([])))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > pipe< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " pipeline_vectorize.onnx" < / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
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< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > The maximum opset needed by this model is only 1.
< / pre > < / div >
< / div >
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< p > We load the model with ONNX Runtime and look at
its input and output.< / 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 = "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 = "s2" > " pipeline_vectorize.onnx" < / span > < span class = "p" > )< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span >
< span class = "n" > inp< / span > < span class = "p" > ,< / span > < span class = "n" > out< / 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 = "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 >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > and type=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > inp< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > inp< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ,< / span > < span class = "n" > inp< / span > < span class = "o" > .< / span > < span class = "n" > type< / span > < span class = "p" > ))< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > and type=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > out< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > out< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ,< / span > < span class = "n" > out< / span > < span class = "o" > .< / span > < span class = "n" > 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=' float_input' and shape=[] and type=map(int64,tensor(float))
output name=' variable1' and shape=[1, 1] and type=tensor(float)
< / pre > < / div >
< / div >
< p > We compute the predictions.
We could do that in one call:< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > try< / span > < span class = "p" > :< / span >
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< span class = "n" > pred_onx< / 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" > out< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > inp< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > :< / span > < span class = "n" > X_test_dict< / span > < span class = "p" > })[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > except< / span > < span class = "ne" > RuntimeError< / span > < span class = "k" > as< / span > < span class = "n" > e< / span > < span class = "p" > :< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > e< / 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 > Method run failed due to: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (class onnxruntime::SequenceType< class std::vector< class std::map< __int64,float,struct std::less< __int64> ,class std::allocator< struct std::pair< __int64 const ,float> > > ,class std::allocator< class std::map< __int64,float,struct std::less< __int64> ,class std::allocator< struct std::pair< __int64 const ,float> > > > > > ) , expected: (class onnxruntime::MapType< class std::map< __int64,float,struct std::less< __int64> ,class std::allocator< struct std::pair< __int64 const ,float> > > > )
< / pre > < / div >
< / div >
< p > But it fails because, in case of a DictVectorizer,
ONNX Runtime expects one observation at a time.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > pred_onx< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > out< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > inp< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > :< / span > < span class = "n" > row< / span > < span class = "p" > })[< / span > < span class = "mi" > 0< / span > < span class = "p" > ][< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "k" > for< / span > < span class = "n" > row< / span > < span class = "ow" > in< / span > < span class = "n" > X_test_dict< / span > < span class = "p" > ]< / span >
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< / pre > < / div >
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< p > We compare them to the model’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > r2_score< / span > < span class = "p" > (< / span > < span class = "n" > pred< / span > < span class = "p" > ,< / span > < span class = "n" > pred_onx< / 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 > 0.999999999999972
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< / pre > < / div >
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< p > Very similar. < em > ONNX Runtime< / em > uses floats instead of doubles,
that explains the small discrepencies.< / p >
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