2019-12-21 01:11:46 +00:00
<!DOCTYPE html>
< html xmlns = "http://www.w3.org/1999/xhtml" lang = "en" >
< head >
< meta charset = "utf-8" / >
2020-04-06 22:52:48 +00:00
< title > Train, convert and predict with ONNX Runtime — ONNX Runtime 1.2.0 documentation< / title >
2019-12-21 01:11:46 +00:00
< link rel = "stylesheet" href = "../_static/alabaster.css" type = "text/css" / >
< link rel = "stylesheet" href = "../_static/pygments.css" type = "text/css" / >
< link rel = "stylesheet" type = "text/css" href = "../_static/gallery.css" / >
< link rel = "stylesheet" type = "text/css" href = "../_static/graphviz.css" / >
< script type = "text/javascript" id = "documentation_options" data-url_root = "../" src = "../_static/documentation_options.js" > < / script >
< script type = "text/javascript" src = "../_static/jquery.js" > < / script >
< script type = "text/javascript" src = "../_static/underscore.js" > < / script >
< script type = "text/javascript" src = "../_static/doctools.js" > < / script >
< script type = "text/javascript" src = "../_static/language_data.js" > < / script >
< link rel = "index" title = "Index" href = "../genindex.html" / >
< link rel = "search" title = "Search" href = "../search.html" / >
< link rel = "prev" title = "Common errors with onnxruntime" href = "plot_common_errors.html" / >
< link rel = "stylesheet" href = "../_static/custom.css" type = "text/css" / >
< meta name = "viewport" content = "width=device-width, initial-scale=0.9, maximum-scale=0.9" / >
< / head > < body >
< div class = "document" >
< div class = "documentwrapper" >
< div class = "bodywrapper" >
< div class = "body" role = "main" >
< 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-train-convert-predict-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
< / div >
< div class = "sphx-glr-example-title section" id = "train-convert-and-predict-with-onnx-runtime" >
< span id = "l-logreg-example" > < / span > < span id = "sphx-glr-auto-examples-plot-train-convert-predict-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 machine learned model
to its use in its converted from.< / p >
< div class = "contents local topic" id = "contents" >
< ul class = "simple" >
< li > < p > < a class = "reference internal" href = "#train-a-logistic-regression" id = "id1" > Train a logistic regression< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#conversion-to-onnx-format" id = "id2" > Conversion to ONNX format< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#probabilities" id = "id3" > Probabilities< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#benchmark-with-randomforest" id = "id4" > Benchmark with RandomForest< / a > < / p > < / li >
< / ul >
< / div >
< div class = "section" id = "train-a-logistic-regression" >
< h2 > < a class = "toc-backref" href = "#id1" > Train a logistic regression< / a > < a class = "headerlink" href = "#train-a-logistic-regression" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > The first step consists in retrieving the iris datset.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.datasets< / span > < span class = "kn" > import< / span > < span class = "n" > load_iris< / span >
< span class = "n" > iris< / span > < span class = "o" > =< / span > < span class = "n" > load_iris< / 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" > iris< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "p" > ,< / span > < span class = "n" > iris< / span > < span class = "o" > .< / span > < span class = "n" > target< / span >
< span class = "kn" > from< / span > < span class = "nn" > sklearn.model_selection< / span > < span class = "kn" > import< / span > < span class = "n" > train_test_split< / span >
< 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 >
< / pre > < / div >
< / div >
< p > Then we fit a model.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.linear_model< / span > < span class = "kn" > import< / span > < span class = "n" > LogisticRegression< / span >
< span class = "n" > clr< / span > < span class = "o" > =< / span > < span class = "n" > LogisticRegression< / span > < span class = "p" > ()< / span >
< span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / 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 >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.metrics< / span > < span class = "kn" > import< / span > < span class = "n" > confusion_matrix< / span >
< span class = "n" > pred< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > y_test< / span > < span class = "p" > ,< / span > < span class = "n" > pred< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
2020-04-06 22:52:48 +00:00
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[ 9 0 0]
[ 0 15 1]
[ 0 1 12]]
2019-12-21 01:11:46 +00:00
< / 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 >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > skl2onnx< / span > < span class = "kn" > import< / span > < span class = "n" > convert_sklearn< / span >
< span class = "kn" > from< / span > < span class = "nn" > skl2onnx.common.data_types< / span > < span class = "kn" > import< / span > < span class = "n" > 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" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "bp" > None< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / 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" > clr< / 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" > " logreg_iris.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 >
< p > We load the model with ONNX Runtime and look at
its input and output.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > onnxruntime< / span > < span class = "kn" > as< / span > < span class = "nn" > rt< / span >
< 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" > " logreg_iris.onnx" < / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name=' {}' and shape={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / 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 = "p" > ,< / 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 > < span class = "p" > ))< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name=' {}' and shape={}" < / span > < span class = "o" > .< / span > < span class = "n" > format< / 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 > < span class = "o" > .< / span > < span class = "n" > name< / 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 > < span class = "o" > .< / span > < span class = "n" > shape< / 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 > input name=' float_input' and shape=[None, 4]
output name=' output_label' and shape=[1]
< / pre > < / div >
< / div >
< p > We compute the predictions.< / p >
< div class = "highlight-python 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 = "n" > label_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 = "kn" > import< / span > < span class = "nn" > numpy< / span >
< 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" > label_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / 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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > pred< / span > < span class = "p" > ,< / span > < span class = "n" > pred_onx< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
2020-04-06 22:52:48 +00:00
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[ 9 0 0]
[ 0 16 0]
[ 0 0 13]]
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > The prediction are perfectly identical.< / p >
< / div >
< div class = "section" id = "probabilities" >
< h2 > < a class = "toc-backref" href = "#id3" > Probabilities< / a > < a class = "headerlink" href = "#probabilities" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > Probabilities are needed to compute other
relevant metrics such as the ROC Curve.
Let’
scikit-learn.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_sklearn< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > prob_sklearn< / span > < span class = "p" > [:< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
2020-04-06 22:52:48 +00:00
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[0.00198628 0.71308408 0.28492964]
[0.00481246 0.83770353 0.15748401]
[0.00483108 0.75930899 0.23585993]]
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > And then with ONNX Runtime.
The probabilies appear to be< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_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" > 1< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
< span class = "n" > prob_rt< / 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" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / 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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "kn" > import< / span > < span class = "nn" > pprint< / span >
< span class = "n" > pprint< / span > < span class = "o" > .< / span > < span class = "n" > pprint< / span > < span class = "p" > (< / span > < span class = "n" > prob_rt< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > :< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
2020-04-06 22:52:48 +00:00
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [{0: 0.0019862803164869547, 1: 0.7130840420722961, 2: 0.28492966294288635},
{0: 0.0048124659806489944, 1: 0.8377037048339844, 2: 0.15748386085033417},
{0: 0.004831086378544569, 1: 0.7593092322349548, 2: 0.23585963249206543}]
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > Let’ < / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > timeit< / span > < span class = "kn" > import< / span > < span class = "n" > Timer< / span >
< span class = "k" > def< / span > < span class = "nf" > speed< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 20< / span > < span class = "p" > ):< / span >
< span class = "n" > timer< / span > < span class = "o" > =< / span > < span class = "n" > Timer< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "nb" > globals< / span > < span class = "o" > =< / span > < span class = "nb" > globals< / span > < span class = "p" > ())< / span >
< span class = "n" > raw< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > (< / span > < span class = "n" > timer< / span > < span class = "o" > .< / span > < span class = "n" > repeat< / span > < span class = "p" > (< / span > < span class = "n" > repeat< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "n" > number< / span > < span class = "p" > ))< / span >
< span class = "n" > ave< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > raw< / span > < span class = "p" > )< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
< span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > min< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span > < span class = "p" > ,< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Average < / span > < span class = "si" > %1.3g< / span > < span class = "s2" > min=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > max=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > " < / span > < span class = "o" > %< / span > < span class = "p" > (< / span > < span class = "n" > ave< / span > < span class = "p" > ,< / span > < span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "p" > ))< / span >
< span class = "k" > return< / span > < span class = "n" > ave< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " clr.predict(X_test)" < / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for ONNX Runtime" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]" < / 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 > Execution time for clr.predict
2020-04-06 22:52:48 +00:00
Average 4.8e-05 min=4.63e-05 max=5.71e-05
2019-12-21 01:11:46 +00:00
Execution time for ONNX Runtime
2020-04-06 22:52:48 +00:00
Average 3.19e-05 min=3.08e-05 max=3.81e-05
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > Let’
experiences: the model has to do one prediction at a time
as opposed to a batch of prediction.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > loop< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > fct< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "bp" > None< / span > < span class = "p" > ):< / span >
< span class = "n" > nrow< / span > < span class = "o" > =< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > if< / span > < span class = "n" > n< / span > < span class = "ow" > is< / span > < span class = "bp" > None< / span > < span class = "p" > :< / span >
< span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "n" > nrow< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "p" > ):< / span >
< span class = "n" > im< / span > < span class = "o" > =< / span > < span class = "n" > i< / span > < span class = "o" > %< / span > < span class = "n" > nrow< / span >
< span class = "n" > fct< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > [< / span > < span class = "n" > im< / span > < span class = "p" > :< / span > < span class = "n" > im< / span > < span class = "o" > +< / span > < span class = "mi" > 1< / span > < span class = "p" > ])< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > label_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 = "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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict, 100)" < / 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 > Execution time for clr.predict
2020-04-06 22:52:48 +00:00
Average 0.00445 min=0.00423 max=0.00495
2019-12-21 01:11:46 +00:00
Execution time for sess_predict
2020-04-06 22:52:48 +00:00
Average 0.00151 min=0.00145 max=0.00164
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > Let’ < / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict_proba, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_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 = "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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba, 100)" < / 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 > Execution time for predict_proba
2020-04-06 22:52:48 +00:00
Average 0.00643 min=0.00632 max=0.00666
2019-12-21 01:11:46 +00:00
Execution time for sess_predict_proba
2020-04-06 22:52:48 +00:00
Average 0.00158 min=0.00152 max=0.00177
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > This second comparison is better as
ONNX Runtime, in this experience,
computes the label and the probabilities
in every case.< / p >
< / div >
< div class = "section" id = "benchmark-with-randomforest" >
< h2 > < a class = "toc-backref" href = "#id4" > Benchmark with RandomForest< / a > < a class = "headerlink" href = "#benchmark-with-randomforest" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We first train and save a model in ONNX format.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.ensemble< / span > < span class = "kn" > import< / span > < span class = "n" > RandomForestClassifier< / span >
< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > ()< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / 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" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / 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" > rf< / 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" > " rf_iris.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 >
< p > We compare.< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < 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" > " rf_iris.onnx" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba_rf< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_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 = "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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, rf.predict_proba, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba_rf, 100)" < / 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 > Execution time for predict_proba
2020-04-06 22:52:48 +00:00
Average 0.516 min=0.483 max=0.572
2019-12-21 01:11:46 +00:00
Execution time for sess_predict_proba
2020-04-06 22:52:48 +00:00
Average 0.00176 min=0.00171 max=0.00188
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
< p > Let’ < / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > measures< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
< span class = "k" > for< / span > < span class = "n" > n_trees< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 51< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ):< / span >
< span class = "k" > print< / span > < span class = "p" > (< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > (< / span > < span class = "n" > n_estimators< / span > < span class = "o" > =< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / 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" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / 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" > rf< / 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" > " rf_iris_< / span > < span class = "si" > %d< / span > < span class = "s2" > .onnx" < / span > < span class = "o" > %< / span > < span class = "n" > n_trees< / 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 >
< 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" > " rf_iris_< / span > < span class = "si" > %d< / span > < span class = "s2" > .onnx" < / span > < span class = "o" > %< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba_loop< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_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 = "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 = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "n" > tsk< / span > < span class = "o" > =< / span > < span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, rf.predict_proba, 100)" < / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
< span class = "n" > trt< / span > < span class = "o" > =< / span > < span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba_loop, 100)" < / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
< span class = "n" > measures< / span > < span class = "o" > .< / span > < span class = "n" > append< / span > < span class = "p" > ({< / span > < span class = "s1" > ' n_trees' < / span > < span class = "p" > :< / span > < span class = "n" > n_trees< / span > < span class = "p" > ,< / span > < span class = "s1" > ' sklearn' < / span > < span class = "p" > :< / span > < span class = "n" > tsk< / span > < span class = "p" > ,< / span > < span class = "s1" > ' rt' < / span > < span class = "p" > :< / span > < span class = "n" > trt< / span > < span class = "p" > })< / span >
< span class = "kn" > from< / span > < span class = "nn" > pandas< / span > < span class = "kn" > import< / span > < span class = "n" > DataFrame< / span >
< span class = "n" > df< / span > < span class = "o" > =< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > measures< / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > =< / span > < span class = "n" > df< / span > < span class = "o" > .< / span > < span class = "n" > plot< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "s2" > " n_trees" < / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "s2" > " sklearn" < / span > < span class = "p" > ,< / span > < span class = "n" > label< / span > < span class = "o" > =< / span > < span class = "s2" > " scikit-learn" < / span > < span class = "p" > ,< / span > < span class = "n" > c< / span > < span class = "o" > =< / span > < span class = "s2" > " blue" < / span > < span class = "p" > ,< / span > < span class = "n" > logy< / span > < span class = "o" > =< / span > < span class = "bp" > True< / span > < span class = "p" > )< / span >
< span class = "n" > df< / span > < span class = "o" > .< / span > < span class = "n" > plot< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "s2" > " n_trees" < / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "s2" > " rt" < / span > < span class = "p" > ,< / span > < span class = "n" > label< / span > < span class = "o" > =< / span > < span class = "s2" > " onnxruntime" < / span > < span class = "p" > ,< / span >
< span class = "n" > ax< / span > < span class = "o" > =< / span > < span class = "n" > ax< / span > < span class = "p" > ,< / span > < span class = "n" > c< / span > < span class = "o" > =< / span > < span class = "s2" > " green" < / span > < span class = "p" > ,< / span > < span class = "n" > logy< / span > < span class = "o" > =< / span > < span class = "bp" > True< / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_xlabel< / span > < span class = "p" > (< / span > < span class = "s2" > " Number of trees" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_ylabel< / span > < span class = "p" > (< / span > < span class = "s2" > " Prediction time (s)" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > set_title< / span > < span class = "p" > (< / span > < span class = "s2" > " Speed comparison between scikit-learn and ONNX Runtime< / span > < span class = "se" > \n< / span > < span class = "s2" > For a random forest on Iris dataset" < / span > < span class = "p" > )< / span >
< span class = "n" > ax< / span > < span class = "o" > .< / span > < span class = "n" > legend< / span > < span class = "p" > ()< / span >
< / pre > < / div >
< / div >
< img alt = "../_images/sphx_glr_plot_train_convert_predict_001.png" class = "sphx-glr-single-img" src = "../_images/sphx_glr_plot_train_convert_predict_001.png" / >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > 5
2020-04-06 22:52:48 +00:00
Average 0.042 min=0.0412 max=0.0432
Average 0.00137 min=0.00136 max=0.00138
2019-12-21 01:11:46 +00:00
10
2020-04-06 22:52:48 +00:00
Average 0.065 min=0.0641 max=0.0662
Average 0.00151 min=0.00139 max=0.00159
2019-12-21 01:11:46 +00:00
15
2020-04-06 22:52:48 +00:00
Average 0.0886 min=0.0873 max=0.0911
Average 0.00145 min=0.00143 max=0.00148
2019-12-21 01:11:46 +00:00
20
2020-04-06 22:52:48 +00:00
Average 0.113 min=0.112 max=0.115
Average 0.0015 min=0.00149 max=0.00152
2019-12-21 01:11:46 +00:00
25
2020-04-06 22:52:48 +00:00
Average 0.14 min=0.135 max=0.15
Average 0.00161 min=0.0016 max=0.00164
2019-12-21 01:11:46 +00:00
30
2020-04-06 22:52:48 +00:00
Average 0.159 min=0.154 max=0.167
Average 0.00149 min=0.00146 max=0.00158
2019-12-21 01:11:46 +00:00
35
2020-04-06 22:52:48 +00:00
Average 0.183 min=0.177 max=0.195
Average 0.00153 min=0.0015 max=0.00159
2019-12-21 01:11:46 +00:00
40
2020-04-06 22:52:48 +00:00
Average 0.221 min=0.203 max=0.242
Average 0.0019 min=0.00153 max=0.0024
2019-12-21 01:11:46 +00:00
45
2020-04-06 22:52:48 +00:00
Average 0.23 min=0.22 max=0.248
Average 0.00177 min=0.00171 max=0.0018
2019-12-21 01:11:46 +00:00
50
2020-04-06 22:52:48 +00:00
Average 0.245 min=0.244 max=0.248
Average 0.0016 min=0.00157 max=0.00165
2019-12-21 01:11:46 +00:00
< / pre > < / div >
< / div >
2020-04-06 22:52:48 +00:00
< p > < strong > Total running time of the script:< / strong > ( 2 minutes 25.165 seconds)< / p >
2019-12-21 01:11:46 +00:00
< div class = "sphx-glr-footer class sphx-glr-footer-example docutils container" id = "sphx-glr-download-auto-examples-plot-train-convert-predict-py" >
< div class = "sphx-glr-download docutils container" >
< p > < a class = "reference download internal" download = "" href = "../_downloads/d83345a79a181a29892287297803aeec/plot_train_convert_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_train_convert_predict.py< / span > < / code > < / a > < / p >
< / div >
< div class = "sphx-glr-download docutils container" >
< p > < a class = "reference download internal" download = "" href = "../_downloads/3a0c2ba94405e579c84b6f356705659d/plot_train_convert_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_train_convert_predict.ipynb< / span > < / code > < / a > < / p >
< / div >
< / div >
< p class = "sphx-glr-signature" > < a class = "reference external" href = "https://sphinx-gallery.readthedocs.io" > Gallery generated by Sphinx-Gallery< / a > < / p >
< / div >
< / div >
< / div >
< / div >
< / div >
< div class = "sphinxsidebar" role = "navigation" aria-label = "main navigation" >
< div class = "sphinxsidebarwrapper" >
< p class = "logo" > < a href = "../index.html" >
< img class = "logo" src = "../_static/ONNX_Runtime_icon.png" alt = "Logo" / >
< / a > < / p >
< h1 class = "logo" > < a href = "../index.html" > ONNX Runtime< / a > < / h1 >
< h3 > Navigation< / h3 >
2020-04-06 22:52:48 +00:00
< ul >
2019-12-21 01:11:46 +00:00
< li class = "toctree-l1" > < a class = "reference internal" href = "../tutorial.html" > Tutorial< / a > < / li >
< li class = "toctree-l1" > < a class = "reference internal" href = "../api_summary.html" > API Summary< / a > < / li >
2020-04-06 22:52:48 +00:00
< li class = "toctree-l1" > < a class = "reference internal" href = "index.html" > Gallery of examples< / a > < / li >
2019-12-21 01:11:46 +00:00
< / ul >
< div class = "relations" >
< h3 > Related Topics< / h3 >
< ul >
< li > < a href = "../index.html" > Documentation overview< / a > < ul >
< li > < a href = "index.html" > Gallery of examples< / a > < ul >
< li > Previous: < a href = "plot_common_errors.html" title = "previous chapter" > Common errors with onnxruntime< / a > < / li >
< / ul > < / li >
< / ul > < / li >
< / ul >
< / div >
< div id = "searchbox" style = "display: none" role = "search" >
< h3 id = "searchlabel" > Quick search< / h3 >
< div class = "searchformwrapper" >
< form class = "search" action = "../search.html" method = "get" >
< input type = "text" name = "q" aria-labelledby = "searchlabel" / >
< input type = "submit" value = "Go" / >
< / form >
< / div >
< / div >
< script type = "text/javascript" > $ ( '#searchbox' ) . show ( 0 ) ; < / script >
2020-04-06 22:52:48 +00:00
2019-12-21 01:11:46 +00:00
< / div >
< / div >
< div class = "clearer" > < / div >
< / div >
< div class = "footer" >
© 2018-2019, Microsoft.
|
2020-04-06 22:52:48 +00:00
Powered by < a href = "http://sphinx-doc.org/" > Sphinx 2.2.1< / a >
& < a href = "https://github.com/bitprophet/alabaster" > Alabaster 0.7.12< / a >
2019-12-21 01:11:46 +00:00
|
< a href = "../_sources/auto_examples/plot_train_convert_predict.rst.txt"
rel="nofollow">Page source< / a >
< / div >
< / body >
< / html >
