mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-15 18:23:41 +00:00
79542dd377
Automated changes by [create-pull-request](https://github.com/peter-evans/create-pull-request) GitHub action Co-authored-by: fs-eire <fs-eire@users.noreply.github.com>
467 lines
No EOL
44 KiB
HTML
467 lines
No EOL
44 KiB
HTML
|
||
<!DOCTYPE html>
|
||
|
||
<html lang="en">
|
||
<head>
|
||
<meta charset="utf-8" />
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
|
||
|
||
<!-- Google tag (gtag.js) -->
|
||
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-156955408-1"></script>
|
||
<script>
|
||
window.dataLayer = window.dataLayer || [];
|
||
function gtag(){dataLayer.push(arguments);}
|
||
gtag('js', new Date());
|
||
gtag('config', 'UA-156955408-1');
|
||
</script>
|
||
|
||
<title>Train, convert and predict with ONNX Runtime — ONNX Runtime 1.14.0 documentation</title>
|
||
<link rel="stylesheet" type="text/css" href="../static/pygments.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/alabaster.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/graphviz.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/sg_gallery.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/sg_gallery-binder.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/sg_gallery-dataframe.css" />
|
||
<link rel="stylesheet" type="text/css" href="../static/sg_gallery-rendered-html.css" />
|
||
<script data-url_root="../" id="documentation_options" src="../static/documentation_options.js"></script>
|
||
<script src="../static/jquery.js"></script>
|
||
<script src="../static/underscore.js"></script>
|
||
<script src="../static/_sphinx_javascript_frameworks_compat.js"></script>
|
||
<script src="../static/doctools.js"></script>
|
||
<script src="../static/sphinx_highlight.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>
|
||
<section class="sphx-glr-example-title" 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 heading">¶</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>
|
||
<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 heading">¶</a></h2>
|
||
<p>The first step consists in retrieving the iris datset.</p>
|
||
<div class="highlight-default 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-default 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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):
|
||
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
|
||
|
||
Increase the number of iterations (max_iter) or scale the data as shown in:
|
||
https://scikit-learn.org/stable/modules/preprocessing.html
|
||
Please also refer to the documentation for alternative solver options:
|
||
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
|
||
n_iter_i = _check_optimize_result(
|
||
</pre></div>
|
||
</div>
|
||
<div class="output_subarea output_html rendered_html output_result">
|
||
<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div>
|
||
</div>
|
||
<br />
|
||
<br /><p>We compute the prediction on the test set
|
||
and we show the confusion matrix.</p>
|
||
<div class="highlight-default 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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[14 0 0]
|
||
[ 0 11 0]
|
||
[ 0 1 12]]
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<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 heading">¶</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-default 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="s2">"float_input"</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="kc">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-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>
|
||
|
||
<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="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>
|
||
|
||
<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">"</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="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">"</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>
|
||
<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=[None]
|
||
</pre></div>
|
||
</div>
|
||
<p>We compute the predictions.</p>
|
||
<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="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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[14 0 0]
|
||
[ 0 12 0]
|
||
[ 0 0 12]]
|
||
</pre></div>
|
||
</div>
|
||
<p>The prediction are perfectly identical.</p>
|
||
</section>
|
||
<section id="probabilities">
|
||
<h2><a class="toc-backref" href="#id3">Probabilities</a><a class="headerlink" href="#probabilities" title="Permalink to this heading">¶</a></h2>
|
||
<p>Probabilities are needed to compute other
|
||
relevant metrics such as the ROC Curve.
|
||
Let’s see how to get them first with
|
||
scikit-learn.</p>
|
||
<div class="highlight-default 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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[1.90908960e-01 8.02874408e-01 6.21663156e-03]
|
||
[2.81698299e-02 9.12759445e-01 5.90707247e-02]
|
||
[9.67801591e-01 3.21983324e-02 7.64754321e-08]]
|
||
</pre></div>
|
||
</div>
|
||
<p>And then with ONNX Runtime.
|
||
The probabilies appear to be</p>
|
||
<div class="highlight-default 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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[{0: 0.19090914726257324, 1: 0.8028742074966431, 2: 0.0062166303396224976},
|
||
{0: 0.02816985361278057, 1: 0.9127594232559204, 2: 0.059070732444524765},
|
||
{0: 0.9678016304969788, 1: 0.03219832107424736, 2: 7.647536648391906e-08}]
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s benchmark.</p>
|
||
<div class="highlight-default 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="nb">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="nb">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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
|
||
Average 4.44e-05 min=4.24e-05 max=5.4e-05
|
||
Execution time for ONNX Runtime
|
||
Average 2.21e-05 min=2.16e-05 max=2.71e-05
|
||
|
||
2.2087500000509407e-05
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s benchmark a scenario similar to what a webservice
|
||
experiences: the model has to do one prediction at a time
|
||
as opposed to a batch of prediction.</p>
|
||
<div class="highlight-default 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="kc">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="kc">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="nb">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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for clr.predict
|
||
Average 0.00406 min=0.00404 max=0.00414
|
||
Execution time for sess_predict
|
||
Average 0.00104 min=0.00102 max=0.00107
|
||
|
||
0.00103513229499967
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s do the same for the probabilities.</p>
|
||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
|
||
Average 0.0061 min=0.00608 max=0.00626
|
||
Execution time for sess_predict_proba
|
||
Average 0.00108 min=0.00107 max=0.0011
|
||
|
||
0.001081757285000009
|
||
</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>
|
||
</section>
|
||
<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 heading">¶</a></h2>
|
||
<p>We first train and save a model in ONNX format.</p>
|
||
<div class="highlight-default 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="s2">"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-default 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="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>
|
||
|
||
|
||
<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="nb">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="nb">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>
|
||
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Execution time for predict_proba
|
||
Average 0.674 min=0.672 max=0.678
|
||
Execution time for sess_predict_proba
|
||
Average 0.0013 min=0.00129 max=0.00133
|
||
|
||
0.001301839815000676
|
||
</pre></div>
|
||
</div>
|
||
<p>Let’s see with different number of trees.</p>
|
||
<div class="highlight-default 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="nb">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="s2">"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="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>
|
||
|
||
<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="s2">"n_trees"</span><span class="p">:</span> <span class="n">n_trees</span><span class="p">,</span> <span class="s2">"sklearn"</span><span class="p">:</span> <span class="n">tsk</span><span class="p">,</span> <span class="s2">"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="kc">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="kc">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 src="../images/sphx_glr_plot_train_convert_predict_001.png" srcset="../images/sphx_glr_plot_train_convert_predict_001.png" alt="Speed comparison between scikit-learn and ONNX Runtime For a random forest on Iris dataset" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>5
|
||
Average 0.0491 min=0.049 max=0.0491
|
||
Average 0.00103 min=0.00102 max=0.00105
|
||
10
|
||
Average 0.0823 min=0.0822 max=0.0824
|
||
Average 0.00104 min=0.00102 max=0.00106
|
||
15
|
||
Average 0.115 min=0.115 max=0.115
|
||
Average 0.00103 min=0.00102 max=0.00106
|
||
20
|
||
Average 0.148 min=0.148 max=0.148
|
||
Average 0.00105 min=0.00104 max=0.00108
|
||
25
|
||
Average 0.181 min=0.181 max=0.182
|
||
Average 0.00107 min=0.00106 max=0.00109
|
||
30
|
||
Average 0.214 min=0.214 max=0.214
|
||
Average 0.00107 min=0.00106 max=0.00109
|
||
35
|
||
Average 0.247 min=0.247 max=0.248
|
||
Average 0.00109 min=0.00108 max=0.00111
|
||
40
|
||
Average 0.28 min=0.279 max=0.28
|
||
Average 0.00109 min=0.00108 max=0.00112
|
||
45
|
||
Average 0.312 min=0.311 max=0.312
|
||
Average 0.00112 min=0.00111 max=0.00115
|
||
50
|
||
Average 0.345 min=0.344 max=0.345
|
||
Average 0.00112 min=0.00111 max=0.00114
|
||
|
||
<matplotlib.legend.Legend object at 0x7f0fd5bd2a40>
|
||
</pre></div>
|
||
</div>
|
||
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes 8.150 seconds)</p>
|
||
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-train-convert-predict-py">
|
||
<div class="sphx-glr-download sphx-glr-download-python docutils container">
|
||
<p><a class="reference download internal" download="" href="../downloads/c647c128e0cf2b3db04ce60b41ef1a14/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 sphx-glr-download-jupyter docutils container">
|
||
<p><a class="reference download internal" download="" href="../downloads/1680115d3d937dfbb2d86adb705d9c5d/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.github.io">Gallery generated by Sphinx-Gallery</a></p>
|
||
</section>
|
||
</section>
|
||
|
||
|
||
</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>
|
||
<ul>
|
||
<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</a></li>
|
||
<li class="toctree-l1"><a class="reference internal" href="index.html">Gallery of examples</a></li>
|
||
</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" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
|
||
<input type="submit" value="Go" />
|
||
</form>
|
||
</div>
|
||
</div>
|
||
<script>document.getElementById('searchbox').style.display = "block"</script>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
</div>
|
||
</div>
|
||
<div class="clearer"></div>
|
||
</div>
|
||
<div class="footer">
|
||
©2018-2023, Microsoft.
|
||
|
||
|
|
||
Powered by <a href="http://sphinx-doc.org/">Sphinx 5.3.0</a>
|
||
& <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.13</a>
|
||
|
||
|
|
||
<a href="../sources/auto_examples/plot_train_convert_predict.rst.txt"
|
||
rel="nofollow">Page source</a>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
</body>
|
||
</html> |