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[Automated]: Update Python API docs (#13926) 
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Co-authored-by: natke <natke@users.noreply.github.com>
2022-12-09 15:55:42 -08:00

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<title>ONNX Runtime Backend for ONNX &#8212; ONNX Runtime 1.14.0 documentation</title>
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<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-backend-py"><span class="std std-ref">here</span></a>
to download the full example code</p>
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<section class="sphx-glr-example-title" id="onnx-runtime-backend-for-onnx">
<span id="l-example-backend-api"></span><span id="sphx-glr-auto-examples-plot-backend-py"></span><h1>ONNX Runtime Backend for ONNX<a class="headerlink" href="#onnx-runtime-backend-for-onnx" title="Permalink to this heading"></a></h1>
<p><em>ONNX Runtime</em> extends the
<a class="reference external" href="https://github.com/onnx/onnx/blob/main/docs/ImplementingAnOnnxBackend.md">onnx backend API</a>
to run predictions using this runtime.
Let’s use the API to compute the prediction
of a simple logistic regression model.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">onnx</span> <span class="kn">import</span> <span class="n">load</span>
<span class="kn">import</span> <span class="nn">onnxruntime.backend</span> <span class="k">as</span> <span class="nn">backend</span>
</pre></div>
</div>
<p>The device depends on how the package was compiled,
GPU or CPU.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnxruntime</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">get_device</span>
<span class="kn">from</span> <span class="nn">onnxruntime.capi.onnxruntime_pybind11_state</span> <span class="kn">import</span> <span class="n">InvalidArgument</span>
<span class="n">device</span> <span class="o">=</span> <span class="n">get_device</span><span class="p">()</span>
<span class="n">name</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">get_example</span><span class="p">(</span><span class="s2">&quot;logreg_iris.onnx&quot;</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="n">rep</span> <span class="o">=</span> <span class="n">backend</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">label</span><span class="p">,</span> <span class="n">proba</span> <span class="o">=</span> <span class="n">rep</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;label=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;probabilities=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">proba</span><span class="p">))</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
index: 0 Got: 1 Expected: 3
Please fix either the inputs or the model.
</pre></div>
</div>
<p>The backend can also directly load the model
without using <em>onnx</em>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">rep</span> <span class="o">=</span> <span class="n">backend</span><span class="o">.</span><span class="n">prepare</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">label</span><span class="p">,</span> <span class="n">proba</span> <span class="o">=</span> <span class="n">rep</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;label=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;probabilities=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">proba</span><span class="p">))</span>
<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
index: 0 Got: 1 Expected: 3
Please fix either the inputs or the model.
</pre></div>
</div>
<p>The backend API is implemented by other frameworks
and makes it easier to switch between multiple runtimes
with the same API.</p>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.021 seconds)</p>
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<p><a class="reference download internal" download="" href="../downloads/3e23fa9ebb26f4728ee8426ed7da0f63/plot_backend.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_backend.py</span></code></a></p>
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<p><a class="reference download internal" download="" href="../downloads/f8e5d8e309ca291f68bd029c26838ccc/plot_backend.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_backend.ipynb</span></code></a></p>
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