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[Automated]: Update Python API docs (#11113)
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<title>Load and predict with ONNX Runtime and a very simple model &#8212; ONNX Runtime 1.12.0 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-load-and-predict-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="load-and-predict-with-onnx-runtime-and-a-very-simple-model">
<span id="l-example-simple-usage"></span><span id="sphx-glr-auto-examples-plot-load-and-predict-py"></span><h1>Load and predict with ONNX Runtime and a very simple model<a class="headerlink" href="#load-and-predict-with-onnx-runtime-and-a-very-simple-model" title="Permalink to this headline"></a></h1>
<p>This example demonstrates how to load a model and compute
the output for an input vector. It also shows how to
retrieve the definition of its inputs and outputs.</p>
<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="kn">import</span> <span class="nn">numpy</span>
<span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="k">import</span> <span class="n">get_example</span>
</pre></div>
</div>
<p>Let’s load a very simple model.
The model is available on github <a class="reference external" href="https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid">onnx…test_sigmoid</a>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">example1</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">&quot;sigmoid.onnx&quot;</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="n">example1</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>
</pre></div>
</div>
<p>Let’s see the input name and shape.</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="nb">print</span><span class="p">(</span><span class="s2">&quot;input name&quot;</span><span class="p">,</span> <span class="n">input_name</span><span class="p">)</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input shape&quot;</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">)</span>
<span class="n">input_type</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">type</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;input type&quot;</span><span class="p">,</span> <span class="n">input_type</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 x
input shape [3, 4, 5]
input type tensor(float)
</pre></div>
</div>
<p>Let’s see the output name and shape.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">output_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output name&quot;</span><span class="p">,</span> <span class="n">output_name</span><span class="p">)</span>
<span class="n">output_shape</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output shape&quot;</span><span class="p">,</span> <span class="n">output_shape</span><span class="p">)</span>
<span class="n">output_type</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">type</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;output type&quot;</span><span class="p">,</span> <span class="n">output_type</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>output name y
output shape [3, 4, 5]
output type tensor(float)
</pre></div>
</div>
<p>Let’s compute its outputs (or predictions if it is a machine learned model).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy.random</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res</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>[array([[[0.6514643 , 0.7119085 , 0.51459455, 0.61384887, 0.5246651 ],
[0.5636192 , 0.56092787, 0.64965874, 0.6829034 , 0.59758645],
[0.6981421 , 0.5157956 , 0.6199459 , 0.58960956, 0.6927162 ],
[0.5597512 , 0.68971556, 0.57514626, 0.6138272 , 0.52836967]],
[[0.6518691 , 0.59283644, 0.58380157, 0.6333722 , 0.7277889 ],
[0.5069886 , 0.60194266, 0.71367145, 0.5432901 , 0.50752676],
[0.5166088 , 0.5692218 , 0.5205236 , 0.6938001 , 0.6609123 ],
[0.5552745 , 0.6102384 , 0.6113623 , 0.57109815, 0.5270999 ]],
[[0.5491344 , 0.5831849 , 0.5123648 , 0.71404976, 0.72816455],
[0.66446227, 0.5016449 , 0.55448776, 0.71575 , 0.6223295 ],
[0.6581422 , 0.51240706, 0.6351511 , 0.6651438 , 0.6633214 ],
[0.5530689 , 0.6762203 , 0.5492414 , 0.6816697 , 0.7236053 ]]],
dtype=float32)]
</pre></div>
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<p><a class="reference download internal" download="" href="../downloads/7c8424f45d0156abd4d0221c65601124/plot_load_and_predict.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_load_and_predict.py</span></code></a></p>
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