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[Automated]: Update Python API docs (#12473)
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<title>Load and predict with ONNX Runtime and a very simple model &#8212; ONNX Runtime 1.13.0 documentation</title>
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<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>
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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 heading"></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">numpy</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</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>
<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>
<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>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[array([[[0.72465897, 0.72574586, 0.6371799 , 0.71288747, 0.6594067 ],
[0.6177746 , 0.587299 , 0.5193266 , 0.6938682 , 0.64304864],
[0.52724993, 0.6042261 , 0.6895429 , 0.6509503 , 0.6616442 ],
[0.6763506 , 0.6537579 , 0.6264375 , 0.59437954, 0.5884929 ]],
[[0.6776498 , 0.5767992 , 0.6779048 , 0.67587113, 0.61081856],
[0.7236815 , 0.5775631 , 0.61381036, 0.6063235 , 0.62550837],
[0.50100976, 0.62944853, 0.70942533, 0.6089567 , 0.63848245],
[0.5719551 , 0.68211 , 0.511835 , 0.7158986 , 0.53346515]],
[[0.6256963 , 0.5512383 , 0.5719546 , 0.6073346 , 0.64393806],
[0.6807264 , 0.6192803 , 0.5377599 , 0.5919384 , 0.7111624 ],
[0.7152287 , 0.6213895 , 0.7274681 , 0.609508 , 0.59370804],
[0.6366582 , 0.5454885 , 0.72254837, 0.55745107, 0.5137241 ]]],
dtype=float32)]
</pre></div>
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