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onnxruntime/python/auto_examples/plot_pipeline.html
Ryan Hill 5ef84bf0f8 Update to v1.1.0 (#2706)
2019-12-20 13:35:58 -08:00

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<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-1-train-a-model-using-your-favorite-framework">Step 1: Train a model using your favorite framework</a></li>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format">Step 2: Convert or export the model into ONNX format</a></li>
<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime">Step 3: Load and run the model using ONNX Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../api_summary.html">API Summary</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#device">Device</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#examples-and-datasets">Examples and datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#load-and-run-a-model">Load and run a model</a></li>
<li class="toctree-l2"><a class="reference internal" href="../api_summary.html#backend">Backend</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="index.html">Gallery of examples</a><ul class="current">
<li class="toctree-l2 current"><a class="current reference internal" href="#">Draw a pipeline</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_load_and_predict.html">Load and predict with ONNX Runtime and a very simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_profiling.html">Profile the execution of a simple model</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_backend.html">ONNX Runtime Backend for ONNX</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_metadata.html">Metadata</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_dl_keras.html">ONNX Runtime for Keras</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_convert_pipeline_vectorizer.html">Train, convert and predict with ONNX Runtime</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_common_errors.html">Common errors with onnxruntime</a></li>
<li class="toctree-l2"><a class="reference internal" href="plot_train_convert_predict.html">Train, convert and predict with ONNX Runtime</a></li>
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<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-pipeline-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="draw-a-pipeline">
<span id="sphx-glr-auto-examples-plot-pipeline-py"></span><h1>Draw a pipeline<a class="headerlink" href="#draw-a-pipeline" title="Permalink to this headline"></a></h1>
<p>There is no other way to look into one model stored
in ONNX format than looking into its node with
<em>onnx</em>. This example demonstrates
how to draw a model and to retrieve it in <em>json</em>
format.</p>
<div class="contents local topic" id="contents">
<ul class="simple">
<li><a class="reference internal" href="#retrieve-a-model-in-json-format" id="id1">Retrieve a model in JSON format</a></li>
<li><a class="reference internal" href="#draw-a-model-with-onnx" id="id2">Draw a model with ONNX</a></li>
</ul>
</div>
<div class="section" id="retrieve-a-model-in-json-format">
<h2><a class="toc-backref" href="#id1">Retrieve a model in JSON format</a><a class="headerlink" href="#retrieve-a-model-in-json-format" title="Permalink to this headline"></a></h2>
<p>That’s the most simple way.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="kn">import</span> <span class="n">get_example</span>
<span class="n">example1</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">&quot;mul_1.onnx&quot;</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">onnx</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">onnx</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">example1</span><span class="p">)</span> <span class="c1"># model is a ModelProto protobuf message</span>
<span class="k">print</span><span class="p">(</span><span class="n">model</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>producer_name: &quot;chenta&quot;
graph {
node {
input: &quot;X&quot;
input: &quot;W&quot;
output: &quot;Y&quot;
name: &quot;mul_1&quot;
op_type: &quot;Mul&quot;
}
name: &quot;mul test&quot;
initializer {
dims: 3
dims: 2
data_type: 1
float_data: 1.0
float_data: 2.0
float_data: 3.0
float_data: 4.0
float_data: 5.0
float_data: 6.0
name: &quot;W&quot;
}
input {
name: &quot;X&quot;
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 3
}
dim {
dim_value: 2
}
}
}
}
}
output {
name: &quot;Y&quot;
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 3
}
dim {
dim_value: 2
}
}
}
}
}
}
opset_import {
domain: &quot;&quot;
version: 7
}
</pre></div>
</div>
</div>
<div class="section" id="draw-a-model-with-onnx">
<h2><a class="toc-backref" href="#id2">Draw a model with ONNX</a><a class="headerlink" href="#draw-a-model-with-onnx" title="Permalink to this headline"></a></h2>
<p>We use <a class="reference external" href="https://github.com/onnx/onnx/blob/master/onnx/tools/net_drawer.py">net_drawer.py</a>
included in <em>onnx</em> package.
We use <em>onnx</em> to load the model
in a different way than before.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnx</span> <span class="kn">import</span> <span class="n">ModelProto</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ModelProto</span><span class="p">()</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">example1</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fid</span><span class="p">:</span>
<span class="n">content</span> <span class="o">=</span> <span class="n">fid</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">ParseFromString</span><span class="p">(</span><span class="n">content</span><span class="p">)</span>
</pre></div>
</div>
<p>We convert it into a graph.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">onnx.tools.net_drawer</span> <span class="kn">import</span> <span class="n">GetPydotGraph</span><span class="p">,</span> <span class="n">GetOpNodeProducer</span>
<span class="n">pydot_graph</span> <span class="o">=</span> <span class="n">GetPydotGraph</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">rankdir</span><span class="o">=</span><span class="s2">&quot;LR&quot;</span><span class="p">,</span>
<span class="n">node_producer</span><span class="o">=</span><span class="n">GetOpNodeProducer</span><span class="p">(</span><span class="s2">&quot;docstring&quot;</span><span class="p">))</span>
<span class="n">pydot_graph</span><span class="o">.</span><span class="n">write_dot</span><span class="p">(</span><span class="s2">&quot;graph.dot&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>Then into an image</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="n">os</span><span class="o">.</span><span class="n">system</span><span class="p">(</span><span class="s1">&#39;dot -O -Tpng graph.dot&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>Which we display…</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s2">&quot;graph.dot.png&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_plot_pipeline_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_plot_pipeline_001.png" />
<p><strong>Total running time of the script:</strong> ( 0 minutes 0.493 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-pipeline-py">
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../_downloads/plot_pipeline.py" download=""><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_pipeline.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../_downloads/plot_pipeline.ipynb" download=""><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_pipeline.ipynb</span></code></a></div>
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