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onnxruntime/auto_examples/plot_dl_keras.html
Hariharan Seshadri e10d3f5202 Update docs based on 0.5.0 release (#1548)
2019-08-01 18:12:59 -07:00

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<title>ONNX Runtime for Keras</title>
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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"><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_pipeline.html">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_metadata.html">Metadata</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">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="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-dl-keras-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="onnx-runtime-for-keras">
<span id="l-example-backend-api-tensorflow"></span><span id="sphx-glr-auto-examples-plot-dl-keras-py"></span><h1>ONNX Runtime for Keras<a class="headerlink" href="#onnx-runtime-for-keras" title="Permalink to this headline"></a></h1>
<p>The following demonstrates how to compute the predictions
of a pretrained deep learning model obtained from
<a class="reference external" href="https://keras.io/">keras</a>
with <em>onnxruntime</em>. The conversion requires
<a class="reference external" href="https://keras.io/">keras</a>,
<a class="reference external" href="https://www.tensorflow.org/">tensorflow</a>,
<a class="reference external" href="https://github.com/onnx/keras-onnx/">keras-onnx</a>,
<a class="reference external" href="https://pypi.org/project/onnxmltools/">onnxmltools</a>
but then only <em>onnxruntime</em> is required
to compute the predictions.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="s1">&#39;dense121.onnx&#39;</span><span class="p">):</span>
<span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="k">import</span> <span class="n">DenseNet121</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">DenseNet121</span><span class="p">(</span><span class="n">include_top</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="s1">&#39;imagenet&#39;</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">keras2onnx</span> <span class="k">import</span> <span class="n">convert_keras</span>
<span class="n">onx</span> <span class="o">=</span> <span class="n">convert_keras</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s1">&#39;dense121.onnx&#39;</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;dense121.onnx&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</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>Let’s load an image (source: wikipedia).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing.image</span> <span class="k">import</span> <span class="n">array_to_img</span><span class="p">,</span> <span class="n">img_to_array</span><span class="p">,</span> <span class="n">load_img</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">load_img</span><span class="p">(</span><span class="s1">&#39;Sannosawa1.jpg&#39;</span><span class="p">)</span>
<span class="n">ximg</span> <span class="o">=</span> <span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">ximg</span> <span class="o">/</span> <span class="mi">255</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../_images/sphx_glr_plot_dl_keras_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_plot_dl_keras_001.png" />
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using TensorFlow backend.
</pre></div>
</div>
<p>Let’s load the model with onnxruntime.</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="s1">&#39;dense121.onnx&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The model expects input shape:&quot;</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">&quot;image shape:&quot;</span><span class="p">,</span> <span class="n">ximg</span><span class="o">.</span><span class="n">shape</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>The model expects input shape: [&#39;N&#39;, 224, 224, 3]
image shape: (960, 1280, 3)
</pre></div>
</div>
<p>Let’s resize the image.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="k">import</span> <span class="n">resize</span>
<span class="kn">import</span> <span class="nn">numpy</span>
<span class="n">ximg224</span> <span class="o">=</span> <span class="n">resize</span><span class="p">(</span><span class="n">ximg</span> <span class="o">/</span> <span class="mi">255</span><span class="p">,</span> <span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">anti_aliasing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">ximg</span> <span class="o">=</span> <span class="n">ximg224</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="p">:]</span>
<span class="n">ximg</span> <span class="o">=</span> <span class="n">ximg</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="nb">print</span><span class="p">(</span><span class="s2">&quot;new shape:&quot;</span><span class="p">,</span> <span class="n">ximg</span><span class="o">.</span><span class="n">shape</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>new shape: (1, 224, 224, 3)
</pre></div>
</div>
<p>Let’s compute the output.</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">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="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">ximg</span><span class="p">})</span>
<span class="n">prob</span> <span class="o">=</span> <span class="n">res</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">prob</span><span class="o">.</span><span class="n">ravel</span><span class="p">()[:</span><span class="mi">10</span><span class="p">])</span> <span class="c1"># Too big to be displayed.</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>[2.0848269e-05 9.0345173e-07 1.6248799e-06 4.8086245e-06 6.5069430e-06
9.4028013e-07 1.9978056e-06 4.6639886e-07 9.4333654e-07 3.2267878e-06]
</pre></div>
</div>
<p>Let’s get more comprehensive results.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="k">import</span> <span class="n">decode_predictions</span>
<span class="n">decoded</span> <span class="o">=</span> <span class="n">decode_predictions</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">pandas</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">decoded</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;class_id&quot;</span><span class="p">,</span> <span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="s2">&quot;P&quot;</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df</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>Downloading data from https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json
8192/35363 [=====&gt;........................] - ETA: 0s
24576/35363 [===================&gt;..........] - ETA: 0s
40960/35363 [==================================] - 0s 2us/step
class_id name P
0 n09468604 valley 0.673280
1 n09193705 alp 0.267427
2 n09399592 promontory 0.013859
3 n09246464 cliff 0.013251
4 n03792972 mountain_tent 0.007756
</pre></div>
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