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<div class="sphx-glr-download-link-note admonition note">
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<p class="admonition-title">Note</p>
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<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>
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<div class="sphx-glr-example-title section" id="onnx-runtime-for-keras">
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<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>
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<p>The following demonstrates how to compute the predictions
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of a pretrained deep learning model obtained from
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<a class="reference external" href="https://keras.io/">keras</a>
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with <em>onnxruntime</em>. The conversion requires
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<a class="reference external" href="https://keras.io/">keras</a>,
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<a class="reference external" href="https://www.tensorflow.org/">tensorflow</a>,
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<a class="reference external" href="https://github.com/onnx/keras-onnx/">keras-onnx</a>,
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<a class="reference external" href="https://pypi.org/project/onnxmltools/">onnxmltools</a>
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but then only <em>onnxruntime</em> is required
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to compute the predictions.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
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<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">'dense121.onnx'</span><span class="p">):</span>
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<span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="kn">import</span> <span class="n">DenseNet121</span>
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<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="bp">True</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="s1">'imagenet'</span><span class="p">)</span>
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<span class="kn">from</span> <span class="nn">keras2onnx</span> <span class="kn">import</span> <span class="n">convert_keras</span>
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<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">'dense121.onnx'</span><span class="p">)</span>
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<span class="n">onx</span><span class="o">.</span><span class="n">ir_version</span> <span class="o">=</span> <span class="mi">6</span>
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<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"dense121.onnx"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
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<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>
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</pre></div>
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</div>
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<p>Let’s load an image (source: wikipedia).</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing.image</span> <span class="kn">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>
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<span class="n">img</span> <span class="o">=</span> <span class="n">load_img</span><span class="p">(</span><span class="s1">'Sannosawa1.jpg'</span><span class="p">)</span>
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<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>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
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<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>
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<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
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</pre></div>
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</div>
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<img alt="../_images/sphx_glr_plot_dl_keras_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_plot_dl_keras_001.png" />
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<p>Let’s load the model with onnxruntime.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="kn">as</span> <span class="nn">rt</span>
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<span class="kn">from</span> <span class="nn">onnxruntime.capi.onnxruntime_pybind11_state</span> <span class="kn">import</span> <span class="n">InvalidGraph</span>
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<span class="k">try</span><span class="p">:</span>
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<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">'dense121.onnx'</span><span class="p">)</span>
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<span class="n">ok</span> <span class="o">=</span> <span class="bp">True</span>
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<span class="k">except</span> <span class="p">(</span><span class="n">InvalidGraph</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">,</span> <span class="ne">RuntimeError</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="c1"># Probably a mismatch between onnxruntime and onnx version.</span>
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<span class="k">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
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<span class="n">ok</span> <span class="o">=</span> <span class="bp">False</span>
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<span class="k">if</span> <span class="n">ok</span><span class="p">:</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">"The model expects input shape:"</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>
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<span class="k">print</span><span class="p">(</span><span class="s2">"image shape:"</span><span class="p">,</span> <span class="n">ximg</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>The model expects input shape: ['N', 224, 224, 3]
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image shape: (960, 1280, 3)
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</pre></div>
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</div>
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<p>Let’s resize the image.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">ok</span><span class="p">:</span>
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<span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="kn">import</span> <span class="n">resize</span>
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<span class="kn">import</span> <span class="nn">numpy</span>
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<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="bp">True</span><span class="p">)</span>
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<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>
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<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>
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<span class="k">print</span><span class="p">(</span><span class="s2">"new shape:"</span><span class="p">,</span> <span class="n">ximg</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>new shape: (1, 224, 224, 3)
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</pre></div>
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</div>
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<p>Let’s compute the output.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">ok</span><span class="p">:</span>
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<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>
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<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="bp">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>
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<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>
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<span class="k">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>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[2.0848038e-05 9.0344531e-07 1.6248682e-06 4.8086035e-06 6.5069275e-06
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9.4027422e-07 1.9977892e-06 4.6639599e-07 9.4333433e-07 3.2267465e-06]
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</pre></div>
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</div>
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<p>Let’s get more comprehensive results.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">ok</span><span class="p">:</span>
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<span class="kn">from</span> <span class="nn">keras.applications.densenet</span> <span class="kn">import</span> <span class="n">decode_predictions</span>
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<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>
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<span class="kn">import</span> <span class="nn">pandas</span>
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<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">"class_id"</span><span class="p">,</span> <span class="s2">"name"</span><span class="p">,</span> <span class="s2">"P"</span><span class="p">])</span>
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<span class="k">print</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>class_id name P
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0 n09468604 valley 0.673278
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1 n09193705 alp 0.267429
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2 n09399592 promontory 0.013859
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3 n09246464 cliff 0.013251
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4 n03792972 mountain_tent 0.007756
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</pre></div>
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