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   <h1>Source code for apache_beam.ml.inference.onnx_inference</h1><div class="highlight"><pre>
 <span></span><span class="c1">#</span>
 <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
 <span class="c1"># contributor license agreements.  See the NOTICE file distributed with</span>
 <span class="c1"># this work for additional information regarding copyright ownership.</span>
 <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
 <span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
 <span class="c1"># the License.  You may obtain a copy of the License at</span>
 <span class="c1">#</span>
 <span class="c1">#    http://www.apache.org/licenses/LICENSE-2.0</span>
 <span class="c1">#</span>
 <span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
 <span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
 <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
 <span class="c1"># See the License for the specific language governing permissions and</span>
 <span class="c1"># limitations under the License.</span>
 <span class="c1">#</span>

 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>

 <span class="kn">import</span> <span class="nn">numpy</span>

 <span class="kn">import</span> <span class="nn">onnx</span>
 <span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">ort</span>
 <span class="kn">from</span> <span class="nn">apache_beam.io.filesystems</span> <span class="kn">import</span> <span class="n">FileSystems</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference</span> <span class="kn">import</span> <span class="n">utils</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">ModelHandler</span>
 <span class="kn">from</span> <span class="nn">apache_beam.ml.inference.base</span> <span class="kn">import</span> <span class="n">PredictionResult</span>

 <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;OnnxModelHandlerNumpy&#39;</span><span class="p">]</span>

 <span class="n">NumpyInferenceFn</span> <span class="o">=</span> <span class="n">Callable</span><span class="p">[</span>
     <span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">,</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]],</span>
     <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]]</span>


 <span class="k">def</span> <span class="nf">default_numpy_inference_fn</span><span class="p">(</span>
     <span class="n">inference_session</span><span class="p">:</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">,</span>
     <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
     <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
   <span class="n">ort_inputs</span> <span class="o">=</span> <span class="p">{</span>
       <span class="n">inference_session</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="p">:</span> <span class="n">numpy</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
   <span class="p">}</span>
   <span class="k">if</span> <span class="n">inference_args</span><span class="p">:</span>
     <span class="n">ort_inputs</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">ort_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">inference_args</span><span class="p">}</span>
   <span class="n">ort_outs</span> <span class="o">=</span> <span class="n">inference_session</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="n">ort_inputs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
   <span class="k">return</span> <span class="n">ort_outs</span>


 <div class="viewcode-block" id="OnnxModelHandlerNumpy"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.onnx_inference.html#apache_beam.ml.inference.onnx_inference.OnnxModelHandlerNumpy">[docs]</a><span class="k">class</span> <span class="nc">OnnxModelHandlerNumpy</span><span class="p">(</span><span class="n">ModelHandler</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                                          <span class="n">PredictionResult</span><span class="p">,</span>
                                          <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">]):</span>
   <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> <span class="c1">#pylint: disable=dangerous-default-value</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">model_uri</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
       <span class="n">session_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
       <span class="n">providers</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;CUDAExecutionProvider&#39;</span><span class="p">,</span> <span class="s1">&#39;CPUExecutionProvider&#39;</span><span class="p">],</span>
       <span class="n">provider_options</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
       <span class="o">*</span><span class="p">,</span>
       <span class="n">inference_fn</span><span class="p">:</span> <span class="n">NumpyInferenceFn</span> <span class="o">=</span> <span class="n">default_numpy_inference_fn</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot; Implementation of the ModelHandler interface for onnx</span>
 <span class="sd">    using numpy arrays as input.</span>
 <span class="sd">    Note that inputs to ONNXModelHandler should be of the same sizes</span>

 <span class="sd">    Example Usage::</span>

 <span class="sd">      pcoll | RunInference(OnnxModelHandler(model_uri=&quot;my_uri&quot;))</span>

 <span class="sd">    Args:</span>
 <span class="sd">      model_uri: The URI to where the model is saved.</span>
 <span class="sd">      inference_fn: The inference function to use on RunInference calls.</span>
 <span class="sd">        default=default_numpy_inference_fn</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_model_uri</span> <span class="o">=</span> <span class="n">model_uri</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_session_options</span> <span class="o">=</span> <span class="n">session_options</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_providers</span> <span class="o">=</span> <span class="n">providers</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_provider_options</span> <span class="o">=</span> <span class="n">provider_options</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">_model_inference_fn</span> <span class="o">=</span> <span class="n">inference_fn</span>

 <div class="viewcode-block" id="OnnxModelHandlerNumpy.load_model"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.onnx_inference.html#apache_beam.ml.inference.onnx_inference.OnnxModelHandlerNumpy.load_model">[docs]</a>  <span class="k">def</span> <span class="nf">load_model</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Loads and initializes an onnx inference session for processing.&quot;&quot;&quot;</span>
     <span class="c1"># when path is remote, we should first load into memory then deserialize</span>
     <span class="n">f</span> <span class="o">=</span> <span class="n">FileSystems</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_model_uri</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span>
     <span class="n">model_proto</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">f</span><span class="p">)</span>
     <span class="n">model_proto_bytes</span> <span class="o">=</span> <span class="n">onnx</span><span class="o">.</span><span class="n">_serialize</span><span class="p">(</span><span class="n">model_proto</span><span class="p">)</span>
     <span class="n">ort_session</span> <span class="o">=</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span>
         <span class="n">model_proto_bytes</span><span class="p">,</span>
         <span class="n">sess_options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_session_options</span><span class="p">,</span>
         <span class="n">providers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_providers</span><span class="p">,</span>
         <span class="n">provider_options</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_provider_options</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">ort_session</span></div>

 <div class="viewcode-block" id="OnnxModelHandlerNumpy.run_inference"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.onnx_inference.html#apache_beam.ml.inference.onnx_inference.OnnxModelHandlerNumpy.run_inference">[docs]</a>  <span class="k">def</span> <span class="nf">run_inference</span><span class="p">(</span>
       <span class="bp">self</span><span class="p">,</span>
       <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">],</span>
       <span class="n">inference_session</span><span class="p">:</span> <span class="n">ort</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">,</span>
       <span class="n">inference_args</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
   <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">PredictionResult</span><span class="p">]:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Runs inferences on a batch of numpy arrays.</span>

 <span class="sd">    Args:</span>
 <span class="sd">      batch: A sequence of examples as numpy arrays. They should</span>
 <span class="sd">        be single examples.</span>
 <span class="sd">      inference_session: An onnx inference session.</span>
 <span class="sd">        Must be runnable with input x where x is sequence of numpy array</span>
 <span class="sd">      inference_args: Any additional arguments for an inference.</span>

 <span class="sd">    Returns:</span>
 <span class="sd">      An Iterable of type PredictionResult.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_model_inference_fn</span><span class="p">(</span>
         <span class="n">inference_session</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">inference_args</span><span class="p">)</span>

     <span class="k">return</span> <span class="n">utils</span><span class="o">.</span><span class="n">_convert_to_result</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span></div>

 <div class="viewcode-block" id="OnnxModelHandlerNumpy.get_num_bytes"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.onnx_inference.html#apache_beam.ml.inference.onnx_inference.OnnxModelHandlerNumpy.get_num_bytes">[docs]</a>  <span class="k">def</span> <span class="nf">get_num_bytes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">      The number of bytes of data for a batch.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="nb">sum</span><span class="p">((</span><span class="n">np_array</span><span class="o">.</span><span class="n">itemsize</span> <span class="k">for</span> <span class="n">np_array</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">))</span></div>

 <div class="viewcode-block" id="OnnxModelHandlerNumpy.get_metrics_namespace"><a class="viewcode-back" href="../../../../apache_beam.ml.inference.onnx_inference.html#apache_beam.ml.inference.onnx_inference.OnnxModelHandlerNumpy.get_metrics_namespace">[docs]</a>  <span class="k">def</span> <span class="nf">get_metrics_namespace</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns:</span>
 <span class="sd">       A namespace for metrics collected by the RunInference transform.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="s1">&#39;BeamML_Onnx&#39;</span></div></div>
 </pre></div>

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