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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">optim</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.engine</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.handlers</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.metrics.regression</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ignite.utils</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
<span class="c1"># create default evaluator for doctests</span>
<span class="k">def</span><span class="w"> </span><span class="nf">eval_step</span><span class="p">(</span><span class="n">engine</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
<span class="k">return</span> <span class="n">batch</span>
<span class="n">default_evaluator</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">eval_step</span><span class="p">)</span>
<span class="c1"># create default optimizer for doctests</span>
<span class="n">param_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">1</span><span class="p">],</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">default_optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">([</span><span class="n">param_tensor</span><span class="p">],</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="c1"># create default trainer for doctests</span>
<span class="c1"># as handlers could be attached to the trainer,</span>
<span class="c1"># each test must define his own trainer using `.. testsetup:`</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_default_trainer</span><span class="p">():</span>
<span class="k">def</span><span class="w"> </span><span class="nf">train_step</span><span class="p">(</span><span class="n">engine</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
<span class="k">return</span> <span class="n">batch</span>
<span class="k">return</span> <span class="n">Engine</span><span class="p">(</span><span class="n">train_step</span><span class="p">)</span>
<span class="c1"># create default model for doctests</span>
<span class="n">default_model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([</span>
<span class="p">(</span><span class="s1">'base'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span>
<span class="p">(</span><span class="s1">'fc'</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="p">]))</span>
<span class="n">manual_seed</span><span class="p">(</span><span class="mi">666</span><span class="p">)</span>
</pre></div>
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