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            <li><a class="toctree-l4" href="#4-programmerge">4. 合并Program（merge）</a></li>
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        <a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/tutorials/distillation_demo.md"
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                <p>本示例将介绍如何使用PaddleSlim蒸馏接口来对模型进行蒸馏训练。</p>
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<h2 id="_1">接口介绍<a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
<p>请参考<a href="https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/">蒸馏API文档</a>。</p>
<h2 id="paddleslim">PaddleSlim蒸馏训练流程<a class="headerlink" href="#paddleslim" title="Permanent link">#</a></h2>
<p>一般情况下，模型参数量越多，结构越复杂，其性能越好，但运算量和资源消耗也越大。<strong>知识蒸馏</strong> 就是一种将大模型学习到的有用信息（Dark Knowledge）压缩进更小更快的模型，而获得可以匹敌大模型结果的方法。</p>
<p>在本示例中精度较高的大模型被称为teacher，精度稍逊但速度更快的小模型被称为student。</p>
<h3 id="1-student_program">1. 定义student_program<a class="headerlink" href="#1-student_program" title="Permanent link">#</a></h3>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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11</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">student_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="n">student_startup</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">program_guard</span><span class="p">(</span><span class="n">student_program</span><span class="p">,</span> <span class="n">student_startup</span><span class="p">):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span>
        <span class="n">name</span><span class="o">=</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</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="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
    <span class="n">label</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;label&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int64&#39;</span><span class="p">)</span>
    <span class="c1"># student model definition</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">MobileNet</span><span class="p">()</span>
    <span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">image</span><span class="p">,</span> <span class="n">class_dim</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
    <span class="n">cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
    <span class="n">avg_cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>
</pre></div>
</td></tr></table>

<h3 id="2-teacher_program">2. 定义teacher_program<a class="headerlink" href="#2-teacher_program" title="Permanent link">#</a></h3>
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<p>在定义好<code>teacher_program</code>后，可以一并加载训练好的pretrained_model。</p>
<p>在<code>teacher_program</code>内需要加上<code>with fluid.unique_name.guard():</code>，保证teacher的变量命名不被<code>student_program</code>影响，从而能够正确地加载预训练参数。</p>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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18</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">teacher_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="n">teacher_startup</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">program_guard</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">teacher_startup</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">unique_name</span><span class="o">.</span><span class="n">guard</span><span class="p">():</span>
        <span class="n">image</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span>
            <span class="n">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</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="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
        <span class="c1"># teacher model definition</span>
        <span class="n">teacher_model</span> <span class="o">=</span> <span class="n">ResNet</span><span class="p">()</span>
        <span class="n">predict</span> <span class="o">=</span> <span class="n">teacher_model</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">class_dim</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">teacher_startup</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">if_exist</span><span class="p">(</span><span class="n">var</span><span class="p">):</span>
    <span class="k">return</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="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;./pretrained&quot;</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">fluid</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">load_vars</span><span class="p">(</span>
    <span class="n">exe</span><span class="p">,</span>
    <span class="s2">&quot;./pretrained&quot;</span><span class="p">,</span>
    <span class="n">main_program</span><span class="o">=</span><span class="n">teacher_program</span><span class="p">,</span>
    <span class="n">predicate</span><span class="o">=</span><span class="n">if_exist</span><span class="p">)</span>
</pre></div>
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<h3 id="3">3.选择特征图<a class="headerlink" href="#3" title="Permanent link">#</a></h3>
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<p>定义好<code>student_program</code>和<code>teacher_program</code>后，我们需要从中两两对应地挑选出若干个特征图，留待后续为其添加知识蒸馏损失函数。</p>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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18</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># get all student variables</span>
<span class="n">student_vars</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">student_program</span><span class="o">.</span><span class="n">list_vars</span><span class="p">():</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">student_vars</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
    <span class="k">except</span><span class="p">:</span>
        <span class="k">pass</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;student_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">student_vars</span><span class="p">)</span>
<span class="c1"># get all teacher variables</span>
<span class="n">teacher_vars</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">teacher_program</span><span class="o">.</span><span class="n">list_vars</span><span class="p">():</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="n">teacher_vars</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
    <span class="k">except</span><span class="p">:</span>
        <span class="k">pass</span>
<span class="k">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;teacher_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">teacher_vars</span><span class="p">)</span>
</pre></div>
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<h3 id="4-programmerge">4. 合并Program（merge）<a class="headerlink" href="#4-programmerge" title="Permanent link">#</a></h3>
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<p>PaddlePaddle使用Program来描述计算图，为了同时计算student和teacher两个Program，这里需要将其两者合并（merge）为一个Program。</p>
<p>merge过程操作较多，具体细节请参考<a href="https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/#merge">merge API文档</a>。</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="s1">&#39;image&#39;</span><span class="p">}</span>
<span class="n">student_program</span> <span class="o">=</span> <span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span> <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</span><span class="p">)</span>
</pre></div>
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<h3 id="5loss">5.添加蒸馏loss<a class="headerlink" href="#5loss" title="Permanent link">#</a></h3>
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<p>在添加蒸馏loss的过程中，可能还会引入部分变量（Variable），为了避免命名重复这里可以使用<code>with fluid.name_scope("distill"):</code>为新引入的变量加一个命名作用域。</p>
<p>另外需要注意的是，merge过程为<code>teacher_program</code>的变量统一加了名称前缀，默认是<code>"teacher_"</code>, 这里在添加<code>l2_loss</code>时也要为teacher的变量加上这个前缀。</p>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
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9</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">program_guard</span><span class="p">(</span><span class="n">student_program</span><span class="p">,</span> <span class="n">student_startup</span><span class="p">):</span>
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    <span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s2">&quot;distill&quot;</span><span class="p">):</span>
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        <span class="n">distill_loss</span> <span class="o">=</span> <span class="n">l2_loss</span><span class="p">(</span><span class="s1">&#39;teacher_bn5c_branch2b.output.1.tmp_3&#39;</span><span class="p">,</span>
            <span class="s1">&#39;depthwise_conv2d_11.tmp_0&#39;</span><span class="p">,</span> <span class="n">student_program</span><span class="p">)</span>
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        <span class="n">distill_weight</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="n">loss</span> <span class="o">=</span> <span class="n">avg_cost</span> <span class="o">+</span> <span class="n">distill_loss</span> <span class="o">*</span> <span class="n">distill_weight</span>
        <span class="n">opt</span> <span class="o">=</span> <span class="n">create_optimizer</span><span class="p">()</span>
        <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">student_startup</span><span class="p">)</span>
</pre></div>
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<p>至此，我们就得到了用于蒸馏训练的<code>student_program</code>，后面就可以使用一个普通program一样对其开始训练和评估。</p>
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