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                <h2 id="oneshotsearch">OneShotSearch<a class="headerlink" href="#oneshotsearch" title="Permanent link">#</a></h2>
<dl>
<dt>paddleslim.nas.one_shot.OneShotSearch(model, eval_func, strategy='sa', search_steps=100)<a href="">代码</a></dt>
<dd>
<p>从超级网络中搜索出一个最佳的子网络。</p>
</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>model(fluid.dygraph.layer):</strong> 通过在<code>OneShotSuperNet</code>前后添加若该模块构建的动态图模块。因为<code>OneShotSuperNet</code>是一个超网络，所以<code>model</code>也是一个超网络。换句话说，在<code>model</code>模块的子模块中，至少有一个是<code>OneShotSuperNet</code>的实例。该方法从<code>model</code>超网络中搜索得到一个最佳的子网络。超网络<code>model</code>需要先被训练，具体细节请参考<a href="">OneShotSuperNet</a>。</p>
</li>
<li>
<p><strong>eval_func:</strong> 用于评估子网络性能的回调函数。该回调函数需要接受<code>model</code>为参数，并调用<code>model</code>的<code>forward</code>方法进行性能评估。</p>
</li>
<li>
<p><strong>strategy(str):</strong> 搜索策略的名称。默认为'sa'， 当前仅支持'sa'.</p>
</li>
<li>
<p><strong>search_steps(int):</strong> 搜索轮次数。默认为100。</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>best_tokens:</strong> 表示最佳子网络的编码信息（tokens）。</li>
</ul>
<p><strong>示例代码：</strong></p>
<p>请参考<a href="">one-shot NAS示例</a></p>
<h2 id="oneshotsupernet">OneShotSuperNet<a class="headerlink" href="#oneshotsupernet" title="Permanent link">#</a></h2>
<p>用于<code>OneShot</code>搜索策略的超级网络的基类，所有超级网络的实现要继承该类。</p>
<dl>
<dt>paddleslim.nas.one_shot.OneShotSuperNet(name_scope)</dt>
<dd>
<p>构造方法。</p>
</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li>**name_scope:(str) **超级网络的命名空间。</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>super_net:</strong> 一个<code>OneShotSuperNet</code>实例。</li>
</ul>
<dl>
<dt>init_tokens()</dt>
<dd>
<p>获得当前超级网络的初始化子网络的编码，主要用于搜索。</p>
</dd>
</dl>
<p><strong>返回：</strong></p>
<ul>
<li><strong>tokens(list<int>):</strong> 一个子网络的编码。</li>
</ul>
<dl>
<dt>range_table()</dt>
<dd>
<p>超级网络中各个子网络由一组整型数字编码表示，该方法返回编码每个位置的取值范围。</p>
</dd>
</dl>
<p><strong>返回：</strong></p>
<ul>
<li><strong>range_table(tuple):</strong> 子网络编码每一位的取值范围。<code>range_table</code>格式为<code>(min_values, max_values)</code>，其中，<code>min_values</code>为一个整型数组，表示每个编码位置可选取的最小值；<code>max_values</code>表示每个编码位置可选取的最大值。</li>
</ul>
<dl>
<dt>_forward_impl(input, tokens)</dt>
<dd>
<p>前向计算函数。<code>OneShotSuperNet</code>的子类需要实现该函数。</p>
</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>input(Variable):</strong> 超级网络的输入。</p>
</li>
<li>
<p><strong>tokens(list<int>):</strong> 执行前向计算所用的子网络的编码。默认为<code>None</code>，即随机选取一个子网络执行前向。</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>output(Variable):</strong> 前向计算的输出</li>
</ul>
<dl>
<dt>forward(self, input, tokens=None)</dt>
<dd>
<p>执行前向计算。</p>
</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>input(Variable):</strong> 超级网络的输入。</p>
</li>
<li>
<p><strong>tokens(list<int>):</strong> 执行前向计算所用的子网络的编码。默认为<code>None</code>，即随机选取一个子网络执行前向。</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>output(Variable):</strong> 前向计算的输出</li>
</ul>
<dl>
<dt>_random_tokens()</dt>
<dd>
<p>随机选取一个子网络，并返回其编码。</p>
</dd>
</dl>
<p><strong>返回：</strong></p>
<ul>
<li><strong>tokens(list<int>):</strong> 一个子网络的编码。</li>
</ul>
<h2 id="supermnasnet">SuperMnasnet<a class="headerlink" href="#supermnasnet" title="Permanent link">#</a></h2>
<p>在<a href="https://arxiv.org/abs/1807.11626">Mnasnet</a>基础上修改得到的超级网络, 该类继承自<code>OneShotSuperNet</code>.</p>
<dl>
<dt>paddleslim.nas.one_shot.SuperMnasnet(name_scope, input_channels=3, out_channels=1280, repeat_times=[6, 6, 6, 6, 6, 6], stride=[1, 1, 1, 1, 2, 1], channels=[16, 24, 40, 80, 96, 192, 320], use_auxhead=False)</dt>
<dd>
<p>构造函数。</p>
</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li>
<p><strong>name_scope(str):</strong> 命名空间。</p>
</li>
<li>
<p><strong>input_channels(str):</strong> 当前超级网络的输入的特征图的通道数量。</p>
</li>
<li>
<p><strong>out_channels(str):</strong> 当前超级网络的输出的特征图的通道数量。</p>
</li>
<li>
<p><strong>repeat_times(list):</strong> 每种<code>block</code>重复的次数。</p>
</li>
<li>
<p><strong>stride(list):</strong> 一种<code>block</code>重复堆叠成<code>repeat_block</code>，<code>stride</code>表示每个<code>repeat_block</code>的下采样比例。</p>
</li>
<li>
<p><strong>channels(list):</strong> channels[i]和channels[i+1]分别表示第i个<code>repeat_block</code>的输入特征图的通道数和输出特征图的通道数。</p>
</li>
<li>
<p><strong>use_auxhead(bool):</strong> 是否使用辅助特征图。如果设置为<code>True</code>，则<code>SuperMnasnet</code>除了返回输出特征图，还还返回辅助特征图。默认为False.</p>
</li>
</ul>
<p><strong>返回：</strong></p>
<ul>
<li><strong>instance(SuperMnasnet):</strong> 一个<code>SuperMnasnet</code>实例</li>
</ul>
<p><strong>示例：</strong>
<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</span>
<span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="kn">as</span> <span class="nn">fluid</span>
<span class="k">class</span> <span class="nc">MNIST</span><span class="p">(</span><span class="n">fluid</span><span class="o">.</span><span class="n">dygraph</span><span class="o">.</span><span class="n">Layer</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">MNIST</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">arch</span> <span class="o">=</span> <span class="n">SuperMnasnet</span><span class="p">(</span>
            <span class="n">name_scope</span><span class="o">=</span><span class="s2">&quot;super_net&quot;</span><span class="p">,</span> <span class="n">input_channels</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">pool_2_shape</span> <span class="o">=</span> <span class="mi">50</span> <span class="o">*</span> <span class="mi">13</span> <span class="o">*</span> <span class="mi">13</span>
        <span class="n">SIZE</span> <span class="o">=</span> <span class="mi">10</span>
        <span class="n">scale</span> <span class="o">=</span> <span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_2_shape</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">SIZE</span><span class="p">))</span><span class="o">**</span><span class="mf">0.5</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_fc</span> <span class="o">=</span> <span class="n">Linear</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">pool_2_shape</span><span class="p">,</span>
            <span class="mi">10</span><span class="p">,</span>
            <span class="n">param_attr</span><span class="o">=</span><span class="n">fluid</span><span class="o">.</span><span class="n">param_attr</span><span class="o">.</span><span class="n">ParamAttr</span><span class="p">(</span>
                <span class="n">initializer</span><span class="o">=</span><span class="n">fluid</span><span class="o">.</span><span class="n">initializer</span><span class="o">.</span><span class="n">NormalInitializer</span><span class="p">(</span>
                    <span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="n">scale</span><span class="p">)),</span>
            <span class="n">act</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">tokens</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>

        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">tokens</span><span class="o">=</span><span class="n">tokens</span><span class="p">)</span>
        <span class="n">x</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">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool_2_shape</span><span class="p">])</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fc</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">acc</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">accuracy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">x</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="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">acc</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">x</span>
</pre></div></p>
              
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