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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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<h1 id="one-shot-nas">One Shot NAS 示例<a class="headerlink" href="#one-shot-nas" title="Permanent link">#</a></h1>
<blockquote>
<p>该示例依赖Paddle1.7.0或Paddle develop版本。</p>
</blockquote>
<p>该示例使用MNIST数据，介绍了如何使用PaddleSlim的OneShotNAS接口搜索出一个分类网络。OneShotNAS仅支持动态图，所以该示例完全使用Paddle动态图模式。</p>
<h2 id="_1">关键代码介绍<a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
<p>One-shot网络结构搜索策略包含以下步骤：</p>
<ol>
<li>定义超网络</li>
<li>训练超网络</li>
<li>基于超网络搜索子网络</li>
<li>训练最佳子网络</li>
</ol>
<p>以下按序介绍各个步骤的关键代码。</p>
<h3 id="_2">定义超级网络<a class="headerlink" href="#_2" title="Permanent link">#</a></h3>
<p>按照动态图教程，定义一个分类网络模块，该模块包含4个子模块：<code>_simple_img_conv_pool_1</code>,<code>_simple_img_conv_pool_2</code>,<code>super_net</code>和<code>fc</code>，其中<code>super_net</code>为<code>SuperMnasnet</code>的一个实例。</p>
<p>在前向计算过程中，输入图像先后经过子模块<code>_simple_img_conv_pool_1</code>、<code>super_net</code>、<code>_simple_img_conv_pool_2</code>和<code>fc</code>的前向计算。</p>
<p>代码如下所示：
<div class="codehilite"><pre><span></span><span class="k">class</span> <span class="n">MNIST</span>(<span class="n">fluid</span>.<span class="n">dygraph</span>.<span class="n">Layer</span>):
<span class="n">def</span> <span class="n">__init__</span>(<span class="k">self</span>):
<span class="n">super</span>(<span class="n">MNIST</span>, <span class="k">self</span>).<span class="n">__init__</span>()
<span class="k">self</span>.<span class="n">_simple_img_conv_pool_1</span> = <span class="n">SimpleImgConv</span>(<span class="mi">1</span>, <span class="mi">20</span>, <span class="mi">2</span>, <span class="n">act</span>=<span class="s">&quot;relu&quot;</span>)
<span class="k">self</span>.<span class="n">arch</span> = <span class="n">SuperMnasnet</span>(
<span class="n">name_scope</span>=<span class="s">&quot;super_net&quot;</span>, <span class="n">input_channels</span>=<span class="mi">20</span>, <span class="n">out_channels</span>=<span class="mi">20</span>)
<span class="k">self</span>.<span class="n">_simple_img_conv_pool_2</span> = <span class="n">SimpleImgConv</span>(<span class="mi">20</span>, <span class="mi">50</span>, <span class="mi">2</span>, <span class="n">act</span>=<span class="s">&quot;relu&quot;</span>)
<span class="k">self</span>.<span class="n">pool_2_shape</span> = <span class="mi">50</span> * <span class="mi">13</span> * <span class="mi">13</span>
<span class="n">SIZE</span> = <span class="mi">10</span>
<span class="n">scale</span> = (<span class="mf">2.0</span> / (<span class="k">self</span>.<span class="n">pool_2_shape</span><span class="o">**</span><span class="mi">2</span> * <span class="n">SIZE</span>))**<span class="mf">0.5</span>
<span class="k">self</span>.<span class="n">_fc</span> = <span class="n">Linear</span>(
<span class="k">self</span>.<span class="n">pool_2_shape</span>,
<span class="mi">10</span>,
<span class="n">param_attr</span>=<span class="n">fluid</span>.<span class="n">param_attr</span>.<span class="n">ParamAttr</span>(
<span class="n">initializer</span>=<span class="n">fluid</span>.<span class="n">initializer</span>.<span class="n">NormalInitializer</span>(
<span class="n">loc</span>=<span class="mf">0.0</span>, <span class="n">scale</span>=<span class="n">scale</span>)),
<span class="n">act</span>=<span class="s">&quot;softmax&quot;</span>)
<span class="n">def</span> <span class="n">forward</span>(<span class="k">self</span>, <span class="n">inputs</span>, <span class="n">label</span>=<span class="n">None</span>, <span class="n">tokens</span>=<span class="n">None</span>):
<span class="o">x</span> = <span class="k">self</span>.<span class="n">_simple_img_conv_pool_1</span>(<span class="n">inputs</span>)
<span class="o">x</span> = <span class="k">self</span>.<span class="n">arch</span>(<span class="o">x</span>, <span class="n">tokens</span>=<span class="n">tokens</span>) <span class="c1"># addddddd</span>
<span class="o">x</span> = <span class="k">self</span>.<span class="n">_simple_img_conv_pool_2</span>(<span class="o">x</span>)
<span class="o">x</span> = <span class="n">fluid</span>.<span class="n">layers</span>.<span class="n">reshape</span>(<span class="o">x</span>, <span class="nb">shape</span>=[-<span class="mi">1</span>, <span class="k">self</span>.<span class="n">pool_2_shape</span>])
<span class="o">x</span> = <span class="k">self</span>.<span class="n">_fc</span>(<span class="o">x</span>)
<span class="k">if</span> <span class="n">label</span> <span class="k">is</span> <span class="nb">not</span> <span class="n">None:</span>
<span class="n">acc</span> = <span class="n">fluid</span>.<span class="n">layers</span>.<span class="n">accuracy</span>(<span class="n">input</span>=<span class="o">x</span>, <span class="n">label</span>=<span class="n">label</span>)
<span class="k">return</span> <span class="o">x</span>, <span class="n">acc</span>
<span class="n">else:</span>
<span class="k">return</span> <span class="o">x</span>
</pre></div></p>
<p>动态图模块MNIST的forward函数接受一个参数<code>tokens</code>，用于指定在前向计算中使用的子网络，如果<code>tokens</code>为None，则随机选取一个子网络进行前向计算。</p>
<h3 id="_3">训练超级网络<a class="headerlink" href="#_3" title="Permanent link">#</a></h3>
<p>网络训练的逻辑定义在<code>train_mnist</code>函数中，将<code>tokens</code>参数设置为None，进行超网络训练，即在每个batch选取一个超网络进行训练。</p>
<p>代码如下所示：</p>
<div class="codehilite"><pre><span></span><span class="err">with fluid.dygraph.guard(place):</span>
<span class="err"> model = MNIST()</span>
<span class="err"> train_mnist(args, model)</span>
</pre></div>
<h3 id="_4">搜索最佳子网络<a class="headerlink" href="#_4" title="Permanent link">#</a></h3>
<p>使用PaddleSlim提供的<code>OneShotSearch</code>接口搜索最佳子网络。传入已定义且训练好的超网络实例<code>model</code>和一个用于评估子网络的回调函数<code>test_mnist</code>.</p>
<p>代码如下：</p>
<div class="codehilite"><pre><span></span><span class="err">best_tokens = OneShotSearch(model, test_mnist)</span>
</pre></div>
<h3 id="_5">训练最佳子网络<a class="headerlink" href="#_5" title="Permanent link">#</a></h3>
<p>获得最佳的子网络的编码<code>best_tokens</code>后，调用之前定义的<code>train_mnist</code>方法进行子网络的训练。代码如下：</p>
<div class="codehilite"><pre><span></span><span class="err">train_mnist(args, model, best_tokens)</span>
</pre></div>
<h2 id="_6">启动示例<a class="headerlink" href="#_6" title="Permanent link">#</a></h2>
<p>执行以下代码运行示例：</p>
<div class="codehilite"><pre><span></span><span class="err">python train.py</span>
</pre></div>
<p>执行<code>python train.py --help</code>查看更多可配置选项。</p>
<h2 id="faq">FAQ<a class="headerlink" href="#faq" title="Permanent link">#</a></h2>
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