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                <h1 id="paddleslimnas-api">paddleslim.nas API文档<a class="headerlink" href="#paddleslimnas-api" title="Permanent link">#</a></h1>
<h2 id="sanas-api">SANAS API文档<a class="headerlink" href="#sanas-api" title="Permanent link">#</a></h2>
<h2 id="class-sanas">class SANAS<a class="headerlink" href="#class-sanas" title="Permanent link">#</a></h2>
<p>SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。</p>
<hr />
<blockquote>
<p>paddleslim.nas.SANAS(configs, server_addr, init_temperature, reduce_rate, search_steps, save_checkpoint, load_checkpoint, is_server)</p>
</blockquote>
<p><strong>参数：</strong>
- <strong>configs(list<tuple>):</strong> 搜索空间配置列表，格式是<code>[(key, {input_size, output_size, block_num, block_mask})]</code>或者<code>[(key)]</code>（MobileNetV2、MobilenetV1和ResNet的搜索空间使用和原本网络结构相同的搜索空间，所以仅需指定<code>key</code>即可）, <code>input_size</code> 和<code>output_size</code>表示输入和输出的特征图的大小，<code>block_num</code>是指搜索网络中的block数量，<code>block_mask</code>是一组由0和1组成的列表，0代表不进行下采样的block，1代表下采样的block。 更多paddleslim提供的搜索空间配置可以参考。
- <strong>server_addr(tuple):</strong> SANAS的地址，包括server的ip地址和端口号，如果ip地址为None或者为""的话则默认使用本机ip。默认：（"", 8881）。
- <strong>init_temperature(float):</strong> 基于模拟退火进行搜索的初始温度。默认：100。
- <strong>reduce_rate(float):</strong> 基于模拟退火进行搜索的衰减率。默认：0.85。
- <strong>search_steps(int):</strong> 搜索过程迭代的次数。默认：300。
- <strong>save_checkpoint(str|None):</strong> 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：<code>./nas_checkpoint</code>。
- <strong>load_checkpoint(str|None):</strong> 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。
- <strong>is_server(bool):</strong> 当前实例是否要启动一个server。默认：True。</p>
<p><strong>返回：</strong> 
一个SANAS类的实例</p>
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<p><strong>示例代码：</strong>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
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<span class="n">config</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;MobileNetV2Space&#39;</span><span class="p">)]</span>
<span class="n">sanas</span> <span class="o">=</span> <span class="n">SANAS</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
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<hr />
<blockquote>
<p>tokens2arch(tokens)
通过一组token得到实际的模型结构，一般用来把搜索到最优的token转换为模型结构用来做最后的训练。</p>
</blockquote>
<p><strong>参数：</strong>
- <strong>tokens(list):</strong> 一组token。</p>
<p><strong>返回</strong>
返回一个模型结构实例。</p>
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<p><strong>示例代码：</strong>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
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<span class="nb">input</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;input&#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">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</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">archs</span> <span class="o">=</span> <span class="n">sanas</span><span class="o">.</span><span class="n">token2arch</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span>
<span class="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">output</span>
</pre></div>
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<hr />
<blockquote>
<p>next_archs():
获取下一组模型结构。</p>
</blockquote>
<p><strong>返回</strong>
返回模型结构实例的列表，形式为list。</p>
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<p><strong>示例代码：</strong>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2
3
4
5
6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="kn">as</span> <span class="nn">fluid</span>
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<span class="nb">input</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;input&#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">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</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">archs</span> <span class="o">=</span> <span class="n">sanas</span><span class="o">.</span><span class="n">next_archs</span><span class="p">()</span>
<span class="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">output</span>
</pre></div>
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<hr />
<blockquote>
<p>reward(score):
把当前模型结构的得分情况回传。</p>
</blockquote>
<p><strong>参数：</strong>
<strong>score<float>:</strong> 当前模型的得分，分数越大越好。</p>
<p><strong>返回</strong>
模型结构更新成功或者失败，成功则返回<code>True</code>，失败则返回<code>False</code>。</p>
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<p><strong>代码示例</strong>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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97</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
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<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="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
<span class="kn">from</span> <span class="nn">paddleslim.analysis</span> <span class="kn">import</span> <span class="n">flops</span>

<span class="n">max_flops</span> <span class="o">=</span> <span class="mi">321208544</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span>

<span class="c1"># 搜索空间配置</span>
<span class="n">config</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;MobileNetV2Space&#39;</span><span class="p">)]</span> 

<span class="c1"># 实例化SANAS</span>
<span class="n">sa_nas</span> <span class="o">=</span> <span class="n">SANAS</span><span class="p">(</span><span class="n">config</span><span class="p">,</span> <span class="n">server_addr</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="mi">8887</span><span class="p">),</span> <span class="n">init_temperature</span><span class="o">=</span><span class="mf">10.24</span><span class="p">,</span> <span class="n">reduce_rate</span><span class="o">=</span><span class="mf">0.85</span><span class="p">,</span> <span class="n">search_steps</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">is_server</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="k">for</span> <span class="n">step</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
    <span class="n">archs</span> <span class="o">=</span> <span class="n">sa_nas</span><span class="o">.</span><span class="n">next_archs</span><span class="p">()</span>
    <span class="n">train_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">test_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">startup_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="c1">### 构造训练program</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">train_program</span><span class="p">,</span> <span class="n">startup_program</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="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</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="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
            <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
        <span class="n">out</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">fc</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</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="n">softmax_out</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">softmax</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">use_cudnn</span><span class="o">=</span><span class="bp">False</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">softmax_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">cost</span><span class="p">)</span>
        <span class="n">acc_top1</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">softmax_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">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

        <span class="c1">### 构造测试program</span>
        <span class="n">test_program</span> <span class="o">=</span> <span class="n">train_program</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">for_test</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
        <span class="c1">### 定义优化器</span>
        <span class="n">sgd</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
        <span class="n">sgd</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">avg_cost</span><span class="p">)</span>


    <span class="c1">### 增加限制条件，如果没有则进行无限制搜索</span>
    <span class="k">if</span> <span class="n">flops</span><span class="p">(</span><span class="n">train_program</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">max_flops</span><span class="p">:</span>
        <span class="k">continue</span>

    <span class="c1">### 定义代码是在cpu上运行</span>
    <span class="n">place</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CPUPlace</span><span class="p">()</span>
    <span class="n">exe</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Executor</span><span class="p">(</span><span class="n">place</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">startup_program</span><span class="p">)</span>

    <span class="c1">### 定义训练输入数据</span>
    <span class="n">train_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
        <span class="n">paddle</span><span class="o">.</span><span class="n">reader</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span>
            <span class="n">paddle</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">cifar</span><span class="o">.</span><span class="n">train10</span><span class="p">(</span><span class="n">cycle</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span> <span class="n">buf_size</span><span class="o">=</span><span class="mi">1024</span><span class="p">),</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">drop_last</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

    <span class="c1">### 定义预测输入数据</span>
    <span class="n">test_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
        <span class="n">paddle</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">cifar</span><span class="o">.</span><span class="n">test10</span><span class="p">(</span><span class="n">cycle</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
        <span class="n">drop_last</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
    <span class="n">train_feeder</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">DataFeeder</span><span class="p">([</span><span class="n">image</span><span class="p">,</span> <span class="n">label</span><span class="p">],</span> <span class="n">place</span><span class="p">,</span> <span class="n">program</span><span class="o">=</span><span class="n">train_program</span><span class="p">)</span>
    <span class="n">test_feeder</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">DataFeeder</span><span class="p">([</span><span class="n">image</span><span class="p">,</span> <span class="n">label</span><span class="p">],</span> <span class="n">place</span><span class="p">,</span> <span class="n">program</span><span class="o">=</span><span class="n">test_program</span><span class="p">)</span>


    <span class="c1">### 开始训练，每个搜索结果训练5个epoch</span>
    <span class="k">for</span> <span class="n">epoch_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">train_reader</span><span class="p">()):</span>
            <span class="n">fetches</span> <span class="o">=</span> <span class="p">[</span><span class="n">avg_cost</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
            <span class="n">outs</span> <span class="o">=</span> <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">train_program</span><span class="p">,</span>
                           <span class="n">feed</span><span class="o">=</span><span class="n">train_feeder</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="n">data</span><span class="p">),</span>
                           <span class="n">fetch_list</span><span class="o">=</span><span class="n">fetches</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
            <span class="k">if</span> <span class="n">batch_id</span> <span class="o">%</span> <span class="mi">10</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                <span class="k">print</span><span class="p">(</span><span class="s1">&#39;TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">step</span><span class="p">,</span> <span class="n">epoch_id</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">outs</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>

    <span class="c1">### 开始预测，得到最终的测试结果作为score回传给sa_nas</span>
    <span class="n">reward</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">test_reader</span><span class="p">()):</span>
        <span class="n">test_fetches</span> <span class="o">=</span> <span class="p">[</span>
            <span class="n">avg_cost</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">acc_top1</span><span class="o">.</span><span class="n">name</span>
        <span class="p">]</span>
        <span class="n">batch_reward</span> <span class="o">=</span> <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">test_program</span><span class="p">,</span>
                               <span class="n">feed</span><span class="o">=</span><span class="n">test_feeder</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="n">data</span><span class="p">),</span>
                               <span class="n">fetch_list</span><span class="o">=</span><span class="n">test_fetches</span><span class="p">)</span>
        <span class="n">reward_avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">batch_reward</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="n">reward</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">reward_avg</span><span class="p">)</span>

        <span class="k">print</span><span class="p">(</span><span class="s1">&#39;TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}&#39;</span><span class="o">.</span>
            <span class="n">format</span><span class="p">(</span><span class="n">step</span><span class="p">,</span> <span class="n">batch_id</span><span class="p">,</span> <span class="n">batch_reward</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">batch_reward</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>

    <span class="n">finally_reward</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">reward</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="k">print</span><span class="p">(</span>
        <span class="s1">&#39;FINAL TEST: avg_cost: {}, acc_top1: {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="n">finally_reward</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">finally_reward</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>

    <span class="c1">### 回传score</span>
    <span class="n">sa_nas</span><span class="o">.</span><span class="n">reward</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">finally_reward</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
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