Skip to content

P
PaddleSlim

PaddlePaddle / PaddleSlim
1 年多 前同步成功

通知 51
Star 1434
Fork 344
P
PaddleSlim
切换分支/标签
查找文件 普通视图历史永久链接Permalink
search_space.html 28.3 KB
Newer Older
W
update pages.  
wanghaoshuang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>搜索空间 &mdash; PaddleSlim 1.0 文档</title>
  

  
  

  

  
  
    

  

  
  
    <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
  

  

  
        <link rel="index" title="索引"
              href="../genindex.html"/>
        <link rel="search" title="搜索" href="../search.html"/>
    <link rel="top" title="PaddleSlim 1.0 文档" href="../index.html"/>
        <link rel="up" title="API文档" href="index.html"/>
        <link rel="next" title="硬件延时评估表" href="table_latency.html"/>
        <link rel="prev" title="简单蒸馏" href="single_distiller_api.html"/> 

  
  <script src="../_static/js/modernizr.min.js"></script>

</head>

<body class="wy-body-for-nav" role="document">

  <div class="wy-grid-for-nav">

    
    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
      <div class="wy-side-scroll">
        <div class="wy-side-nav-search">
          

          
            <a href="../index.html" class="icon icon-home"> PaddleSlim
          

          
          </a>

          
            
            
          

          
<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
    <input type="text" name="q" placeholder="Search docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

          
        </div>

        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          
            
            
                <ul class="current">
<li class="toctree-l1"><a class="reference internal" href="../index_en.html">English Documents</a></li>
W
Update pages.  
wanghaoshuang 已提交
86 
<li class="toctree-l1"><a class="reference internal" href="../intro.html">介绍</a></li>
W
update pages.  
wanghaoshuang 已提交
87 88 89 90 91 92 93 
<li class="toctree-l1"><a class="reference internal" href="../install.html">安装</a></li>
<li class="toctree-l1"><a class="reference internal" href="../quick_start/index.html">快速开始</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">进阶教程</a></li>
<li class="toctree-l1 current"><a class="reference internal" href="index.html">API文档</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="analysis_api.html">模型分析</a></li>
<li class="toctree-l2"><a class="reference internal" href="nas_api.html">SA-NAS</a></li>
<li class="toctree-l2"><a class="reference internal" href="one_shot_api.html">OneShotNAS</a></li>
W
Update pages.  
wanghaoshuang 已提交
94 
<li class="toctree-l2"><a class="reference internal" href="pantheon_api.html">大规模可扩展知识蒸馏框架 Pantheon</a></li>
W
update pages.  
wanghaoshuang 已提交
95 96 97 98 
<li class="toctree-l2"><a class="reference internal" href="prune_api.html">卷积层通道剪裁</a></li>
<li class="toctree-l2"><a class="reference internal" href="quantization_api.html">量化</a></li>
<li class="toctree-l2"><a class="reference internal" href="single_distiller_api.html">简单蒸馏</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">搜索空间</a><ul>
W
Update  
wanghaoshuang 已提交
99 100 101 102 103 104 
<li class="toctree-l3"><a class="reference internal" href="#paddleslim-nas">paddleslim.nas 提供的搜索空间</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#id2">根据初始模型结构构造搜索空间:</a></li>
<li class="toctree-l4"><a class="reference internal" href="#block">根据相应模型的block构造搜索空间:</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#id14">搜索空间使用示例</a></li>
W
update pages.  
wanghaoshuang 已提交
105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 
<li class="toctree-l3"><a class="reference internal" href="#search-space">自定义搜索空间(search space)</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="table_latency.html">硬件延时评估表</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../model_zoo.html">模型库</a></li>
<li class="toctree-l1"><a class="reference internal" href="../algo/algo.html">算法原理</a></li>
</ul>

            
          
        </div>
      </div>
    </nav>

    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">

      
      <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
        <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
        <a href="../index.html">PaddleSlim</a>
      </nav>


      
      <div class="wy-nav-content">
        <div class="rst-content">
          

 



<div role="navigation" aria-label="breadcrumbs navigation">
  <ul class="wy-breadcrumbs">
    <li><a href="../index.html">Docs</a> &raquo;</li>
      
          <li><a href="index.html">API文档</a> &raquo;</li>
      
    <li>搜索空间</li>
      <li class="wy-breadcrumbs-aside">
W
Update  
wanghaoshuang 已提交
149 
            <a href="../_sources/api_cn/search_space.md.txt" rel="nofollow"> View page source</a>
W
update pages.  
wanghaoshuang 已提交
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 
          
        
      </li>
  </ul>
  <hr/>
</div>
          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
           <div itemprop="articleBody">
            
  <div class="section" id="id1">
<h1>搜索空间<a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>搜索空间是神经网络搜索中的一个概念。搜索空间是一系列模型结构的汇集, SANAS主要是利用模拟退火的思想在搜索空间中搜索到一个比较小的模型结构或者一个精度比较高的模型结构。</p>
<div class="section" id="paddleslim-nas">
<h2>paddleslim.nas 提供的搜索空间<a class="headerlink" href="#paddleslim-nas" title="永久链接至标题"></a></h2>
<div class="section" id="id2">
W
Update  
wanghaoshuang 已提交
165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 
<h3>根据初始模型结构构造搜索空间:<a class="headerlink" href="#id2" title="永久链接至标题"></a></h3>
<ol class="arabic simple">
<li>MobileNetV2Space<span class="raw-html-m2r"><br></span>
&amp;emsp; MobileNetV2的网络结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v2.py#L29">代码</a>，<a class="reference external" href="https://arxiv.org/abs/1801.04381">论文</a></li>
<li>MobileNetV1Space<span class="raw-html-m2r"><br></span>
&amp;emsp; MobilNetV1的网络结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v1.py#L29">代码</a>，<a class="reference external" href="https://arxiv.org/abs/1704.04861">论文</a></li>
<li>ResNetSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; ResNetSpace的网络结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/resnet.py#L30">代码</a>，<a class="reference external" href="https://arxiv.org/pdf/1512.03385.pdf">论文</a></li>
</ol>
</div>
<div class="section" id="block">
<h3>根据相应模型的block构造搜索空间:<a class="headerlink" href="#block" title="永久链接至标题"></a></h3>
<ol class="arabic simple">
<li>MobileNetV1BlockSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; MobileNetV1Block的结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v1.py#L173">代码</a></li>
<li>MobileNetV2BlockSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; MobileNetV2Block的结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/mobilenet_v2.py#L174">代码</a></li>
<li>ResNetBlockSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; ResNetBlock的结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/resnet.py#L148">代码</a></li>
<li>InceptionABlockSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; InceptionABlock的结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/inception_v4.py#L140">代码</a></li>
<li>InceptionCBlockSpace<span class="raw-html-m2r"><br></span>
&amp;emsp; InceptionCBlock结构可以参考：<a class="reference external" href="https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/models/inception_v4.py#L291">代码</a></li>
</ol>
</div>
</div>
<div class="section" id="id14">
<h2>搜索空间使用示例<a class="headerlink" href="#id14" title="永久链接至标题"></a></h2>
W
update pages.  
wanghaoshuang 已提交
193 194 
<ol class="arabic simple">
<li>使用paddleslim中提供用初始的模型结构来构造搜索空间的话，仅需要指定搜索空间名字即可。例如：如果使用原本的MobileNetV2的搜索空间进行搜索的话，传入SANAS中的configs直接指定为[(&#8216;MobileNetV2Space&#8217;)]。</li>
W
Update  
wanghaoshuang 已提交
195 196 197 
<li>使用paddleslim中提供的block搜索空间构造搜索空间：<span class="raw-html-m2r"><br></span>
2.1 使用<code class="docutils literal"><span class="pre">input_size</span></code>, <code class="docutils literal"><span class="pre">output_size</span></code>和<code class="docutils literal"><span class="pre">block_num</span></code>来构造搜索空间。例如：传入SANAS的configs可以指定为[(&#8216;MobileNetV2BlockSpace&#8217;, {&#8216;input_size&#8217;: 224, &#8216;output_size&#8217;: 32, &#8216;block_num&#8217;: 10})]。<span class="raw-html-m2r"><br></span>
2.2 使用<code class="docutils literal"><span class="pre">block_mask</span></code>构造搜索空间。例如：传入SANAS的configs可以指定为[(&#8216;MobileNetV2BlockSpace&#8217;, {&#8216;block_mask&#8217;: [0, 1, 1, 1, 1, 0, 1, 0]})]。</li>
W
update pages.  
wanghaoshuang 已提交
198 199 200 201 
</ol>
</div>
<div class="section" id="search-space">
<h2>自定义搜索空间(search space)<a class="headerlink" href="#search-space" title="永久链接至标题"></a></h2>
W
Update  
wanghaoshuang 已提交
202 203 204 205 
<p>自定义搜索空间类需要继承搜索空间基类并重写以下几部分：<span class="raw-html-m2r"><br></span>
&amp;emsp; 1. 初始化的tokens(<code class="docutils literal"><span class="pre">init_tokens</span></code>函数)，可以设置为自己想要的tokens列表, tokens列表中的每个数字指的是当前数字在相应的搜索列表中的索引。例如本示例中若tokens=[0, 3, 5]，则代表当前模型结构搜索到的通道数为[8, 40, 128]。<span class="raw-html-m2r"><br></span>
&amp;emsp; 2. tokens中每个数字的搜索列表长度(<code class="docutils literal"><span class="pre">range_table</span></code>函数)，tokens中每个token的索引范围。<span class="raw-html-m2r"><br></span>
&amp;emsp; 3. 根据tokens产生模型结构(<code class="docutils literal"><span class="pre">token2arch</span></code>函数)，根据搜索到的tokens列表产生模型结构。 <span class="raw-html-m2r"><br></span></p>
W
update pages.  
wanghaoshuang 已提交
206 
<p>以新增reset block为例说明如何构造自己的search space。自定义的search space不能和已有的search space同名。</p>
W
Update  
wanghaoshuang 已提交
207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1">### 引入搜索空间基类函数和search space的注册类函数</span>
<span class="kn">from</span> <span class="nn">.search_space_base</span> <span class="kn">import</span> <span class="n">SearchSpaceBase</span>
<span class="kn">from</span> <span class="nn">.search_space_registry</span> <span class="kn">import</span> <span class="n">SEARCHSPACE</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1">### 需要调用注册函数把自定义搜索空间注册到space space中</span>
<span class="nd">@SEARCHSPACE</span><span class="o">.</span><span class="n">register</span>
<span class="c1">### 定义一个继承SearchSpaceBase基类的搜索空间的类函数</span>
<span class="k">class</span> <span class="nc">ResNetBlockSpace2</span><span class="p">(</span><span class="n">SearchSpaceBase</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="n">input_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">block_num</span><span class="p">,</span> <span class="n">block_mask</span><span class="p">):</span>
        <span class="c1">### 定义一些实际想要搜索的内容，例如：通道数、每个卷积的重复次数、卷积核大小等等</span>
        <span class="c1">### self.filter_num 代表通道数的搜索列表</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">filter_num</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">])</span>

    <span class="c1">### 定义初始化token，初始化token的长度根据传入的block_num或者block_mask的长度来得到的</span>
    <span class="k">def</span> <span class="nf">init_tokens</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">block_mask</span><span class="p">)</span>

    <span class="c1">### 定义token的index的取值范围</span>
    <span class="k">def</span> <span class="nf">range_table</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_num</span><span class="p">)]</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">block_mask</span><span class="p">)</span>

    <span class="c1">### 把token转换成模型结构</span>
    <span class="k">def</span> <span class="nf">token2arch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokens</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">tokens</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_tokens</span><span class="p">()</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_params_list</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">block_mask</span><span class="p">)):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_params_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_num</span><span class="p">[</span><span class="n">tokens</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">+</span> <span class="mi">0</span><span class="p">]],</span>
                                               <span class="bp">self</span><span class="o">.</span><span class="n">filter_num</span><span class="p">[</span><span class="n">tokens</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mi">3</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">filter_num</span><span class="p">[</span><span class="n">tokens</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">+</span> <span class="mi">2</span><span class="p">]],</span>
                                               <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_mask</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">1</span><span class="p">)</span>

        <span class="k">def</span> <span class="nf">net_arch</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">layer_setting</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_params_list</span><span class="p">):</span>
                <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span> <span class="o">=</span> <span class="n">layer_setting</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">layer_setting</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
                <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resnet_block</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet_layer</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>

            <span class="k">return</span> <span class="nb">input</span>

        <span class="k">return</span> <span class="n">net_arch</span>

    <span class="c1">### 构造具体block的操作</span>
    <span class="k">def</span> <span class="nf">_resnet_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">shortcut_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shortcut</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv0&#39;</span><span class="p">)</span>
        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv1&#39;</span><span class="p">)</span>
        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv2&#39;</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">elementwise_add</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">shortcut_conv</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="nb">input</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="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_elementwise_add&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_shortcut</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">channel_in</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="k">if</span> <span class="n">channel_in</span> <span class="o">!=</span> <span class="n">channel_num</span> <span class="ow">or</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_shortcut&#39;</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">return</span> <span class="nb">input</span>

    <span class="k">def</span> <span class="nf">_conv_bn_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="p">,</span> <span class="n">filter_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">conv</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">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="p">,</span> <span class="n">filter_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_conv&#39;</span><span class="p">)</span>
        <span class="n">bn</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">batch_norm</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="n">act</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_bn&#39;</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">bn</span>
</pre></div>
</div>
W
update pages.  
wanghaoshuang 已提交
271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 
</div>
</div>


           </div>
          </div>
          <footer>
  
    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
      
        <a href="table_latency.html" class="btn btn-neutral float-right" title="硬件延时评估表" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
      
      
        <a href="single_distiller_api.html" class="btn btn-neutral" title="简单蒸馏" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
      
    </div>
  

  <hr/>

  <div role="contentinfo">
    <p>
        &copy; Copyright 2020, paddleslim.

    </p>
  </div>
  Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/snide/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 

</footer>

        </div>
      </div>

    </section>

  </div>
  


  

    <script type="text/javascript">
        var DOCUMENTATION_OPTIONS = {
            URL_ROOT:'../',
            VERSION:'1.0',
            COLLAPSE_INDEX:false,
            FILE_SUFFIX:'.html',
            HAS_SOURCE:  true
        };
    </script>
      <script type="text/javascript" src="../_static/jquery.js"></script>
      <script type="text/javascript" src="../_static/underscore.js"></script>
      <script type="text/javascript" src="../_static/doctools.js"></script>
      <script type="text/javascript" src="../_static/translations.js"></script>
      <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>

  

  
  
    <script type="text/javascript" src="../_static/js/theme.js"></script>
  

  
  
  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.StickyNav.enable();
      });
  </script>
   

</body>
</html>