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@@ -8,7 +8,7 @@
   
   
   <link rel="shortcut icon" href="/img/favicon.ico">
-  <title>PaddleSlim</title>
+  <title>PaddleSlim Docs</title>
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@@ -29,7 +29,7 @@
     
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       <div class="wy-side-nav-search">
-        <a href="/." class="icon icon-home"> PaddleSlim</a>
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@@ -43,80 +43,59 @@
           
             <li class="toctree-l1">
 		
-    <a class="" href="/.">Welcome to MkDocs</a>
+    <a class="" href="/.">Home</a>
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+    <span class="caption-text">教程</span>
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-    <a class="" href="/docs/">PaddleSlim</a>
+    <a class="" href="/tutorials/quant_post_demo/">离线量化</a>
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-    <a class="" href="/docs/table_latency/">硬件延时评估表</a>
+    <a class="" href="/tutorials/quant_aware_demo/">量化训练</a>
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-    <span class="caption-text">Api</span>
-    <ul class="subnav">
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-    <a class="" href="/docs/api/prune_api/">卷积通道剪裁API文档</a>
+    <a class="" href="/tutorials/quant_embedding_demo/">Embedding量化</a>
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-                <li class="toctree-l3">
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-    <a class="" href="/docs/api/quantization_api/">paddleslim.quant API文档</a>
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-    <a class="" href="/docs/api/search_space/">paddleslim.nas 提供的搜索空间：</a>
-                </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="/docs/api/single_distiller_api/">paddleslim.dist API文档</a>
+    <a class="" href="/tutorials/nas_demo/">SA搜索</a>
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-                <li class="">
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-    <span class="caption-text">Tutorials</span>
+	    </li>
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+            <li class="toctree-l1">
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+    <span class="caption-text">API</span>
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-    <a class="" href="/docs/tutorials/demo_guide/">Demo guide</a>
+    <a class="" href="/api/quantization_api/">量化</a>
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-                <li class="toctree-l3">
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-    <a class="" href="/docs/tutorials/nas_demo/">网络结构搜索示例</a>
+    <a class="" href="/api/prune_api/">剪枝</a>
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-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="/docs/tutorials/quant_aware_demo/">在线量化示例</a>
+    <a class="" href="/api/analysis_api/">敏感度分析</a>
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-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="/docs/tutorials/quant_embedding_demo/">Embedding量化示例</a>
+    <a class="" href="/api/single_distiller_api/">蒸馏</a>
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-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="/docs/tutorials/quant_post_demo/">离线量化示例</a>
+    <a class="" href="/api/nas_api/">SA搜索</a>
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-    </ul>
+                <li class="">
+                    
+    <a class="" href="/api/search_space/">搜索空间</a>
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@@ -131,7 +110,7 @@
       
       <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
         <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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+        <a href="/.">PaddleSlim Docs</a>
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@@ -182,12 +161,16 @@
   <div class="rst-versions" role="note" style="cursor: pointer">
     <span class="rst-current-version" data-toggle="rst-current-version">
       
+          <a href="https://github.com/PaddlePaddle/PaddleSlim/" class="fa fa-github" style="float: left; color: #fcfcfc"> GitHub</a>
+      
       
       
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docs/api/api_guide/index.html → api/api_guide/index.html

@@ -7,23 +7,23 @@
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-  <link rel="shortcut icon" href="../../../img/favicon.ico">
-  <title>PaddleSlim API文档导航 - PaddleSlim</title>
+  <link rel="shortcut icon" href="../../img/favicon.ico">
+  <title>PaddleSlim API文档导航 - PaddleSlim Docs</title>
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+    var mkdocs_page_input_path = "api/api_guide.md";
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@@ -36,9 +36,9 @@
     
     <nav data-toggle="wy-nav-shift" class="wy-nav-side stickynav">
       <div class="wy-side-nav-search">
-        <a href="../../.." class="icon icon-home"> PaddleSlim</a>
+        <a href="../.." class="icon icon-home"> PaddleSlim Docs</a>
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-  <form id ="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+  <form id ="rtd-search-form" class="wy-form" action="../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" title="Type search term here" />
   </form>
 </div>
@@ -50,98 +50,59 @@
           
             <li class="toctree-l1">
 		
-    <a class="" href="../../..">Welcome to MkDocs</a>
+    <a class="" href="../..">Home</a>
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-    <span class="caption-text">Docs</span>
+    <span class="caption-text">教程</span>
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                 <li class="">
                     
-    <a class="" href="../../">PaddleSlim</a>
+    <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
                     
-    <a class="" href="../../table_latency/">硬件延时评估表</a>
+    <a class="" href="../../tutorials/quant_aware_demo/">量化训练</a>
                 </li>
-                <li class=" current">
-                    
-    <span class="caption-text">Api</span>
-    <ul class="subnav">
-                <li class="toctree-l3">
-                    
-    <a class="" href="../analysis_api/">模型分析API文档</a>
-                </li>
-                <li class="toctree-l3 current">
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-    <a class="current" href="./">PaddleSlim API文档导航</a>
-    <ul class="subnav">
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-    <li class="toctree-l4"><a href="#paddleslim-api">PaddleSlim API文档导航</a></li>
-    
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-        
-            <li><a class="toctree-l5" href="#_1">模型分析</a></li>
-        
-            <li><a class="toctree-l5" href="#_2">卷积通道剪裁</a></li>
-        
-            <li><a class="toctree-l5" href="#_3">蒸馏</a></li>
-        
-            <li><a class="toctree-l5" href="#_5">小模型结构搜索</a></li>
-        
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-    
-
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-    <a class="" href="../nas_api/">paddleslim.nas API文档</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="../prune_api/">卷积通道剪裁API文档</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="../quantization_api/">paddleslim.quant API文档</a>
-                </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../search_space/">paddleslim.nas 提供的搜索空间：</a>
+    <a class="" href="../../tutorials/quant_embedding_demo/">Embedding量化</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../single_distiller_api/">paddleslim.dist API文档</a>
+    <a class="" href="../../tutorials/nas_demo/">SA搜索</a>
                 </li>
     </ul>
-                </li>
-                <li class="">
-                    
-    <span class="caption-text">Tutorials</span>
+	    </li>
+          
+            <li class="toctree-l1">
+		
+    <span class="caption-text">API</span>
     <ul class="subnav">
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../../tutorials/demo_guide/">Demo guide</a>
+    <a class="" href="../quantization_api/">量化</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../../tutorials/nas_demo/">网络结构搜索示例</a>
+    <a class="" href="../prune_api/">剪枝</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../../tutorials/quant_aware_demo/">在线量化示例</a>
+    <a class="" href="../analysis_api/">敏感度分析</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../../tutorials/quant_embedding_demo/">Embedding量化示例</a>
+    <a class="" href="../single_distiller_api/">蒸馏</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="../../tutorials/quant_post_demo/">离线量化示例</a>
+    <a class="" href="../nas_api/">SA搜索</a>
                 </li>
-    </ul>
+                <li class="">
+                    
+    <a class="" href="../search_space/">搜索空间</a>
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@@ -156,7 +117,7 @@
       
       <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
         <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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@@ -164,21 +125,16 @@
         <div class="rst-content">
           <div role="navigation" aria-label="breadcrumbs navigation">
   <ul class="wy-breadcrumbs">
-    <li><a href="../../..">Docs</a> &raquo;</li>
+    <li><a href="../..">Docs</a> &raquo;</li>
     
       
-        
-          <li>Api &raquo;</li>
-        
-      
-        
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-        
-      
     
     <li>PaddleSlim API文档导航</li>
     <li class="wy-breadcrumbs-aside">
       
+        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/api/api_guide.md"
+          class="icon icon-github"> Edit on GitHub</a>
+      
     </li>
   </ul>
   <hr/>
@@ -186,10 +142,10 @@
           <div role="main">
             <div class="section">
               
-                <h1 id="paddleslim-api">PaddleSlim API文档导航</h1>
-<h2 id="_1"><a href="../analysis_api/">模型分析</a></h2>
-<h2 id="_2"><a href="../prune_api/">卷积通道剪裁</a></h2>
-<h2 id="_3"><a href="">蒸馏</a></h2>
+                <h1 id="paddleslim-api">PaddleSlim API文档导航<a class="headerlink" href="#paddleslim-api" title="Permanent link">#</a></h1>
+<h2 id="_1"><a href="../analysis_api/">模型分析</a><a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
+<h2 id="_2"><a href="../prune_api/">卷积通道剪裁</a><a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
+<h2 id="_3"><a href="">蒸馏</a><a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
 <ul>
 <li>
 <p><a href="../single_distiller_api/">单进程蒸馏</a></p>
@@ -198,7 +154,7 @@
 <p><a href="../prune_api/">通道剪裁</a></p>
 </li>
 </ul>
-<h3 id="_4"><a href="../quantization_api/">量化</a></h3>
+<h3 id="_4"><a href="../quantization_api/">量化</a><a class="headerlink" href="#_4" title="Permanent link">#</a></h3>
 <ul>
 <li>
 <p><a href="../quantization_api/#量化训练API">量化训练</a></p>
@@ -210,7 +166,7 @@
 <p><a href="../quantization_api/#Embedding量化API">embedding量化</a></p>
 </li>
 </ul>
-<h2 id="_5"><a href="">小模型结构搜索</a></h2>
+<h2 id="_5"><a href="">小模型结构搜索</a><a class="headerlink" href="#_5" title="Permanent link">#</a></h2>
 <ul>
 <li><a href="../nas_api/">nas API</a></li>
 <li><a href="../search_space/">SearchSpace</a></li>
@@ -220,15 +176,6 @@
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@@ -250,17 +197,17 @@
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     <span class="rst-current-version" data-toggle="rst-current-version">
       
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+          <a href="https://github.com/PaddlePaddle/PaddleSlim/" class="fa fa-github" style="float: left; color: #fcfcfc"> GitHub</a>
       
       
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-                <h1 id="api">模型分析API文档</h1>
-<h2 id="flops">flops</h2>
-<blockquote>
-<p>paddleslim.analysis.flops(program, detail=False) <a href="">源代码</a></p>
-</blockquote>
-<p>获得指定网络的每秒浮点运算次数(FLOPS)。</p>
-<p><strong>参数：</strong></p>
-<ul>
-<li>
-<p><strong>program(paddle.fluid.Program):</strong>  待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</p>
-</li>
-<li>
-<p><strong>detail(bool):</strong> 是否返回每个卷积层的FLOPS。默认为False。</p>
-</li>
-</ul>
-<p><strong>返回值：</strong></p>
-<ul>
-<li>
-<p><strong>flops(float):</strong> 整个网络的FLOPS。</p>
-</li>
-<li>
-<p><strong>params2flops(dict):</strong> 每层卷积对应的FLOPS，其中key为卷积层参数名称，value为FLOPS值。</p>
-</li>
-</ul>
-<p><strong>示例：</strong></p>
-<pre><code>import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddleslim.analysis import flops
-
-def conv_bn_layer(input,
-                  num_filters,
-                  filter_size,
-                  name,
-                  stride=1,
-                  groups=1,
-                  act=None):
-    conv = fluid.layers.conv2d(
-        input=input,
-        num_filters=num_filters,
-        filter_size=filter_size,
-        stride=stride,
-        padding=(filter_size - 1) // 2,
-        groups=groups,
-        act=None,
-        param_attr=ParamAttr(name=name + &quot;_weights&quot;),
-        bias_attr=False,
-        name=name + &quot;_out&quot;)
-    bn_name = name + &quot;_bn&quot;
-    return fluid.layers.batch_norm(
-        input=conv,
-        act=act,
-        name=bn_name + '_output',
-        param_attr=ParamAttr(name=bn_name + '_scale'),
-        bias_attr=ParamAttr(bn_name + '_offset'),
-        moving_mean_name=bn_name + '_mean',
-        moving_variance_name=bn_name + '_variance', )
-
-main_program = fluid.Program()
-startup_program = fluid.Program()
-#   X       X              O       X              O
-# conv1--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6
-#     |            ^ |                    ^
-#     |____________| |____________________|
-#
-# X: prune output channels
-# O: prune input channels
-with fluid.program_guard(main_program, startup_program):
-    input = fluid.data(name=&quot;image&quot;, shape=[None, 3, 16, 16])
-    conv1 = conv_bn_layer(input, 8, 3, &quot;conv1&quot;)
-    conv2 = conv_bn_layer(conv1, 8, 3, &quot;conv2&quot;)
-    sum1 = conv1 + conv2
-    conv3 = conv_bn_layer(sum1, 8, 3, &quot;conv3&quot;)
-    conv4 = conv_bn_layer(conv3, 8, 3, &quot;conv4&quot;)
-    sum2 = conv4 + sum1
-    conv5 = conv_bn_layer(sum2, 8, 3, &quot;conv5&quot;)
-    conv6 = conv_bn_layer(conv5, 8, 3, &quot;conv6&quot;)
-
-print(&quot;FLOPS: {}&quot;.format(flops(main_program)))
-</code></pre>
-
-<h2 id="model_size">model_size</h2>
-<blockquote>
-<p>paddleslim.analysis.model_size(program) <a href="">源代码</a></p>
-</blockquote>
-<p>获得指定网络的参数数量。</p>
-<p><strong>参数：</strong></p>
-<ul>
-<li><strong>program(paddle.fluid.Program):</strong>  待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</li>
-</ul>
-<p><strong>返回值：</strong></p>
-<ul>
-<li><strong>model_size(int):</strong> 整个网络的参数数量。</li>
-</ul>
-<p><strong>示例：</strong></p>
-<pre><code>import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddleslim.analysis import model_size
-
-def conv_layer(input,
-                  num_filters,
-                  filter_size,
-                  name,
-                  stride=1,
-                  groups=1,
-                  act=None):
-    conv = fluid.layers.conv2d(
-        input=input,
-        num_filters=num_filters,
-        filter_size=filter_size,
-        stride=stride,
-        padding=(filter_size - 1) // 2,
-        groups=groups,
-        act=None,
-        param_attr=ParamAttr(name=name + &quot;_weights&quot;),
-        bias_attr=False,
-        name=name + &quot;_out&quot;)
-    return conv
-
-main_program = fluid.Program()
-startup_program = fluid.Program()
-#   X       X              O       X              O
-# conv1--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6
-#     |            ^ |                    ^
-#     |____________| |____________________|
-#
-# X: prune output channels
-# O: prune input channels
-with fluid.program_guard(main_program, startup_program):
-    input = fluid.data(name=&quot;image&quot;, shape=[None, 3, 16, 16])
-    conv1 = conv_layer(input, 8, 3, &quot;conv1&quot;)
-    conv2 = conv_layer(conv1, 8, 3, &quot;conv2&quot;)
-    sum1 = conv1 + conv2
-    conv3 = conv_layer(sum1, 8, 3, &quot;conv3&quot;)
-    conv4 = conv_layer(conv3, 8, 3, &quot;conv4&quot;)
-    sum2 = conv4 + sum1
-    conv5 = conv_layer(sum2, 8, 3, &quot;conv5&quot;)
-    conv6 = conv_layer(conv5, 8, 3, &quot;conv6&quot;)
-
-print(&quot;FLOPS: {}&quot;.format(model_size(main_program)))
-</code></pre>
-
-<h2 id="tablelatencyevaluator">TableLatencyEvaluator</h2>
-<blockquote>
-<p>paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") <a href="">源代码</a></p>
-</blockquote>
-<p>基于硬件延时表的模型延时评估器。</p>
-<p><strong>参数：</strong></p>
-<ul>
-<li>
-<p><strong>table_file(str):</strong> 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：<a href="../paddleslim/analysis/table_latency.md">PaddleSlim硬件延时评估表格式</a></p>
-</li>
-<li>
-<p><strong>delimiter(str):</strong> 硬件延时评估表中，操作信息之前所使用的分割符，默认为英文字符逗号。</p>
-</li>
-</ul>
-<p><strong>返回值：</strong></p>
-<ul>
-<li><strong>Evaluator:</strong> 硬件延时评估器的实例。</li>
-</ul>
-<blockquote>
-<p>paddleslim.analysis.TableLatencyEvaluator.latency(graph) <a href="">源代码</a></p>
-</blockquote>
-<p>获得指定网络的预估延时。</p>
-<p><strong>参数：</strong></p>
-<ul>
-<li><strong>graph(Program):</strong> 待预估的目标网络。</li>
-</ul>
-<p><strong>返回值：</strong></p>
-<ul>
-<li><strong>latency:</strong> 目标网络的预估延时。</li>
-</ul>
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-                <h1 id="paddleslimnas-api">paddleslim.nas API文档</h1>
-<h2 id="sanas-api">SANAS API文档</h2>
-<h2 id="class-sanas">class SANAS</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>
-<p><strong>示例代码：</strong></p>
-<pre><code>from paddleslim.nas import SANAS
-config = [('MobileNetV2Space')]
-sanas = SANAS(config=config)
-</code></pre>
-
-<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>
-<p><strong>示例代码：</strong></p>
-<pre><code>import paddle.fluid as fluid
-input = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
-archs = sanas.token2arch(tokens)
-for arch in archs:
-    output = arch(input)
-    input = output
-</code></pre>
-
-<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>
-<p><strong>示例代码：</strong></p>
-<pre><code>import paddle.fluid as fluid
-input = fluid.data(name='input', shape=[None, 3, 32, 32], dtype='float32')
-archs = sanas.next_archs()
-for arch in archs:
-    output = arch(input)
-    input = output
-</code></pre>
-
-<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>
-<p><strong>代码示例</strong></p>
-<pre><code class="python">import numpy as np
-import paddle
-import paddle.fluid as fluid
-from paddleslim.nas import SANAS
-from paddleslim.analysis import flops
-
-max_flops = 321208544
-batch_size = 256
-
-# 搜索空间配置
-config=[('MobileNetV2Space')] 
-
-# 实例化SANAS
-sa_nas = SANAS(config, server_addr=(&quot;&quot;, 8887), init_temperature=10.24, reduce_rate=0.85, search_steps=100, is_server=True)
-
-for step in range(100):
-    archs = sa_nas.next_archs()
-    train_program = fluid.Program()
-    test_program = fluid.Program()
-    startup_program = fluid.Program()
-    ### 构造训练program
-    with fluid.program_guard(train_program, startup_program):
-        image = fluid.data(name='image', shape=[None, 3, 32, 32], dtype='float32')
-        label = fluid.data(name='label', shape=[None, 1], dtype='int64')
-
-        for arch in archs:
-            output = arch(image)
-        out = fluid.layers.fc(output, size=10, act=&quot;softmax&quot;) 
-        softmax_out = fluid.layers.softmax(input=out, use_cudnn=False)
-        cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
-        avg_cost = fluid.layers.mean(cost)
-        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
-
-        ### 构造测试program
-        test_program = train_program.clone(for_test=True)
-        ### 定义优化器
-        sgd = fluid.optimizer.SGD(learning_rate=1e-3)
-        sgd.minimize(avg_cost)
-
-
-    ### 增加限制条件，如果没有则进行无限制搜索
-    if flops(train_program) &gt; max_flops:
-        continue
-
-    ### 定义代码是在cpu上运行
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    exe.run(startup_program)
-
-    ### 定义训练输入数据
-    train_reader = paddle.batch(
-        paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
-        batch_size=batch_size,
-        drop_last=True)
-
-    ### 定义预测输入数据
-    test_reader = paddle.batch(
-        paddle.dataset.cifar.test10(cycle=False),
-        batch_size=batch_size,
-        drop_last=False)
-    train_feeder = fluid.DataFeeder([image, label], place, program=train_program)
-    test_feeder = fluid.DataFeeder([image, label], place, program=test_program)
-
-
-    ### 开始训练，每个搜索结果训练5个epoch
-    for epoch_id in range(5):
-        for batch_id, data in enumerate(train_reader()):
-            fetches = [avg_cost.name]
-            outs = exe.run(train_program,
-                           feed=train_feeder.feed(data),
-                           fetch_list=fetches)[0]
-            if batch_id % 10 == 0:
-                print('TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}'.format(step, epoch_id, batch_id, outs[0]))
-
-    ### 开始预测，得到最终的测试结果作为score回传给sa_nas
-    reward = []
-    for batch_id, data in enumerate(test_reader()):
-        test_fetches = [
-            avg_cost.name, acc_top1.name
-        ]
-        batch_reward = exe.run(test_program,
-                               feed=test_feeder.feed(data),
-                               fetch_list=test_fetches)
-        reward_avg = np.mean(np.array(batch_reward), axis=1)
-        reward.append(reward_avg)
-
-        print('TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}'.
-            format(step, batch_id, batch_reward[0],batch_reward[1]))
-
-    finally_reward = np.mean(np.array(reward), axis=0)
-    print(
-        'FINAL TEST: avg_cost: {}, acc_top1: {}'.format(
-            finally_reward[0], finally_reward[1]))
-
-    ### 回传score
-    sa_nas.reward(float(finally_reward[1]))
-
-</code></pre>
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-    <a class="current" href="./">paddleslim.nas 提供的搜索空间：</a>
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-            
-    <li class="toctree-l4"><a href="#paddleslimnas">paddleslim.nas 提供的搜索空间：</a></li>
-    
-        <ul>
-        
-            <li><a class="toctree-l5" href="#_1">搜索空间的配置介绍：</a></li>
-        
-            <li><a class="toctree-l5" href="#_2">搜索空间示例：</a></li>
-        
-        </ul>
-    
-
-    <li class="toctree-l4"><a href="#search-space">自定义搜索空间(search space)</a></li>
-    
-
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-                <h1 id="paddleslimnas">paddleslim.nas 提供的搜索空间：</h1>
-<ol>
-<li>根据原本模型结构构造搜索空间：</li>
-</ol>
-<p>1.1 MobileNetV2Space</p>
-<p>1.2 MobileNetV1Space</p>
-<p>1.3 ResNetSpace</p>
-<ol>
-<li>根据相应模型的block构造搜索空间</li>
-</ol>
-<p>2.1 MobileNetV1BlockSpace</p>
-<p>2.2 MobileNetV2BlockSpace</p>
-<p>2.3 ResNetBlockSpace</p>
-<p>2.4 InceptionABlockSpace</p>
-<p>2.5 InceptionCBlockSpace</p>
-<h2 id="_1">搜索空间的配置介绍：</h2>
-<p><strong>input_size(int|None)</strong>：<code>input_size</code>表示输入feature map的大小。
-<strong>output_size(int|None)</strong>：<code>output_size</code>表示输出feature map的大小。
-<strong>block_num(int|None)</strong>：<code>block_num</code>表示搜索空间中block的数量。
-<strong>block_mask(list|None)</strong>：<code>block_mask</code>表示当前的block是一个reduction block还是一个normal block，是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了<code>block_mask</code>，则主要以<code>block_mask</code>为主要配置，<code>input_size</code>，<code>output_size</code>和<code>block_num</code>三种配置是无效的。</p>
-<p><strong>Note:</strong> 
-1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。
-2. <code>input_size</code>和<code>output_size</code>用来计算整个模型结构中reduction block数量。</p>
-<h2 id="_2">搜索空间示例：</h2>
-<ol>
-<li>使用paddleslim中提供用原本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config直接指定为[('MobileNetV2Space')]。</li>
-<li>使用paddleslim中提供的block搜索空间构造搜索空间：
-  2.1 使用<code>input_size</code>, <code>output_size</code>和<code>block_num</code>来构造搜索空间。例如：传入SANAS的config可以指定为[('MobileNetV2BlockSpace', {'input_size': 224, 'output_size': 32, 'block_num': 10})]。
-  2.2 使用<code>block_mask</code>构造搜索空间。例如：传入SANAS的config可以指定为[('MobileNetV2BlockSpace', {'block_mask': [0, 1, 1, 1, 1, 0, 1, 0]})]。</li>
-</ol>
-<h1 id="search-space">自定义搜索空间(search space)</h1>
-<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：
-  1. 初始化的tokens(<code>init_tokens</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]。
-  2. token中每个数字的搜索列表长度(<code>range_table</code>函数)，tokens中每个token的索引范围。
-  3. 根据token产生模型结构(<code>token2arch</code>函数)，根据搜索到的tokens列表产生模型结构。</p>
-<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>
-<pre><code class="python">### 引入搜索空间基类函数和search space的注册类函数
-from .search_space_base import SearchSpaceBase
-from .search_space_registry import SEARCHSPACE
-import numpy as np
-
-### 需要调用注册函数把自定义搜索空间注册到space space中
-@SEARCHSPACE.register
-### 定义一个继承SearchSpaceBase基类的搜索空间的类函数
-class ResNetBlockSpace2(SearchSpaceBase):
-    def __init__(self, input_size, output_size, block_num, block_mask):
-        ### 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
-        ### self.filter_num 代表通道数的搜索列表
-        self.filter_num = np.array([8, 16, 32, 40, 64, 128, 256, 512])
-
-    ### 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
-    def init_tokens(self):
-        return [0] * 3 * len(self.block_mask)
-
-    ### 定义
-    def range_table(self):
-        return [len(self.filter_num)] * 3 * len(self.block_mask)
-
-    def token2arch(self, tokens=None):
-        if tokens == None:
-            tokens = self.init_tokens()
-
-        self.bottleneck_params_list = []
-        for i in range(len(self.block_mask)):
-            self.bottleneck_params_list.append(self.filter_num[tokens[i * 3 + 0]], 
-                                               self.filter_num[tokens[i * 3 + 1]],
-                                               self.filter_num[tokens[i * 3 + 2]],
-                                               2 if self.block_mask[i] == 1 else 1)
-
-        def net_arch(input):
-            for i, layer_setting in enumerate(self.bottleneck_params_list):
-                channel_num, stride = layer_setting[:-1], layer_setting[-1]
-                input = self._resnet_block(input, channel_num, stride, name='resnet_layer{}'.format(i+1))
-
-            return input
-
-        return net_arch
-
-    ### 构造具体block的操作
-    def _resnet_block(self, input, channel_num, stride, name=None):
-        shortcut_conv = self._shortcut(input, channel_num[2], stride, name=name)
-        input = self._conv_bn_layer(input=input, num_filters=channel_num[0], filter_size=1, act='relu', name=name + '_conv0')
-        input = self._conv_bn_layer(input=input, num_filters=channel_num[1], filter_size=3, stride=stride, act='relu', name=name + '_conv1')
-        input = self._conv_bn_layer(input=input, num_filters=channel_num[2], filter_size=1, name=name + '_conv2')
-        return fluid.layers.elementwise_add(x=shortcut_conv, y=input, axis=0, name=name+'_elementwise_add')
-
-    def _shortcut(self, input, channel_num, stride, name=None):
-        channel_in = input.shape[1]
-        if channel_in != channel_num or stride != 1:
-            return self.conv_bn_layer(input, num_filters=channel_num, filter_size=1, stride=stride, name=name+'_shortcut')
-        else:
-            return input
-
-    def _conv_bn_layer(self, input, num_filters, filter_size, stride=1, padding='SAME', act=None, name=None):
-        conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv')
-        bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn')
-        return bn
-</code></pre>
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-site_name: PaddleSlim Docs
-nav:
-- Home: index.md
-- 教程:
-  - 离线量化: tutorials/quant_post_demo.md
-  - 量化训练: tutorials/quant_aware_demo.md
-  - Embedding量化: tutorials/quant_embedding_demo.md
-  - SA搜索: tutorials/nas_demo.md
-- API:
-  - 量化: api/quantization_api.md
-  - 剪枝:  api/prune_api.md
-  - 敏感度分析: api/analysis_api.md
-  - 蒸馏: api/single_distiller_api.md
-  - SA搜索: api/nas_api.md
-  - 搜索空间: api/search_space.md
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     <ul class="subnav">
                 <li class="">
                     
-    <a class="" href="./docs/">PaddleSlim</a>
+    <a class="" href="./tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
                     
-    <a class="" href="./docs/table_latency/">硬件延时评估表</a>
+    <a class="" href="./tutorials/quant_aware_demo/">量化训练</a>
                 </li>
                 <li class="">
                     
-    <span class="caption-text">Api</span>
-    <ul class="subnav">
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/analysis_api/">模型分析API文档</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/api_guide/">PaddleSlim API文档导航</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/nas_api/">paddleslim.nas API文档</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/prune_api/">卷积通道剪裁API文档</a>
+    <a class="" href="./tutorials/quant_embedding_demo/">Embedding量化</a>
                 </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/quantization_api/">paddleslim.quant API文档</a>
-                </li>
-                <li class="toctree-l3">
-                    
-    <a class="" href="./docs/api/search_space/">paddleslim.nas 提供的搜索空间：</a>
-                </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/api/single_distiller_api/">paddleslim.dist API文档</a>
+    <a class="" href="./tutorials/nas_demo/">SA搜索</a>
                 </li>
     </ul>
-                </li>
-                <li class="">
-                    
-    <span class="caption-text">Tutorials</span>
+	    </li>
+          
+            <li class="toctree-l1">
+		
+    <span class="caption-text">API</span>
     <ul class="subnav">
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/tutorials/demo_guide/">Demo guide</a>
+    <a class="" href="./api/quantization_api/">量化</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/tutorials/nas_demo/">网络结构搜索示例</a>
+    <a class="" href="./api/prune_api/">剪枝</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/tutorials/quant_aware_demo/">在线量化示例</a>
+    <a class="" href="./api/analysis_api/">敏感度分析</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/tutorials/quant_embedding_demo/">Embedding量化示例</a>
+    <a class="" href="./api/single_distiller_api/">蒸馏</a>
                 </li>
-                <li class="toctree-l3">
+                <li class="">
                     
-    <a class="" href="./docs/tutorials/quant_post_demo/">离线量化示例</a>
+    <a class="" href="./api/nas_api/">SA搜索</a>
                 </li>
-    </ul>
+                <li class="">
+                    
+    <a class="" href="./api/search_space/">搜索空间</a>
                 </li>
     </ul>
 	    </li>
@@ -131,7 +110,7 @@
       
       <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
         <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-        <a href="./.">PaddleSlim</a>
+        <a href="./.">PaddleSlim Docs</a>
       </nav>
 
       
@@ -189,12 +168,16 @@
   <div class="rst-versions" role="note" style="cursor: pointer">
     <span class="rst-current-version" data-toggle="rst-current-version">
       
+          <a href="https://github.com/PaddlePaddle/PaddleSlim/" class="fa fa-github" style="float: left; color: #fcfcfc"> GitHub</a>
+      
       
       
     </span>
 </div>
     <script>var base_url = '.';</script>
     <script src="./js/theme.js" defer></script>
+      <script src="./mathjax-config.js" defer></script>
+      <script src="./MathJax.js?config=TeX-AMS-MML_HTMLorMML" defer></script>
       <script src="./search/main.js" defer></script>
 
 </body>