Deployed 44b48c82 with MkDocs version: 1.0.4

404.html

@@ -58,6 +58,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="/tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="/tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

algo/algo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

api/analysis_api/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -206,7 +210,7 @@
 </li>
 </ul>
 <p><strong>示例：</strong></p>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddle.fluid.param_attr</span> <span class="kn">import</span> <span class="n">ParamAttr</span>
 <span class="kn">from</span> <span class="nn">paddleslim.analysis</span> <span class="kn">import</span> <span class="n">flops</span>
 
@@ -216,7 +220,7 @@
                   <span class="n">name</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">groups</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="bp">None</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">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="o">=</span><span class="nb">input</span><span class="p">,</span>
         <span class="n">num_filters</span><span class="o">=</span><span class="n">num_filters</span><span class="p">,</span>
@@ -224,9 +228,9 @@
         <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
         <span class="n">padding</span><span class="o">=</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="o">//</span> <span class="mi">2</span><span class="p">,</span>
         <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
-        <span class="n">act</span><span class="o">=</span><span class="bp">None</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">param_attr</span><span class="o">=</span><span class="n">ParamAttr</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="s2">&quot;_weights&quot;</span><span class="p">),</span>
-        <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+        <span class="n">bias_attr</span><span class="o">=</span><span class="kc">False</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="s2">&quot;_out&quot;</span><span class="p">)</span>
     <span class="n">bn_name</span> <span class="o">=</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_bn&quot;</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">batch_norm</span><span class="p">(</span>
@@ -248,7 +252,7 @@
 <span class="c1"># X: prune output channels</span>
 <span class="c1"># O: prune input channels</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">main_program</span><span class="p">,</span> <span class="n">startup_program</span><span class="p">):</span>
-    <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="s2">&quot;image&quot;</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">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
+    <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="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
     <span class="n">conv1</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="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv1&quot;</span><span class="p">)</span>
     <span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv2&quot;</span><span class="p">)</span>
     <span class="n">sum1</span> <span class="o">=</span> <span class="n">conv1</span> <span class="o">+</span> <span class="n">conv2</span>
@@ -258,7 +262,7 @@
     <span class="n">conv5</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">sum2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv5&quot;</span><span class="p">)</span>
     <span class="n">conv6</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">conv5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv6&quot;</span><span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flops</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flops</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
 </pre></div>
 
 <h2 id="model_size">model_size<a class="headerlink" href="#model_size" title="Permanent link">#</a></h2>
@@ -273,7 +277,7 @@
 <li><strong>model_size(int)</strong> - 整个网络的参数数量。</li>
 </ul>
 <p><strong>示例：</strong></p>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddle.fluid.param_attr</span> <span class="kn">import</span> <span class="n">ParamAttr</span>
 <span class="kn">from</span> <span class="nn">paddleslim.analysis</span> <span class="kn">import</span> <span class="n">model_size</span>
 
@@ -283,7 +287,7 @@
                   <span class="n">name</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">groups</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="bp">None</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">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="o">=</span><span class="nb">input</span><span class="p">,</span>
         <span class="n">num_filters</span><span class="o">=</span><span class="n">num_filters</span><span class="p">,</span>
@@ -291,9 +295,9 @@
         <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
         <span class="n">padding</span><span class="o">=</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="o">//</span> <span class="mi">2</span><span class="p">,</span>
         <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
-        <span class="n">act</span><span class="o">=</span><span class="bp">None</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">param_attr</span><span class="o">=</span><span class="n">ParamAttr</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="s2">&quot;_weights&quot;</span><span class="p">),</span>
-        <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+        <span class="n">bias_attr</span><span class="o">=</span><span class="kc">False</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="s2">&quot;_out&quot;</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">conv</span>
 
@@ -307,7 +311,7 @@
 <span class="c1"># X: prune output channels</span>
 <span class="c1"># O: prune input channels</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">main_program</span><span class="p">,</span> <span class="n">startup_program</span><span class="p">):</span>
-    <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="s2">&quot;image&quot;</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">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
+    <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="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
     <span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv1&quot;</span><span class="p">)</span>
     <span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv2&quot;</span><span class="p">)</span>
     <span class="n">sum1</span> <span class="o">=</span> <span class="n">conv1</span> <span class="o">+</span> <span class="n">conv2</span>
@@ -317,7 +321,7 @@
     <span class="n">conv5</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">sum2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv5&quot;</span><span class="p">)</span>
     <span class="n">conv6</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">conv5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv6&quot;</span><span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_size</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_size</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
 </pre></div>
 
 <h2 id="tablelatencyevaluator">TableLatencyEvaluator<a class="headerlink" href="#tablelatencyevaluator" title="Permanent link">#</a></h2>

api/api_guide/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

api/nas_api/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -236,16 +240,16 @@
 <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>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">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="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">configs</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
-<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="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="kc">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>
-<span class="k">print</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
 </pre></div></p>
 <dl>
 <dt>paddleslim.nas.SANAS.reward(score)</dt>
@@ -258,7 +262,7 @@
 <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>示例代码：</strong>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">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="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">configs</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
@@ -279,14 +283,14 @@
 <p><strong>返回：</strong>
 根据传入的token得到一个模型结构实例。</p>
 <p><strong>示例代码：</strong>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">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="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">configs</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
-<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="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="kc">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">tokens</span> <span class="o">=</span> <span class="p">([</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">25</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">tokens2arch</span><span class="p">(</span><span class="n">tokens</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-<span class="k">print</span><span class="p">(</span><span class="n">archs</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">archs</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
 </pre></div></p>
 <dl>
 <dt>paddleslim.nas.SANAS.current_info()</dt>
@@ -295,11 +299,11 @@
 <p><strong>返回：</strong>
 搜索过程中最好的token，reward和当前训练的token，形式为dict。</p>
 <p><strong>示例代码：</strong>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">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="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">configs</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">sanas</span><span class="o">.</span><span class="n">current_info</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">sanas</span><span class="o">.</span><span class="n">current_info</span><span class="p">())</span>
 </pre></div></p>
               
             </div>

api/quantization_api/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -219,9 +223,9 @@
     <span class="c1"># The decay coefficient of moving average, default is 0.9</span>
     <span class="s1">&#39;moving_rate&#39;</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">,</span>
     <span class="c1"># if True, &#39;quantize_op_types&#39; will be TENSORRT_OP_TYPES</span>
-    <span class="s1">&#39;for_tensorrt&#39;</span><span class="p">:</span> <span class="bp">False</span><span class="p">,</span>
+    <span class="s1">&#39;for_tensorrt&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
     <span class="c1"># if True, &#39;quantoze_op_types&#39; will be TRANSFORM_PASS_OP_TYPES + QUANT_DEQUANT_PASS_OP_TYPES</span>
-    <span class="s1">&#39;is_full_quantize&#39;</span><span class="p">:</span> <span class="bp">False</span>
+    <span class="s1">&#39;is_full_quantize&#39;</span><span class="p">:</span> <span class="kc">False</span>
 <span class="p">}</span>
 </pre></div>
 
@@ -300,36 +304,36 @@
 <p>因为该接口会对<code>op</code>和<code>Variable</code>做相应的删除和修改，所以此接口只能在训练完成之后调用。如果想转化训练的中间模型，可加载相应的参数之后再使用此接口。</p>
 <p><strong>代码示例</strong></p>
 <div class="codehilite"><pre><span></span><span class="c1">#encoding=utf8</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">import</span> <span class="nn">paddleslim.quant</span> <span class="kn">as</span> <span class="nn">quant</span>
+<span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
+<span class="kn">import</span> <span class="nn">paddleslim.quant</span> <span class="k">as</span> <span class="nn">quant</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="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">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;x&#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="mi">28</span><span class="p">,</span> <span class="mi">28</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="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;x&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</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="kc">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="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="n">image</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
     <span class="n">feat</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">conv</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</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">feat</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">x</span><span class="o">=</span><span class="n">cost</span><span class="p">)</span>
 
-<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="n">place</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CUDAPlace</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">use_gpu</span> <span class="k">else</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">fluid</span><span class="o">.</span><span class="n">default_startup_program</span><span class="p">())</span>
-<span class="n">eval_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="n">eval_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="kc">True</span><span class="p">)</span>
 <span class="c1">#配置</span>
 <span class="n">config</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;weight_quantize_type&#39;</span><span class="p">:</span> <span class="s1">&#39;abs_max&#39;</span><span class="p">,</span>
         <span class="s1">&#39;activation_quantize_type&#39;</span><span class="p">:</span> <span class="s1">&#39;moving_average_abs_max&#39;</span><span class="p">}</span>
 <span class="n">build_strategy</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">BuildStrategy</span><span class="p">()</span>
 <span class="n">exec_strategy</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">ExecutionStrategy</span><span class="p">()</span>
 <span class="c1">#调用api</span>
-<span class="hll"><span class="n">quant_train_program</span> <span class="o">=</span> <span class="n">quant</span><span class="o">.</span><span class="n">quant_aware</span><span class="p">(</span><span class="n">train_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
-</span><span class="hll"><span class="n">quant_eval_program</span> <span class="o">=</span> <span class="n">quant</span><span class="o">.</span><span class="n">quant_aware</span><span class="p">(</span><span class="n">eval_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">config</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="hll"><span class="n">quant_train_program</span> <span class="o">=</span> <span class="n">quant</span><span class="o">.</span><span class="n">quant_aware</span><span class="p">(</span><span class="n">train_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</span><span class="hll"><span class="n">quant_eval_program</span> <span class="o">=</span> <span class="n">quant</span><span class="o">.</span><span class="n">quant_aware</span><span class="p">(</span><span class="n">eval_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">config</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span class="c1">#关闭策略</span>
-<span class="n">build_strategy</span><span class="o">.</span><span class="n">fuse_all_reduce_ops</span> <span class="o">=</span> <span class="bp">False</span>
-<span class="n">build_strategy</span><span class="o">.</span><span class="n">sync_batch_norm</span> <span class="o">=</span> <span class="bp">False</span>
+<span class="n">build_strategy</span><span class="o">.</span><span class="n">fuse_all_reduce_ops</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">build_strategy</span><span class="o">.</span><span class="n">sync_batch_norm</span> <span class="o">=</span> <span class="kc">False</span>
 <span class="n">quant_train_program</span> <span class="o">=</span> <span class="n">quant_train_program</span><span class="o">.</span><span class="n">with_data_parallel</span><span class="p">(</span>
     <span class="n">loss_name</span><span class="o">=</span><span class="n">avg_cost</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
     <span class="n">build_strategy</span><span class="o">=</span><span class="n">build_strategy</span><span class="p">,</span>
@@ -376,11 +380,11 @@
 <blockquote>
 <p>注： 此示例不能直接运行，因为需要加载<code>${model_dir}</code>下的模型，所以不能直接运行。</p>
 </blockquote>
-<p><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>
-<span class="kn">import</span> <span class="nn">paddle.dataset.mnist</span> <span class="kn">as</span> <span class="nn">reader</span>
+<p><div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
+<span class="kn">import</span> <span class="nn">paddle.dataset.mnist</span> <span class="k">as</span> <span class="nn">reader</span>
 <span class="kn">from</span> <span class="nn">paddleslim.quant</span> <span class="kn">import</span> <span class="n">quant_post</span>
 <span class="n">val_reader</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="n">place</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CUDAPlace</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">use_gpu</span> <span class="k">else</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>
@@ -419,22 +423,22 @@
 <p><strong>返回类型</strong></p>
 <p><code>fluid.Program</code></p>
 <p><strong>代码示例</strong>
-<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>
-<span class="kn">import</span> <span class="nn">paddleslim.quant</span> <span class="kn">as</span> <span class="nn">quant</span>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
+<span class="kn">import</span> <span class="nn">paddleslim.quant</span> <span class="k">as</span> <span class="nn">quant</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="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">input_word</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="s2">&quot;input_word&quot;</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="n">input_word</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="s2">&quot;input_word&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">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="n">input_emb</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span>
         <span class="nb">input</span><span class="o">=</span><span class="n">input_word</span><span class="p">,</span>
-        <span class="n">is_sparse</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+        <span class="n">is_sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
         <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">128</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">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;emb&#39;</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">Uniform</span><span class="p">(</span><span class="o">-</span><span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">)))</span>
 
-<span class="n">infer_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="n">infer_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="kc">True</span><span class="p">)</span>
 
-<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="kc">True</span>
 <span class="n">place</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CUDAPlace</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">if</span> <span class="n">use_gpu</span> <span class="k">else</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">fluid</span><span class="o">.</span><span class="n">default_startup_program</span><span class="p">())</span>

index.html

@@ -83,6 +83,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -290,5 +294,5 @@ python setup.py install
 
 <!--
 MkDocs version : 1.0.4
-Build Date UTC : 2020-01-22 09:09:49
+Build Date UTC : 2020-02-03 06:29:19
 -->

model_zoo/index.html

@@ -105,6 +105,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -953,7 +957,7 @@
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="../tutorials/quant_post_demo/" class="btn btn-neutral float-right" title="离线量化">Next <span class="icon icon-circle-arrow-right"></span></a>
+        <a href="../tutorials/pruning_tutorial/" class="btn btn-neutral float-right" title="图像分类模型通道剪裁-快速开始">Next <span class="icon icon-circle-arrow-right"></span></a>
       
       
         <a href=".." class="btn btn-neutral" title="Home"><span class="icon icon-circle-arrow-left"></span> Previous</a>
@@ -987,7 +991,7 @@
         <span><a href=".." style="color: #fcfcfc;">&laquo; Previous</a></span>
       
       
-        <span style="margin-left: 15px"><a href="../tutorials/quant_post_demo/" style="color: #fcfcfc">Next &raquo;</a></span>
+        <span style="margin-left: 15px"><a href="../tutorials/pruning_tutorial/" style="color: #fcfcfc">Next &raquo;</a></span>
       
     </span>
 </div>

search.html

@@ -58,6 +58,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="./tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="./tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

search_space/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -247,10 +251,10 @@
 <div class="codehilite"><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="kn">as</span> <span class="nn">np</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.register</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>
@@ -267,8 +271,8 @@
         <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="bp">None</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">tokens</span> <span class="o">==</span> <span class="bp">None</span><span class="p">:</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>
@@ -281,28 +285,28 @@
         <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{}&#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="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="bp">None</span><span class="p">):</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="bp">None</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="bp">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="bp">None</span><span class="p">):</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>

sitemap.xml

@@ -2,77 +2,82 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
     <url>
      <loc>None</loc>
-     <lastmod>2020-01-22</lastmod>
+     <lastmod>2020-02-03</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2020-02-03</lastmod>
      <changefreq>daily</changefreq>
     </url>
 </urlset>
\ No newline at end of file

table_latency/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../tutorials/pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../tutorials/quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

tutorials/demo_guide/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

tutorials/distillation_demo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -199,8 +203,8 @@
 <span class="n">student_startup</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="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">student_program</span><span class="p">,</span> <span class="n">student_startup</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="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</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="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="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</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="kc">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="c1"># student model definition</span>
     <span class="n">model</span> <span class="o">=</span> <span class="n">MobileNet</span><span class="p">()</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">image</span><span class="p">,</span> <span class="n">class_dim</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
@@ -216,7 +220,7 @@
 <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">teacher_program</span><span class="p">,</span> <span class="n">teacher_startup</span><span class="p">):</span>
     <span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">unique_name</span><span class="o">.</span><span class="n">guard</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;data&#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="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</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">name</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</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="c1"># teacher model definition</span>
         <span class="n">teacher_model</span> <span class="o">=</span> <span class="n">ResNet</span><span class="p">()</span>
         <span class="n">predict</span> <span class="o">=</span> <span class="n">teacher_model</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">class_dim</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
@@ -240,8 +244,8 @@
         <span class="n">student_vars</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
     <span class="k">except</span><span class="p">:</span>
         <span class="k">pass</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;student_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">student_vars</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;student_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">student_vars</span><span class="p">)</span>
 <span class="c1"># get all teacher variables</span>
 <span class="n">teacher_vars</span> <span class="o">=</span> <span class="p">[]</span>
 <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">teacher_program</span><span class="o">.</span><span class="n">list_vars</span><span class="p">():</span>
@@ -249,8 +253,8 @@
         <span class="n">teacher_vars</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
     <span class="k">except</span><span class="p">:</span>
         <span class="k">pass</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;teacher_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">teacher_vars</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="o">+</span><span class="s2">&quot;teacher_model_vars&quot;</span><span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">50</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">teacher_vars</span><span class="p">)</span>
 </pre></div>
 
 <h3 id="4-programmerge">4. 合并Program（merge）<a class="headerlink" href="#4-programmerge" title="Permanent link">#</a></h3>

tutorials/nas_demo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">
@@ -195,7 +199,7 @@
     <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">300</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="n">is_server</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
 <h3 id="3-nas">3. 根据实例化的NAS得到当前的网络结构<a class="headerlink" href="#3-nas" title="Permanent link">#</a></h3>
@@ -203,24 +207,24 @@
 </pre></div>
 
 <h3 id="4-program">4. 根据得到的网络结构和输入构造训练和测试program<a class="headerlink" href="#4-program" title="Permanent link">#</a></h3>
-<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>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</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="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">data</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;data&#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="n">data</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;data&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">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="kc">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">data</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
     <span class="n">output</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">data</span><span class="p">,</span> <span class="mi">10</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">output</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">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">output</span><span class="p">,</span> <span class="n">use_cudnn</span><span class="o">=</span><span class="kc">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="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="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="kc">True</span><span class="p">)</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>
 </pre></div>

tutorials/pruning_demo/index.html → tutorials/pruning_tutorial/index.html

@@ -8,7 +8,7 @@
   
   
   <link rel="shortcut icon" href="../../img/favicon.ico">
-  <title>卷积通道剪裁示例 - PaddleSlim Docs</title>
+  <title>图像分类模型通道剪裁-快速开始 - PaddleSlim Docs</title>
   <link href='https://fonts.googleapis.com/css?family=Lato:400,700|Roboto+Slab:400,700|Inconsolata:400,700' rel='stylesheet' type='text/css'>
 
   <link rel="stylesheet" href="../../css/theme.css" type="text/css" />
@@ -18,8 +18,8 @@
   
   <script>
     // Current page data
-    var mkdocs_page_name = "\u5377\u79ef\u901a\u9053\u526a\u88c1\u793a\u4f8b";
-    var mkdocs_page_input_path = "tutorials/pruning_demo.md";
+    var mkdocs_page_name = "\u56fe\u50cf\u5206\u7c7b\u6a21\u578b\u901a\u9053\u526a\u88c1-\u5feb\u901f\u5f00\u59cb";
+    var mkdocs_page_input_path = "tutorials/pruning_tutorial.md";
     var mkdocs_page_url = null;
   </script>
   
@@ -63,6 +63,28 @@
 		
     <span class="caption-text">教程</span>
     <ul class="subnav">
+                <li class=" current">
+                    
+    <a class="current" href="./">图像分类模型通道剪裁-快速开始</a>
+    <ul class="subnav">
+            
+    <li class="toctree-l3"><a href="#-">图像分类模型通道剪裁-快速开始</a></li>
+    
+        <ul>
+        
+            <li><a class="toctree-l4" href="#1">1. 导入依赖</a></li>
+        
+            <li><a class="toctree-l4" href="#2">2. 构建网络</a></li>
+        
+            <li><a class="toctree-l4" href="#3">3. 剪裁卷积层通道</a></li>
+        
+            <li><a class="toctree-l4" href="#4">4. 训练剪裁后的模型</a></li>
+        
+        </ul>
+    
+
+    </ul>
+                </li>
                 <li class="">
                     
     <a class="" href="../quant_post_demo/">离线量化</a>
@@ -147,11 +169,15 @@
     <li><a href="../..">Docs</a> &raquo;</li>
     
       
+        
+          <li>教程 &raquo;</li>
+        
+      
     
-    <li>卷积通道剪裁示例</li>
+    <li>图像分类模型通道剪裁-快速开始</li>
     <li class="wy-breadcrumbs-aside">
       
-        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/tutorials/pruning_demo.md"
+        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/tutorials/pruning_tutorial.md"
           class="icon icon-github"> Edit on GitHub</a>
       
     </li>
@@ -161,40 +187,87 @@
           <div role="main">
             <div class="section">
               
-                <h1 id="_1">卷积通道剪裁示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
-<p>本示例将演示如何按指定的剪裁率对每个卷积层的通道数进行剪裁。该示例默认会自动下载并使用mnist数据。</p>
-<p>当前示例支持以下分类模型：</p>
-<ul>
-<li>MobileNetV1</li>
-<li>MobileNetV2</li>
-<li>ResNet50</li>
-<li>PVANet</li>
-</ul>
-<h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
-<p>该示例使用了<code>paddleslim.Pruner</code>工具类，用户接口使用介绍请参考：<a href="https://paddlepaddle.github.io/PaddleSlim/api/prune_api/">API文档</a></p>
-<h2 id="_3">确定待裁参数<a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
-<p>不同模型的参数命名不同，在剪裁前需要确定待裁卷积层的参数名称。可通过以下方法列出所有参数名：</p>
-<div class="codehilite"><pre><span></span><span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">program</span><span class="o">.</span><span class="n">global_block</span><span class="p">()</span><span class="o">.</span><span class="n">all_parameters</span><span class="p">():</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;param name: {}; shape: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+                <h1 id="-">图像分类模型通道剪裁-快速开始<a class="headerlink" href="#-" title="Permanent link">#</a></h1>
+<p>该教程以图像分类模型MobileNetV1为例，说明如何快速使用<a href="">PaddleSlim的卷积通道剪裁接口</a>。
+该示例包含以下步骤：</p>
+<ol>
+<li>导入依赖</li>
+<li>构建模型</li>
+<li>剪裁</li>
+<li>训练剪裁后的模型</li>
+</ol>
+<p>以下章节依次次介绍每个步骤的内容。</p>
+<h2 id="1">1. 导入依赖<a class="headerlink" href="#1" title="Permanent link">#</a></h2>
+<p>PaddleSlim依赖Paddle1.7版本，请确认已正确安装Paddle，然后按以下方式导入Paddle和PaddleSlim:</p>
+<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="kn">import</span> <span class="nn">paddleslim</span> <span class="kn">as</span> <span class="nn">slim</span>
 </pre></div>
 
-<p>在<code>train.py</code>脚本中，提供了<code>get_pruned_params</code>方法，根据用户设置的选项<code>--model</code>确定要裁剪的参数。</p>
-<h2 id="_4">启动裁剪任务<a class="headerlink" href="#_4" title="Permanent link">#</a></h2>
-<p>通过以下命令启动裁剪任务：</p>
-<div class="codehilite"><pre><span></span><span class="n">export</span> <span class="n">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="mi">0</span>
-<span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span>
+<h2 id="2">2. 构建网络<a class="headerlink" href="#2" title="Permanent link">#</a></h2>
+<p>该章节构造一个用于对MNIST数据进行分类的分类模型，选用<code>MobileNetV1</code>，并将输入大小设置为<code>[1, 28, 28]</code>，输出类别数为10。
+为了方便展示示例，我们在<code>paddleslim.models</code>下预定义了用于构建分类模型的方法，执行以下代码构建分类模型：</p>
+<div class="codehilite"><pre><span></span><span class="err">exe, train_program, val_program, inputs, outputs =</span>
+<span class="err">    slim.models.image_classification(&quot;MobileNet&quot;, [1, 28, 28], 10, use_gpu=False)</span>
 </pre></div>
 
-<p>执行<code>python train.py --help</code>查看更多选项。</p>
-<h2 id="_5">注意<a class="headerlink" href="#_5" title="Permanent link">#</a></h2>
-<ol>
-<li>在接口<code>paddle.Pruner.prune</code>的参数中，<code>params</code>和<code>ratios</code>的长度需要一样。</li>
-</ol>
+<blockquote>
+<p>注意：paddleslim.models下的API并非PaddleSlim常规API，是为了简化示例而封装预定义的一系列方法，比如：模型结构的定义、Program的构建等。</p>
+</blockquote>
+<h2 id="3">3. 剪裁卷积层通道<a class="headerlink" href="#3" title="Permanent link">#</a></h2>
+<h3 id="31-flops">3.1 计算剪裁之前的FLOPs<a class="headerlink" href="#31-flops" title="Permanent link">#</a></h3>
+<div class="codehilite"><pre><span></span><span class="err">FLOPs = slim.analysis.flops(train_program)</span>
+<span class="err">print(&quot;FLOPs: {}&quot;.format(FLOPs))</span>
+</pre></div>
+
+<h3 id="32">3.2 剪裁<a class="headerlink" href="#32" title="Permanent link">#</a></h3>
+<p>我们这里对参数名为<code>conv2_1_sep_weights</code>和<code>conv2_2_sep_weights</code>的卷积层进行剪裁，分别剪掉20%和30%的通道数。
+代码如下所示：</p>
+<div class="codehilite"><pre><span></span><span class="err">pruner = slim.prune.Pruner()</span>
+<span class="err">pruned_program, _, _ = pruner.prune(</span>
+<span class="err">        train_program,</span>
+<span class="err">        fluid.global_scope(),</span>
+<span class="err">        params=[&quot;conv2_1_sep_weights&quot;, &quot;conv2_2_sep_weights&quot;],</span>
+<span class="err">        ratios=[0.33] * 2,</span>
+<span class="err">        place=fluid.CPUPlace())</span>
+</pre></div>
+
+<p>以上操作会修改<code>train_program</code>中对应卷积层参数的定义，同时对<code>fluid.global_scope()</code>中存储的参数数组进行裁剪。</p>
+<h3 id="33-flops">3.3 计算剪裁之后的FLOPs<a class="headerlink" href="#33-flops" title="Permanent link">#</a></h3>
+<div class="codehilite"><pre><span></span><span class="err">FLOPs = paddleslim.analysis.flops(train_program)</span>
+<span class="err">print(&quot;FLOPs: {}&quot;.format(FLOPs))</span>
+</pre></div>
+
+<h2 id="4">4. 训练剪裁后的模型<a class="headerlink" href="#4" title="Permanent link">#</a></h2>
+<h3 id="41">4.1 定义输入数据<a class="headerlink" href="#41" title="Permanent link">#</a></h3>
+<p>为了快速执行该示例，我们选取简单的MNIST数据，Paddle框架的<code>paddle.dataset.mnist</code>包定义了MNIST数据的下载和读取。
+代码如下：</p>
+<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.dataset.mnist</span> <span class="kn">as</span> <span class="nn">reader</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">reader</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</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="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">inputs</span><span class="p">,</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CPUPlace</span><span class="p">())</span>
+</pre></div>
+
+<h3 id="42">4.2 执行训练<a class="headerlink" href="#42" title="Permanent link">#</a></h3>
+<p>以下代码执行了一个<code>epoch</code>的训练：</p>
+<div class="codehilite"><pre><span></span><span class="err">for data in train_reader():</span>
+<span class="err">    acc1, acc5, loss = exe.run(pruned_program, feed=train_feeder.feed(data), fetch_list=outputs)</span>
+<span class="err">    print(acc1, acc5, loss)</span>
+</pre></div>
               
             </div>
           </div>
           <footer>
   
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
+        <a href="../quant_post_demo/" class="btn btn-neutral float-right" title="离线量化">Next <span class="icon icon-circle-arrow-right"></span></a>
+      
+      
+        <a href="../../model_zoo/" class="btn btn-neutral" title="模型库"><span class="icon icon-circle-arrow-left"></span> Previous</a>
+      
+    </div>
+  
 
   <hr/>
 
@@ -219,6 +292,10 @@
           <a href="https://github.com/PaddlePaddle/PaddleSlim/" class="fa fa-github" style="float: left; color: #fcfcfc"> GitHub</a>
       
       
+        <span><a href="../../model_zoo/" style="color: #fcfcfc;">&laquo; Previous</a></span>
+      
+      
+        <span style="margin-left: 15px"><a href="../quant_post_demo/" style="color: #fcfcfc">Next &raquo;</a></span>
       
     </span>
 </div>

tutorials/quant_aware_demo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class=" current">
@@ -195,20 +199,20 @@
     <span class="s1">&#39;dtype&#39;</span><span class="p">:</span> <span class="s1">&#39;int8&#39;</span><span class="p">,</span>
     <span class="s1">&#39;window_size&#39;</span><span class="p">:</span> <span class="mi">10000</span><span class="p">,</span>
     <span class="s1">&#39;moving_rate&#39;</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">,</span>
-    <span class="s1">&#39;quant_weight_only&#39;</span><span class="p">:</span> <span class="bp">False</span>
+    <span class="s1">&#39;quant_weight_only&#39;</span><span class="p">:</span> <span class="kc">False</span>
 <span class="p">}</span>
 </pre></div>
 
 <h3 id="2-programop">2. 对训练和测试program插入可训练量化op<a class="headerlink" href="#2-programop" title="Permanent link">#</a></h3>
-<div class="codehilite"><pre><span></span><span class="n">val_program</span> <span class="o">=</span> <span class="n">quant_aware</span><span class="p">(</span><span class="n">val_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">quant_config</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<div class="codehilite"><pre><span></span><span class="n">val_program</span> <span class="o">=</span> <span class="n">quant_aware</span><span class="p">(</span><span class="n">val_program</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">quant_config</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">compiled_train_prog</span> <span class="o">=</span> <span class="n">quant_aware</span><span class="p">(</span><span class="n">train_prog</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">quant_config</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">compiled_train_prog</span> <span class="o">=</span> <span class="n">quant_aware</span><span class="p">(</span><span class="n">train_prog</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">quant_config</span><span class="p">,</span> <span class="n">scope</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 
 <h3 id="3build">3.关掉指定build策略<a class="headerlink" href="#3build" title="Permanent link">#</a></h3>
 <div class="codehilite"><pre><span></span><span class="n">build_strategy</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">BuildStrategy</span><span class="p">()</span>
-<span class="n">build_strategy</span><span class="o">.</span><span class="n">fuse_all_reduce_ops</span> <span class="o">=</span> <span class="bp">False</span>
-<span class="n">build_strategy</span><span class="o">.</span><span class="n">sync_batch_norm</span> <span class="o">=</span> <span class="bp">False</span>
+<span class="n">build_strategy</span><span class="o">.</span><span class="n">fuse_all_reduce_ops</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">build_strategy</span><span class="o">.</span><span class="n">sync_batch_norm</span> <span class="o">=</span> <span class="kc">False</span>
 <span class="n">exec_strategy</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">ExecutionStrategy</span><span class="p">()</span>
 <span class="n">compiled_train_prog</span> <span class="o">=</span> <span class="n">compiled_train_prog</span><span class="o">.</span><span class="n">with_data_parallel</span><span class="p">(</span>
         <span class="n">loss_name</span><span class="o">=</span><span class="n">avg_cost</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
@@ -220,8 +224,8 @@
 <div class="codehilite"><pre><span></span><span class="n">float_program</span><span class="p">,</span> <span class="n">int8_program</span> <span class="o">=</span> <span class="n">convert</span><span class="p">(</span><span class="n">val_program</span><span class="p">,</span> 
                                       <span class="n">place</span><span class="p">,</span>
                                       <span class="n">quant_config</span><span class="p">,</span>
-                                      <span class="n">scope</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
-                                      <span class="n">save_int8</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+                                      <span class="n">scope</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                      <span class="n">save_int8</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 
 <h3 id="5">5.保存预测模型<a class="headerlink" href="#5" title="Permanent link">#</a></h3>

tutorials/quant_embedding_demo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">

tutorials/quant_post_demo/index.html

@@ -63,6 +63,10 @@
 		
     <span class="caption-text">教程</span>
     <ul class="subnav">
+                <li class="">
+                    
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
                 <li class=" current">
                     
     <a class="current" href="./">离线量化</a>
@@ -239,7 +243,7 @@
         <a href="../quant_aware_demo/" class="btn btn-neutral float-right" title="量化训练">Next <span class="icon icon-circle-arrow-right"></span></a>
       
       
-        <a href="../../model_zoo/" class="btn btn-neutral" title="模型库"><span class="icon icon-circle-arrow-left"></span> Previous</a>
+        <a href="../pruning_tutorial/" class="btn btn-neutral" title="图像分类模型通道剪裁-快速开始"><span class="icon icon-circle-arrow-left"></span> Previous</a>
       
     </div>
   
@@ -267,7 +271,7 @@
           <a href="https://github.com/PaddlePaddle/PaddleSlim/" class="fa fa-github" style="float: left; color: #fcfcfc"> GitHub</a>
       
       
-        <span><a href="../../model_zoo/" style="color: #fcfcfc;">&laquo; Previous</a></span>
+        <span><a href="../pruning_tutorial/" style="color: #fcfcfc;">&laquo; Previous</a></span>
       
       
         <span style="margin-left: 15px"><a href="../quant_aware_demo/" style="color: #fcfcfc">Next &raquo;</a></span>

tutorials/sensitivity_demo/index.html

@@ -65,6 +65,10 @@
     <ul class="subnav">
                 <li class="">
                     
+    <a class="" href="../pruning_tutorial/">图像分类模型通道剪裁-快速开始</a>
+                </li>
+                <li class="">
+                    
     <a class="" href="../quant_post_demo/">离线量化</a>
                 </li>
                 <li class="">