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api/analysis_api/index.html

@@ -206,7 +206,7 @@
 </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="k">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="kn">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 +216,7 @@
                   <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="kc">None</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">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 +224,9 @@
         <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="kc">None</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">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="kc">False</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">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 +248,7 @@
 <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="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="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="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 +258,7 @@
     <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="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>
+<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>
 </pre></div>
 
 <h2 id="model_size">model_size<a class="headerlink" href="#model_size" title="Permanent link">#</a></h2>
@@ -273,7 +273,7 @@
 <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="k">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="kn">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 +283,7 @@
                   <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="kc">None</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">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 +291,9 @@
         <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="kc">None</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">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="kc">False</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">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 +307,7 @@
 <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="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="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="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 +317,7 @@
     <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="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>
+<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>
 </pre></div>
 
 <h2 id="tablelatencyevaluator">TableLatencyEvaluator<a class="headerlink" href="#tablelatencyevaluator" title="Permanent link">#</a></h2>

api/nas_api/index.html

@@ -236,16 +236,16 @@
 <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="k">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="kn">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
 <span class="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="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="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
 <span class="n">archs</span> <span class="o">=</span> <span class="n">sanas</span><span class="o">.</span><span class="n">next_archs</span><span class="p">()</span>
 <span class="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
     <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
     <span class="nb">input</span> <span class="o">=</span> <span class="n">output</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+<span class="k">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 +258,7 @@
 <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="k">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="kn">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
 <span class="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 +279,14 @@
 <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="k">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="kn">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
 <span class="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="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="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
 <span class="n">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="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>
+<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>
 </pre></div></p>
 <dl>
 <dt>paddleslim.nas.SANAS.current_info()</dt>
@@ -295,11 +295,11 @@
 <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="k">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="kn">as</span> <span class="nn">fluid</span>
 <span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
 <span class="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">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="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>
 </pre></div></p>
               
             </div>

api/quantization_api/index.html

@@ -219,9 +219,9 @@
     <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="kc">False</span><span class="p">,</span>
+    <span class="s1">&#39;for_tensorrt&#39;</span><span class="p">:</span> <span class="bp">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="kc">False</span>
+    <span class="s1">&#39;is_full_quantize&#39;</span><span class="p">:</span> <span class="bp">False</span>
 <span class="p">}</span>
 </pre></div>
 
@@ -300,36 +300,36 @@
 <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="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="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="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="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">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">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="kc">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">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="kc">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="bp">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="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 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><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="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">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">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 +376,11 @@
 <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="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>
+<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>
 <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="kc">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">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 +419,22 @@
 <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="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>
+<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>
 
 <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="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_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_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="kc">False</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">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="kc">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="bp">True</span><span class="p">)</span>
 
-<span class="n">use_gpu</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">use_gpu</span> <span class="o">=</span> <span class="bp">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>

api/single_distiller_api/index.html

@@ -205,8 +205,8 @@
 <p><em>data_name_map</em> 是 <strong>teacher_var name到student_var name的映射</strong>，如果写反可能无法正确进行merge</p>
 </div>
 <p><strong>使用示例：</strong></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">paddleslim.dist</span> <span class="k">as</span> <span class="nn">dist</span>
+<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.dist</span> <span class="kn">as</span> <span class="nn">dist</span>
 <span class="n">student_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">student_program</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">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="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>
@@ -219,7 +219,7 @@
     <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">conv</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;y&#39;</span><span class="p">:</span><span class="s1">&#39;x&#39;</span><span class="p">}</span>
-<span class="n">USE_GPU</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">USE_GPU</span> <span class="o">=</span> <span class="bp">False</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="hll"><span class="n">dist</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span>
 </span><span class="hll">                          <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</span><span class="p">)</span>
@@ -242,8 +242,8 @@
 </ul>
 <p><strong>返回：</strong> 由teacher_var1, teacher_var2, student_var1, student_var2组合得到的fsp_loss</p>
 <p><strong>使用示例：</strong></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">paddleslim.dist</span> <span class="k">as</span> <span class="nn">dist</span>
+<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.dist</span> <span class="kn">as</span> <span class="nn">dist</span>
 <span class="n">student_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">student_program</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">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="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>
@@ -256,7 +256,7 @@
     <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">conv</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</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="s1">&#39;t2&#39;</span><span class="p">)</span>
 <span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;y&#39;</span><span class="p">:</span><span class="s1">&#39;x&#39;</span><span class="p">}</span>
-<span class="n">USE_GPU</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">USE_GPU</span> <span class="o">=</span> <span class="bp">False</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">dist</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span> <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</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>
@@ -279,8 +279,8 @@
 </ul>
 <p><strong>返回：</strong> 由teacher_var, student_var组合得到的l2_loss</p>
 <p><strong>使用示例：</strong></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">paddleslim.dist</span> <span class="k">as</span> <span class="nn">dist</span>
+<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.dist</span> <span class="kn">as</span> <span class="nn">dist</span>
 <span class="n">student_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">student_program</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">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="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>
@@ -293,7 +293,7 @@
     <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">conv</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</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="s1">&#39;t2&#39;</span><span class="p">)</span>
 <span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;y&#39;</span><span class="p">:</span><span class="s1">&#39;x&#39;</span><span class="p">}</span>
-<span class="n">USE_GPU</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">USE_GPU</span> <span class="o">=</span> <span class="bp">False</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">dist</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span> <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</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>
@@ -318,8 +318,8 @@
 </ul>
 <p><strong>返回：</strong> 由teacher_var, student_var组合得到的soft_label_loss</p>
 <p><strong>使用示例：</strong></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">paddleslim.dist</span> <span class="k">as</span> <span class="nn">dist</span>
+<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.dist</span> <span class="kn">as</span> <span class="nn">dist</span>
 <span class="n">student_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">student_program</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">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="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>
@@ -332,7 +332,7 @@
     <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">conv</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</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="s1">&#39;t2&#39;</span><span class="p">)</span>
 <span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;y&#39;</span><span class="p">:</span><span class="s1">&#39;x&#39;</span><span class="p">}</span>
-<span class="n">USE_GPU</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">USE_GPU</span> <span class="o">=</span> <span class="bp">False</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">dist</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span> <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</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>
@@ -355,8 +355,8 @@
 </ul>
 <p><strong>返回</strong>：自定义的损失函数loss</p>
 <p><strong>使用示例：</strong></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">paddleslim.dist</span> <span class="k">as</span> <span class="nn">dist</span>
+<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.dist</span> <span class="kn">as</span> <span class="nn">dist</span>
 <span class="n">student_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">student_program</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">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="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>
@@ -369,7 +369,7 @@
     <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">conv</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">out</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">padding</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="s1">&#39;t2&#39;</span><span class="p">)</span>
 <span class="n">data_name_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;y&#39;</span><span class="p">:</span><span class="s1">&#39;x&#39;</span><span class="p">}</span>
-<span class="n">USE_GPU</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="n">USE_GPU</span> <span class="o">=</span> <span class="bp">False</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">dist</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">teacher_program</span><span class="p">,</span> <span class="n">student_program</span><span class="p">,</span> <span class="n">data_name_map</span><span class="p">,</span> <span class="n">place</span><span class="p">)</span>
 <span class="k">def</span> <span class="nf">adaptation_loss</span><span class="p">(</span><span class="n">t_var</span><span class="p">,</span> <span class="n">s_var</span><span class="p">):</span>

index.html

@@ -290,5 +290,5 @@ python setup.py install
 
 <!--
 MkDocs version : 1.0.4
-Build Date UTC : 2020-01-22 08:35:11
+Build Date UTC : 2020-01-22 09:09:49
 -->

search_space/index.html

@@ -247,10 +247,10 @@
 <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="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
 
 <span class="c1">### 需要调用注册函数把自定义搜索空间注册到space space中</span>
-<span class="nd">@SEARCHSPACE</span><span class="o">.</span><span class="n">register</span>
+<span class="nd">@SEARCHSPACE.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 +267,8 @@
         <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="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="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="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 +281,28 @@
         <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</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="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="k">return</span> <span class="nb">input</span>
 
         <span class="k">return</span> <span class="n">net_arch</span>
 
     <span class="c1">### 构造具体block的操作</span>
-    <span class="k">def</span> <span class="nf">_resnet_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="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="n">shortcut_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shortcut</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
         <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv0&#39;</span><span class="p">)</span>
         <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv1&#39;</span><span class="p">)</span>
         <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s1">&#39;_conv2&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">elementwise_add</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">shortcut_conv</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_elementwise_add&#39;</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">_shortcut</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">channel_num</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="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="n">channel_in</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
         <span class="k">if</span> <span class="n">channel_in</span> <span class="o">!=</span> <span class="n">channel_num</span> <span class="ow">or</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="o">=</span><span class="n">channel_num</span><span class="p">,</span> <span class="n">filter_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="o">+</span><span class="s1">&#39;_shortcut&#39;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="nb">input</span>
 
-    <span class="k">def</span> <span class="nf">_conv_bn_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">num_filters</span><span class="p">,</span> <span class="n">filter_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;SAME&#39;</span><span class="p">,</span> <span class="n">act</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="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="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>

tutorials/distillation_demo/index.html

@@ -199,8 +199,8 @@
 <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="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="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="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 +216,7 @@
 <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="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">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="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 +240,8 @@
         <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="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="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="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 +249,8 @@
         <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="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>
+<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>
 </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

@@ -180,11 +180,12 @@
                 <h1 id="_1">网络结构搜索示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
 <p>本示例介绍如何使用网络结构搜索接口，搜索到一个更小或者精度更高的模型，该文档仅介绍paddleslim中SANAS的使用及如何利用SANAS得到模型结构，完整示例代码请参考sa_nas_mobilenetv2.py或者block_sa_nas_mobilenetv2.py。</p>
 <h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
-<p>请参考<a href='../api/nas_api.md'>神经网络搜索API介绍</a>。</p>
+<p>请参考<a href="https://paddlepaddle.github.io/PaddleSlim/api/nas_api/">神经网络搜索API介绍</a>。</p>
 <h3 id="1">1. 配置搜索空间<a class="headerlink" href="#1" title="Permanent link">#</a></h3>
-<p>详细的搜索空间配置可以参考<a href='../search_space.md'>搜索空间</a>。
-<div class="codehilite"><pre><span></span><span class="err">config = [(&#39;MobileNetV2Space&#39;)]</span>
-</pre></div></p>
+<p>详细的搜索空间配置可以参考<a href="https://paddlepaddle.github.io/PaddleSlim/search_space/">搜索空间介绍</a>。</p>
+<div class="codehilite"><pre><span></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>
+</pre></div>
+
 <h3 id="2-sanas">2. 利用搜索空间初始化SANAS实例<a class="headerlink" href="#2-sanas" title="Permanent link">#</a></h3>
 <div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
 
@@ -194,7 +195,7 @@
     <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="kc">True</span><span class="p">)</span>
+    <span class="n">is_server</span><span class="o">=</span><span class="bp">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>
@@ -202,24 +203,24 @@
 </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="k">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="kn">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="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="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="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="kc">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="bp">False</span><span class="p">)</span>
     <span class="n">cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">cross_entropy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">softmax_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
     <span class="n">avg_cost</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">cost</span><span class="p">)</span>
     <span class="n">acc_top1</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">softmax_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
-    <span class="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">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">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

@@ -175,7 +175,7 @@
 <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="nb">print</span><span class="p">(</span><span class="s2">&quot;param name: </span><span class="si">{}</span><span class="s2">; shape: </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">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>
+    <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>
 </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>

tutorials/quant_aware_demo/index.html

@@ -195,20 +195,20 @@
     <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="kc">False</span>
+    <span class="s1">&#39;quant_weight_only&#39;</span><span class="p">:</span> <span class="bp">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="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>
+<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>
 
-<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>
+<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>
 </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="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">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">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 +220,8 @@
 <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="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>
+                                      <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>
 </pre></div>
 
 <h3 id="5">5.保存预测模型<a class="headerlink" href="#5" title="Permanent link">#</a></h3>