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...@@ -48,6 +48,11 @@ ...@@ -48,6 +48,11 @@
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...@@ -113,21 +118,6 @@ ...@@ -113,21 +118,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -169,21 +174,6 @@ ...@@ -169,21 +174,6 @@
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...@@ -491,8 +481,6 @@ e^{\frac{(r_k-r)}{T_k}} & r_k < r\\ ...@@ -491,8 +481,6 @@ e^{\frac{(r_k-r)}{T_k}} & r_k < r\\
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
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...@@ -525,8 +513,6 @@ e^{\frac{(r_k-r)}{T_k}} & r_k < r\\ ...@@ -525,8 +513,6 @@ e^{\frac{(r_k-r)}{T_k}} & r_k < r\\
<span><a href="../../tutorials/distillation_demo/" style="color: #fcfcfc;">&laquo; Previous</a></span> <span><a href="../../tutorials/distillation_demo/" style="color: #fcfcfc;">&laquo; Previous</a></span>
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -132,21 +137,6 @@ ...@@ -132,21 +137,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -120,21 +125,6 @@ ...@@ -120,21 +125,6 @@
<a class="" href="../../algo/algo/">算法原理</a> <a class="" href="../../algo/algo/">算法原理</a>
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
<li class="toctree-l1"> <li class="toctree-l1">
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...@@ -134,21 +139,6 @@ ...@@ -134,21 +139,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -138,21 +143,6 @@ ...@@ -138,21 +143,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -138,21 +143,6 @@ ...@@ -138,21 +143,6 @@
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...@@ -520,7 +510,7 @@ ...@@ -520,7 +510,7 @@
<a href="../prune_api/" class="btn btn-neutral float-right" title="剪枝与敏感度">Next <span class="icon icon-circle-arrow-right"></span></a> <a href="../prune_api/" class="btn btn-neutral float-right" title="剪枝与敏感度">Next <span class="icon icon-circle-arrow-right"></span></a>
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...@@ -548,7 +538,7 @@ ...@@ -548,7 +538,7 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -137,21 +142,6 @@ ...@@ -137,21 +142,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
<li class="toctree-l1"> <li class="toctree-l1">
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...@@ -138,21 +143,6 @@ ...@@ -138,21 +143,6 @@
<a class="" href="../../algo/algo/">算法原理</a> <a class="" href="../../algo/algo/">算法原理</a>
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...@@ -73,6 +73,11 @@ ...@@ -73,6 +73,11 @@
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...@@ -138,21 +143,6 @@ ...@@ -138,21 +143,6 @@
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...@@ -288,7 +278,7 @@ ...@@ -288,7 +278,7 @@
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...@@ -48,6 +48,11 @@ ...@@ -48,6 +48,11 @@
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...@@ -113,21 +118,6 @@ ...@@ -113,21 +118,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -138,21 +143,6 @@ ...@@ -138,21 +143,6 @@
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...@@ -55,6 +55,11 @@ ...@@ -55,6 +55,11 @@
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...@@ -132,21 +137,6 @@ ...@@ -132,21 +137,6 @@
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<h1 id="_1">卷积通道剪裁示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
<p>本示例将演示如何按指定的剪裁率对每个卷积层的通道数进行剪裁。该示例默认会自动下载并使用mnist数据。</p>
<p>当前示例支持以下分类模型:</p>
<ul>
<li>MobileNetV1</li>
<li>MobileNetV2</li>
<li>ResNet50</li>
<li>PVANet</li>
</ul>
<h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
<p>该示例使用了<code>paddleslim.Pruner</code>工具类,用户接口使用介绍请参考:<a href="https://paddlepaddle.github.io/PaddleSlim/api/prune_api/">API文档</a></p>
<h2 id="_3">确定待裁参数<a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
<p>不同模型的参数命名不同,在剪裁前需要确定待裁卷积层的参数名称。可通过以下方法列出所有参数名:</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">for</span> <span class="nv">param</span> <span class="nv">in</span> <span class="nv">program</span>.<span class="nv">global_block</span><span class="ss">()</span>.<span class="nv">all_parameters</span><span class="ss">()</span>:
<span class="nv">print</span><span class="ss">(</span><span class="s2">&quot;</span><span class="s">param name: {}; shape: {}</span><span class="s2">&quot;</span>.<span class="nv">format</span><span class="ss">(</span><span class="nv">param</span>.<span class="nv">name</span>, <span class="nv">param</span>.<span class="nv">shape</span><span class="ss">))</span>
</pre></div>
</td></tr></table>
<p><code>train.py</code>脚本中,提供了<code>get_pruned_params</code>方法,根据用户设置的选项<code>--model</code>确定要裁剪的参数。</p>
<h2 id="_4">启动裁剪任务<a class="headerlink" href="#_4" title="Permanent link">#</a></h2>
<p>通过以下命令启动裁剪任务:</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">export</span> <span class="n">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="mi">0</span>
<span class="n">python</span> <span class="n">train</span><span class="p">.</span><span class="n">py</span>
</pre></div>
</td></tr></table>
<p>执行<code>python train.py --help</code>查看更多选项。</p>
<h2 id="_5">注意<a class="headerlink" href="#_5" title="Permanent link">#</a></h2>
<ol>
<li>在接口<code>paddle.Pruner.prune</code>的参数中,<code>params</code><code>ratios</code>的长度需要一样。</li>
</ol>
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