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        <a href="." class="icon icon-home"> PaddleSlim Docs</a>
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    <li class="toctree-l2"><a href="#paddleslim">PaddleSlim</a></li>
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            <li><a class="toctree-l3" href="#_1">功能</a></li>
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            <li><a class="toctree-l3" href="#_2">安装</a></li>
        
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    <a class="" href="tutorials/quant_post_demo/">离线量化</a>
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        <a href="https://github.com/PaddlePaddle/PaddleSlim/edit/master/docs/index.md"
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                <h1 id="paddleslim">PaddleSlim<a class="headerlink" href="#paddleslim" title="Permanent link">#</a></h1>
<p>PaddleSlim是PaddlePaddle框架的一个子模块，主要用于压缩图像领域模型。在PaddleSlim中，不仅实现了目前主流的网络剪枝、量化、蒸馏三种压缩策略，还实现了超参数搜索和小模型网络结构搜索功能。在后续版本中，会添加更多的压缩策略，以及完善对NLP领域模型的支持。</p>
<h2 id="_1">功能<a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
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<li>模型剪裁</li>
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<li>支持通道均匀模型剪裁（uniform pruning)</li>
<li>基于敏感度的模型剪裁</li>
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<li>
<p>基于进化算法的自动模型剪裁三种方式</p>
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<p>量化训练</p>
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<li>在线量化训练（training aware）</li>
<li>离线量化（post training）</li>
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<p>支持对权重全局量化和Channel-Wise量化</p>
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<p>蒸馏</p>
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</li>
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<p>轻量神经网络结构自动搜索（Light-NAS）</p>
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<li>支持基于进化算法的轻量神经网络结构自动搜索（Light-NAS）</li>
<li>支持 FLOPS / 硬件延时约束</li>
<li>支持多平台模型延时评估</li>
</ul>
<h2 id="_2">安装<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
<p>安装PaddleSlim前，请确认已正确安装Paddle1.6版本或更新版本。Paddle安装请参考：<a href="https://www.paddlepaddle.org.cn/install/quick">Paddle安装教程</a>。</p>
<ul>
<li>安装develop版本</li>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="p">.</span><span class="n">com</span><span class="o">/</span><span class="n">PaddlePaddle</span><span class="o">/</span><span class="n">PaddleSlim</span><span class="p">.</span><span class="n">git</span>
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<span class="n">cd</span> <span class="n">PaddleSlim</span>
<span class="n">python</span> <span class="n">setup</span><span class="p">.</span><span class="n">py</span> <span class="n">install</span>
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<ul>
<li>安装官方发布的最新版本</li>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">paddleslim</span> <span class="o">-</span><span class="n">i</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">pypi</span><span class="p">.</span><span class="n">org</span><span class="o">/</span><span class="k">simple</span>
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<ul>
<li>安装历史版本</li>
</ul>
<p>请点击<a href="https://pypi.org/project/paddleslim/#history">pypi.org</a>查看可安装历史版本。</p>
<h2 id="_3">使用<a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
<ul>
<li><a href="doc/api_guide.md">API文档</a>：API使用介绍，包括<a href="">蒸馏</a>、<a href="">剪裁</a>、<a href="">量化</a>和<a href="">模型结构搜索</a>。</li>
<li><a href="doc/demo_guide.md">示例</a>：基于mnist和cifar10等简单分类任务的模型压缩示例，您可以通过该部分快速体验和了解PaddleSlim的功能。</li>
<li><a href="">实践教程</a>：经典模型的分析和压缩实验教程。</li>
<li><a href="">模型库</a>：经过压缩的分类、检测、语义分割模型，包括权重文件、网络结构文件和性能数据。</li>
<li><a href="">Paddle检测库</a>：介绍如何在检测库中使用PaddleSlim。</li>
<li><a href="">Paddle分割库</a>：介绍如何在分割库中使用PaddleSlim。</li>
<li><a href="">PaddleLite</a>：介绍如何使用预测库PaddleLite部署PaddleSlim产出的模型。</li>
</ul>
<h2 id="_4">贡献与反馈<a class="headerlink" href="#_4" title="Permanent link">#</a></h2>
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