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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>
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<div class="highlight"><pre><span></span>for param in program.global_block().all_parameters():
    print(&quot;param name: {}; shape: {}&quot;.format(param.name, param.shape))
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</pre></div>

<p>在<code>train.py</code>脚本中，提供了<code>get_pruned_params</code>方法，根据用户设置的选项<code>--model</code>确定要裁剪的参数。</p>
<h2 id="_4">启动裁剪任务<a class="headerlink" href="#_4" title="Permanent link">#</a></h2>
<p>通过以下命令启动裁剪任务：</p>
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<div class="highlight"><pre><span></span>export CUDA_VISIBLE_DEVICES=0
python train.py
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</pre></div>

<p>执行<code>python train.py --help</code>查看更多选项。</p>
<h2 id="_5">注意<a class="headerlink" href="#_5" title="Permanent link">#</a></h2>
<ol>
<li>在接口<code>paddle.Pruner.prune</code>的参数中，<code>params</code>和<code>ratios</code>的长度需要一样。</li>
</ol>
              
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