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                <h1 id="_1">离线量化示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
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<p>本示例介绍如何使用离线量化接口<code>paddleslim.quant.quant_post</code>来对训练好的分类模型进行离线量化, 该接口无需对模型进行训练就可得到量化模型，减少模型的存储空间和显存占用。</p>
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<h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
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<p>请参考 <a href="https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/">量化API文档</a>。</p>
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<h2 id="_3">分类模型的离线量化流程<a class="headerlink" href="#_3" title="Permanent link">#</a></h2>
<h3 id="_4">准备数据<a class="headerlink" href="#_4" title="Permanent link">#</a></h3>
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<p>在当前文件夹下创建<code>data</code>文件夹，将<code>imagenet</code>数据集解压在<code>data</code>文件夹下，解压后<code>data</code>文件夹下应包含以下文件：
- <code>'train'</code>文件夹，训练图片
- <code>'train_list.txt'</code>文件
- <code>'val'</code>文件夹，验证图片
- <code>'val_list.txt'</code>文件</p>
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<h3 id="_5">准备需要量化的模型<a class="headerlink" href="#_5" title="Permanent link">#</a></h3>
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<p>因为离线量化接口只支持加载通过<code>fluid.io.save_inference_model</code>接口保存的模型，因此如果您的模型是通过其他接口保存的，那需要先将模型进行转化。本示例将以分类模型为例进行说明。</p>
<p>首先在<a href="https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD">imagenet分类模型</a>中下载训练好的<code>mobilenetv1</code>模型。</p>
<p>在当前文件夹下创建<code>'pretrain'</code>文件夹，将<code>mobilenetv1</code>模型在该文件夹下解压，解压后的目录为<code>pretrain/MobileNetV1_pretrained</code></p>
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<h3 id="_6">导出模型<a class="headerlink" href="#_6" title="Permanent link">#</a></h3>
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<p>通过运行以下命令可将模型转化为离线量化接口可用的模型：
<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">python</span> <span class="n">export_model</span><span class="p">.</span><span class="n">py</span> <span class="c1">--model &quot;MobileNet&quot; --pretrained_model ./pretrain/MobileNetV1_pretrained --data imagenet</span>
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转化之后的模型存储在<code>inference_model/MobileNet/</code>文件夹下，可看到该文件夹下有<code>'model'</code>, <code>'weights'</code>两个文件。</p>
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<h3 id="_7">离线量化<a class="headerlink" href="#_7" title="Permanent link">#</a></h3>
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<p>接下来对导出的模型文件进行离线量化，离线量化的脚本为<a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/quant/quant_post/quant_post.py">quant_post.py</a>，脚本中使用接口<code>paddleslim.quant.quant_post</code>对模型进行离线量化。运行命令为：
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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">python</span> <span class="n">quant_post</span><span class="p">.</span><span class="n">py</span> <span class="c1">--model_path ./inference_model/MobileNet --save_path ./quant_model_train/MobileNet --model_filename model --params_filename weights</span>
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<ul>
<li><code>model_path</code>: 需要量化的模型坐在的文件夹</li>
<li><code>save_path</code>: 量化后的模型保存的路径</li>
<li><code>model_filename</code>: 如果需要量化的模型的参数文件保存在一个文件中，则设置为该模型的模型文件名称，如果参数文件保存在多个文件中，则不需要设置。</li>
<li><code>params_filename</code>: 如果需要量化的模型的参数文件保存在一个文件中，则设置为该模型的参数文件名称，如果参数文件保存在多个文件中，则不需要设置。</li>
</ul>
<p>运行以上命令后，可在<code>${save_path}</code>下看到量化后的模型文件和参数文件。</p>
<blockquote>
<p>使用的量化算法为<code>'KL'</code>, 使用训练集中的160张图片进行量化参数的校正。</p>
</blockquote>
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<h3 id="_8">测试精度<a class="headerlink" href="#_8" title="Permanent link">#</a></h3>
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<p>使用<a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/demo/quant/quant_post/eval.py">eval.py</a>脚本对量化前后的模型进行测试，得到模型的分类精度进行对比。</p>
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<p>首先测试量化前的模型的精度，运行以下命令：
<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">python</span> <span class="n">eval</span><span class="p">.</span><span class="n">py</span> <span class="c1">--model_path ./inference_model/MobileNet --model_name model --params_name weights</span>
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精度输出为:
<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">top1_acc</span><span class="o">/</span><span class="n">top5_acc</span><span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">.</span><span class="mi">70913923</span> <span class="mi">0</span><span class="p">.</span><span class="mi">89548034</span><span class="p">]</span>
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<p>使用以下命令测试离线量化后的模型的精度：</p>
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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">python</span> <span class="n">eval</span><span class="p">.</span><span class="n">py</span> <span class="c1">--model_path ./quant_model_train/MobileNet</span>
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</pre></div>
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<p>精度输出为
<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">top1_acc</span><span class="o">/</span><span class="n">top5_acc</span><span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">.</span><span class="mi">70141864</span> <span class="mi">0</span><span class="p">.</span><span class="mi">89086477</span><span class="p">]</span>
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</pre></div>
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</td></tr></table>
从以上精度对比可以看出，对<code>mobilenet</code>在<code>imagenet</code>上的分类模型进行离线量化后 <code>top1</code>精度损失为<code>0.77%</code>， <code>top5</code>精度损失为<code>0.46%</code>. </p>
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