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<li class="toctree-l3"><a class="reference internal" href="#id2">1. 导入依赖</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id3">2. 构建网络</a></li>
<li class="toctree-l3"><a class="reference internal" href="#id4">3. 训练模型</a><ul>
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  <div class="section" id="id1">
<h1>图像分类模型离线量化-快速开始<a class="headerlink" href="#id1" title="永久链接至标题"></a></h1>
<p>该教程以图像分类模型MobileNetV1为例，说明如何快速使用PaddleSlim的<a class="reference external" href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/docs/api/quantization_api">离线量化接口</a>。 该示例包含以下步骤：</p>
<ol class="simple">
<li>导入依赖</li>
<li>构建模型</li>
<li>训练模型</li>
<li>离线量化</li>
</ol>
<div class="section" id="id2">
<h2>1. 导入依赖<a class="headerlink" href="#id2" title="永久链接至标题"></a></h2>
<p>PaddleSlim依赖Paddle1.7版本，请确认已正确安装Paddle，然后按以下方式导入Paddle和PaddleSlim:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle</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</span> <span class="k">as</span> <span class="nn">slim</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
</div>
<div class="section" id="id3">
<h2>2. 构建网络<a class="headerlink" href="#id3" title="永久链接至标题"></a></h2>
<p>该章节构造一个用于对MNIST数据进行分类的分类模型，选用<code class="docutils literal"><span class="pre">MobileNetV1</span></code>，并将输入大小设置为<code class="docutils literal"><span class="pre">[1,</span> <span class="pre">28,</span> <span class="pre">28]</span></code>，输出类别数为10。               为了方便展示示例，我们在<code class="docutils literal"><span class="pre">paddleslim.models</span></code>下预定义了用于构建分类模型的方法，执行以下代码构建分类模型：</p>
<blockquote>
<div>注意：paddleslim.models下的API并非PaddleSlim常规API，是为了简化示例而封装预定义的一系列方法，比如：模型结构的定义、Program的构建等。</div></blockquote>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">exe</span><span class="p">,</span> <span class="n">train_program</span><span class="p">,</span> <span class="n">val_program</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> \
    <span class="n">slim</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">image_classification</span><span class="p">(</span><span class="s2">&quot;MobileNet&quot;</span><span class="p">,</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="mi">10</span><span class="p">,</span> <span class="n">use_gpu</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="id4">
<h2>3. 训练模型<a class="headerlink" href="#id4" title="永久链接至标题"></a></h2>
<p>该章节介绍了如何定义输入数据和如何训练和测试分类模型。先训练分类模型的原因是离线量化需要一个训练好的模型。</p>
<div class="section" id="id5">
<h3>3.1 定义输入数据<a class="headerlink" href="#id5" title="永久链接至标题"></a></h3>
<p>为了快速执行该示例，我们选取简单的MNIST数据，Paddle框架的<code class="docutils literal"><span class="pre">paddle.dataset.mnist</span></code>包定义了MNIST数据的下载和读取。
代码如下：</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.dataset.mnist</span> <span class="k">as</span> <span class="nn">reader</span>
<span class="n">train_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
        <span class="n">reader</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">test_reader</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span>
        <span class="n">reader</span><span class="o">.</span><span class="n">train</span><span class="p">(),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">train_feeder</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">DataFeeder</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">fluid</span><span class="o">.</span><span class="n">CPUPlace</span><span class="p">())</span>
</pre></div>
</div>
</div>
<div class="section" id="id6">
<h3>3.2 训练和测试<a class="headerlink" href="#id6" title="永久链接至标题"></a></h3>
<p>先定义训练和测试函数。在训练函数中执行了一个epoch的训练，因为MNIST数据集数据较少，一个epoch就可将top1精度训练到95%以上。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">prog</span><span class="p">):</span>
    <span class="nb">iter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">train_reader</span><span class="p">():</span>
        <span class="n">acc1</span><span class="p">,</span> <span class="n">acc5</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">prog</span><span class="p">,</span> <span class="n">feed</span><span class="o">=</span><span class="n">train_feeder</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">fetch_list</span><span class="o">=</span><span class="n">outputs</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">acc1</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">acc5</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
        <span class="nb">iter</span> <span class="o">+=</span> <span class="mi">1</span>
        
<span class="k">def</span> <span class="nf">test</span><span class="p">(</span><span class="n">prog</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">outputs</span><span class="p">):</span>
    <span class="nb">iter</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">res</span> <span class="o">=</span> <span class="p">[[],</span> <span class="p">[]]</span>
    <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">train_reader</span><span class="p">():</span>
        <span class="n">acc1</span><span class="p">,</span> <span class="n">acc5</span><span class="p">,</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">exe</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">prog</span><span class="p">,</span> <span class="n">feed</span><span class="o">=</span><span class="n">train_feeder</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">fetch_list</span><span class="o">=</span><span class="n">outputs</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">iter</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">acc1</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">acc5</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
        <span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">acc1</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
        <span class="n">res</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">acc5</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
        <span class="nb">iter</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;final test result&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
</pre></div>
</div>
<p>调用<code class="docutils literal"><span class="pre">train</span></code>函数训练分类网络，<code class="docutils literal"><span class="pre">train_program</span></code>是在第2步：构建网络中定义的。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="n">train_program</span><span class="p">)</span>
</pre></div>
</div>
<p>调用<code class="docutils literal"><span class="pre">test</span></code>函数测试分类网络，<code class="docutils literal"><span class="pre">val_program</span></code>是在第2步：构建网络中定义的。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">test</span><span class="p">(</span><span class="n">val_program</span><span class="p">)</span>
</pre></div>
</div>
<p>保存inference model，将训练好的分类模型保存在<code class="docutils literal"><span class="pre">'./inference_model'</span></code>下，后续进行离线量化时将加载保存在此处的模型。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">target_vars</span> <span class="o">=</span> <span class="p">[</span><span class="n">val_program</span><span class="o">.</span><span class="n">global_block</span><span class="p">()</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">]</span>
<span class="n">fluid</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">save_inference_model</span><span class="p">(</span><span class="n">dirname</span><span class="o">=</span><span class="s1">&#39;./inference_model&#39;</span><span class="p">,</span>
        <span class="n">feeded_var_names</span><span class="o">=</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">],</span>
        <span class="n">target_vars</span><span class="o">=</span><span class="n">target_vars</span><span class="p">,</span>
        <span class="n">executor</span><span class="o">=</span><span class="n">exe</span><span class="p">,</span>
        <span class="n">main_program</span><span class="o">=</span><span class="n">val_program</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id7">
<h2>4. 离线量化<a class="headerlink" href="#id7" title="永久链接至标题"></a></h2>
<p>调用离线量化接口，加载文件夹<code class="docutils literal"><span class="pre">'./inference_model'</span></code>训练好的分类模型，并使用10个batch的数据进行参数校正。此过程无需训练，只需跑前向过程来计算量化所需参数。离线量化后的模型保存在文件夹<code class="docutils literal"><span class="pre">'./quant_post_model'</span></code>下。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">slim</span><span class="o">.</span><span class="n">quant</span><span class="o">.</span><span class="n">quant_post</span><span class="p">(</span>
        <span class="n">executor</span><span class="o">=</span><span class="n">exe</span><span class="p">,</span>
        <span class="n">model_dir</span><span class="o">=</span><span class="s1">&#39;./inference_model&#39;</span><span class="p">,</span>
        <span class="n">quantize_model_path</span><span class="o">=</span><span class="s1">&#39;./quant_post_model&#39;</span><span class="p">,</span>
        <span class="n">sample_generator</span><span class="o">=</span><span class="n">reader</span><span class="o">.</span><span class="n">test</span><span class="p">(),</span>
        <span class="n">batch_nums</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
<p>加载保存在文件夹<code class="docutils literal"><span class="pre">'./quant_post_model'</span></code>下的量化后的模型进行测试，可看到精度和<code class="docutils literal"><span class="pre">3.2</span> <span class="pre">训练和测试</span></code>中得到的测试精度相近，因此离线量化过程对于此分类模型几乎无损。</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">quant_post_prog</span><span class="p">,</span> <span class="n">feed_target_names</span><span class="p">,</span> <span class="n">fetch_targets</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">load_inference_model</span><span class="p">(</span>
        <span class="n">dirname</span><span class="o">=</span><span class="s1">&#39;./quant_post_model&#39;</span><span class="p">,</span>
        <span class="n">executor</span><span class="o">=</span><span class="n">exe</span><span class="p">)</span>
<span class="n">test</span><span class="p">(</span><span class="n">quant_post_prog</span><span class="p">,</span> <span class="n">fetch_targets</span><span class="p">)</span>
</pre></div>
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