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                <h1 id="_1">在线量化示例<a class="headerlink" href="#_1" title="Permanent link">#</a></h1>
<p>本示例介绍如何使用在线量化接口，来对训练好的分类模型进行量化, 可以减少模型的存储空间和显存占用。</p>
<h2 id="_2">接口介绍<a class="headerlink" href="#_2" title="Permanent link">#</a></h2>
<p>请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>量化API文档</a>。</p>
<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="1">1. 配置量化参数<a class="headerlink" href="#1" title="Permanent link">#</a></h3>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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    <span class="s1">&#39;weight_quantize_type&#39;</span><span class="p">:</span> <span class="s1">&#39;abs_max&#39;</span><span class="p">,</span>
    <span class="s1">&#39;activation_quantize_type&#39;</span><span class="p">:</span> <span class="s1">&#39;moving_average_abs_max&#39;</span><span class="p">,</span>
    <span class="s1">&#39;weight_bits&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
    <span class="s1">&#39;activation_bits&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
    <span class="s1">&#39;not_quant_pattern&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;skip_quant&#39;</span><span class="p">],</span>
    <span class="s1">&#39;quantize_op_types&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;conv2d&#39;</span><span class="p">,</span> <span class="s1">&#39;depthwise_conv2d&#39;</span><span class="p">,</span> <span class="s1">&#39;mul&#39;</span><span class="p">],</span>
    <span class="s1">&#39;dtype&#39;</span><span class="p">:</span> <span class="s1">&#39;int8&#39;</span><span class="p">,</span>
    <span class="s1">&#39;window_size&#39;</span><span class="p">:</span> <span class="mi">10000</span><span class="p">,</span>
    <span class="s1">&#39;moving_rate&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">.</span><span class="mi">9</span><span class="p">,</span>
    <span class="s1">&#39;quant_weight_only&#39;</span><span class="p">:</span> <span class="k">False</span>
<span class="err">}</span>
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<h3 id="2-programop">2. 对训练和测试program插入可训练量化op<a class="headerlink" href="#2-programop" title="Permanent link">#</a></h3>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
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<span class="n">compiled_train_prog</span> <span class="o">=</span> <span class="n">quant_aware</span><span class="p">(</span><span class="n">train_prog</span><span class="p">,</span> <span class="n">place</span><span class="p">,</span> <span class="n">quant_config</span><span class="p">,</span> <span class="k">scope</span><span class="o">=</span><span class="k">None</span><span class="p">,</span> <span class="n">for_test</span><span class="o">=</span><span class="k">False</span><span class="p">)</span>
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<h3 id="3build">3.关掉指定build策略<a class="headerlink" href="#3build" title="Permanent link">#</a></h3>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
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<span class="n">build_strategy</span><span class="p">.</span><span class="n">fuse_all_reduce_ops</span> <span class="o">=</span> <span class="k">False</span>
<span class="n">build_strategy</span><span class="p">.</span><span class="n">sync_batch_norm</span> <span class="o">=</span> <span class="k">False</span>
<span class="n">exec_strategy</span> <span class="o">=</span> <span class="n">fluid</span><span class="p">.</span><span class="n">ExecutionStrategy</span><span class="p">()</span>
<span class="n">compiled_train_prog</span> <span class="o">=</span> <span class="n">compiled_train_prog</span><span class="p">.</span><span class="n">with_data_parallel</span><span class="p">(</span>
        <span class="n">loss_name</span><span class="o">=</span><span class="n">avg_cost</span><span class="p">.</span><span class="n">name</span><span class="p">,</span>
        <span class="n">build_strategy</span><span class="o">=</span><span class="n">build_strategy</span><span class="p">,</span>
        <span class="n">exec_strategy</span><span class="o">=</span><span class="n">exec_strategy</span><span class="p">)</span>
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<h3 id="4-freeze-program">4. freeze program<a class="headerlink" href="#4-freeze-program" title="Permanent link">#</a></h3>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span>1
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                                      <span class="n">place</span><span class="p">,</span>
                                      <span class="n">quant_config</span><span class="p">,</span>
                                      <span class="k">scope</span><span class="o">=</span><span class="k">None</span><span class="p">,</span>
                                      <span class="n">save_int8</span><span class="o">=</span><span class="k">True</span><span class="p">)</span>
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<h3 id="5">5.保存预测模型<a class="headerlink" href="#5" title="Permanent link">#</a></h3>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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    <span class="k">dirname</span><span class="o">=</span><span class="nv">float_path</span>,
    <span class="nv">feeded_var_names</span><span class="o">=</span>[<span class="nv">image</span>.<span class="nv">name</span>],
    <span class="nv">target_vars</span><span class="o">=</span>[<span class="nv">out</span>], <span class="nv">executor</span><span class="o">=</span><span class="nv">exe</span>,
    <span class="nv">main_program</span><span class="o">=</span><span class="nv">float_program</span>,
    <span class="nv">model_filename</span><span class="o">=</span><span class="nv">float_path</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="s">/model</span><span class="s1">&#39;</span>,
    <span class="nv">params_filename</span><span class="o">=</span><span class="nv">float_path</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="s">/params</span><span class="s1">&#39;</span><span class="ss">)</span>

<span class="nv">fluid</span>.<span class="nv">io</span>.<span class="nv">save_inference_model</span><span class="ss">(</span>
    <span class="k">dirname</span><span class="o">=</span><span class="nv">int8_path</span>,
    <span class="nv">feeded_var_names</span><span class="o">=</span>[<span class="nv">image</span>.<span class="nv">name</span>],
    <span class="nv">target_vars</span><span class="o">=</span>[<span class="nv">out</span>], <span class="nv">executor</span><span class="o">=</span><span class="nv">exe</span>,
    <span class="nv">main_program</span><span class="o">=</span><span class="nv">int8_program</span>,
    <span class="nv">model_filename</span><span class="o">=</span><span class="nv">int8_path</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="s">/model</span><span class="s1">&#39;</span>,
    <span class="nv">params_filename</span><span class="o">=</span><span class="nv">int8_path</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="s">/params</span><span class="s1">&#39;</span><span class="ss">)</span>
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