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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>
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<p>请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>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="1">1. 配置量化参数<a class="headerlink" href="#1" title="Permanent link">#</a></h3>
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<div class="highlight"><pre><span></span>quant_config = {
    &#39;weight_quantize_type&#39;: &#39;abs_max&#39;,
    &#39;activation_quantize_type&#39;: &#39;moving_average_abs_max&#39;,
    &#39;weight_bits&#39;: 8,
    &#39;activation_bits&#39;: 8,
    &#39;not_quant_pattern&#39;: [&#39;skip_quant&#39;],
    &#39;quantize_op_types&#39;: [&#39;conv2d&#39;, &#39;depthwise_conv2d&#39;, &#39;mul&#39;],
    &#39;dtype&#39;: &#39;int8&#39;,
    &#39;window_size&#39;: 10000,
    &#39;moving_rate&#39;: 0.9,
    &#39;quant_weight_only&#39;: False
}
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</pre></div>

<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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<div class="highlight"><pre><span></span>val_program = quant_aware(val_program, place, quant_config, scope=None, for_test=True)
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compiled_train_prog = quant_aware(train_prog, place, quant_config, scope=None, for_test=False)
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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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<div class="highlight"><pre><span></span>build_strategy = fluid.BuildStrategy()
build_strategy.fuse_all_reduce_ops = False
build_strategy.sync_batch_norm = False
exec_strategy = fluid.ExecutionStrategy()
compiled_train_prog = compiled_train_prog.with_data_parallel(
        loss_name=avg_cost.name,
        build_strategy=build_strategy,
        exec_strategy=exec_strategy)
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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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<div class="highlight"><pre><span></span>float_program, int8_program = convert(val_program, 
                                      place,
                                      quant_config,
                                      scope=None,
                                      save_int8=True)
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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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<div class="highlight"><pre><span></span>fluid.io.save_inference_model(
    dirname=float_path,
    feeded_var_names=[image.name],
    target_vars=[out], executor=exe,
    main_program=float_program,
    model_filename=float_path + &#39;/model&#39;,
    params_filename=float_path + &#39;/params&#39;)
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fluid.io.save_inference_model(
    dirname=int8_path,
    feeded_var_names=[image.name],
    target_vars=[out], executor=exe,
    main_program=int8_program,
    model_filename=int8_path + &#39;/model&#39;,
    params_filename=int8_path + &#39;/params&#39;)
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</pre></div>
              
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