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</li> </li>
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<a href="/docs/tutorials/quant_post_demo/">离线量化示例</a> <a class="" href="/docs/tutorials/quant_post_demo/">离线量化示例</a>
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css/base.css 已删除 100644 → 0
浏览文件 @ 51b6126d
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left: 100%;
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left: -100%;
margin-left: 10px;
-webkit-border-radius: 6px 0 6px 6px;
-moz-border-radius: 6px 0 6px 6px;
border-radius: 6px 0 6px 6px;
}
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...@@ -326,83 +354,53 @@ print(&quot;FLOPS: {}&quot;.format(model_size(main_program))) ...@@ -326,83 +354,53 @@ print(&quot;FLOPS: {}&quot;.format(model_size(main_program)))
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<h2 id="class-sanas">class SANAS</h2> <h2 id="class-sanas">class SANAS</h2>
<p>SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。</p> <p>SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。</p>
...@@ -322,83 +349,53 @@ for step in range(100): ...@@ -322,83 +349,53 @@ for step in range(100):
### 回传score ### 回传score
sa_nas.reward(float(finally_reward[1])) sa_nas.reward(float(finally_reward[1]))
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<h2 id="api">量化训练API</h2> <h2 id="api">量化训练API</h2>
<h3 id="_1">量化配置</h3> <h3 id="_1">量化配置</h3>
<pre><code>quant_config_default = { <pre><code>quant_config_default = {
...@@ -375,83 +403,53 @@ config = {'params_name': 'emb', 'quantize_type': 'abs_max'} ...@@ -375,83 +403,53 @@ config = {'params_name': 'emb', 'quantize_type': 'abs_max'}
quant_program = quant.quant_embedding(infer_program, place, config) quant_program = quant.quant_embedding(infer_program, place, config)
</code></pre> </code></pre>
<p>更详细的用法请参考 <a href='../../demo/quant/quant_embedding/README.md'>Embedding量化demo</a>。</p></div> <p>更详细的用法请参考 <a href='../../demo/quant/quant_embedding/README.md'>Embedding量化demo</a>。</p>
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<ol> <ol>
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...@@ -253,83 +282,53 @@ class ResNetBlockSpace2(SearchSpaceBase): ...@@ -253,83 +282,53 @@ class ResNetBlockSpace2(SearchSpaceBase):
conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv') conv = fluid.layers.conv2d(input, num_filters, filter_size, stride, name=name+'_conv')
bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn') bn = fluid.layers.batch_norm(conv, act=act, name=name+'_bn')
return bn return bn
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<li><a href="#mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_">merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')</a></li> </nav>
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<h2 id="mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_">merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')</h2> <h2 id="mergeteacher_program-student_program-data_name_map-place-scopefluidglobal_scope-name_prefixteacher_">merge(teacher_program, student_program, data_name_map, place, scope=fluid.global_scope(), name_prefix='teacher_')</h2>
<p>该方法将两个fluid program（teacher_program, student_program）融合为一个program，并将融合得到的program返回。在融合的program中，可以为其中合适的teacher特征图和student特征图添加蒸馏损失函数，从而达到用teacher模型的暗知识（Dark Knowledge）指导student模型学习的目的。</p> <p>该方法将两个fluid program（teacher_program, student_program）融合为一个program，并将融合得到的program返回。在融合的program中，可以为其中合适的teacher特征图和student特征图添加蒸馏损失函数，从而达到用teacher模型的暗知识（Dark Knowledge）指导student模型学习的目的。</p>
<p><strong>参数：</strong></p> <p><strong>参数：</strong></p>
...@@ -330,83 +361,53 @@ with fluid.program_guard(main_program): ...@@ -330,83 +361,53 @@ with fluid.program_guard(main_program):
<ol> <ol>
<li>建议与student_program使用同一个命名空间，以避免一些未指定名称的variables(例如tmp_0, tmp_1...)多次定义为同一名称出现命名冲突</li> <li>建议与student_program使用同一个命名空间，以避免一些未指定名称的variables(例如tmp_0, tmp_1...)多次定义为同一名称出现命名冲突</li>
<li>建议在添加蒸馏loss时指定一个命名空间前缀，具体用法请参考Paddle官方文档<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/name_scope_cn.html#name-scope">fluid.name_scope</a></li> <li>建议在添加蒸馏loss时指定一个命名空间前缀，具体用法请参考Paddle官方文档<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/name_scope_cn.html#name-scope">fluid.name_scope</a></li>
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<li class="main active"><a href="#paddleslim">PaddleSlim</a></li> <a href="..">PaddleSlim</a>
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<h1 id="paddleslim">PaddleSlim</h1>
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<h1 id="paddleslim">PaddleSlim</h1>
<p>PaddleSlim是PaddlePaddle框架的一个子模块，主要用于压缩图像领域模型。在PaddleSlim中，不仅实现了目前主流的网络剪枝、量化、蒸馏三种压缩策略，还实现了超参数搜索和小模型网络结构搜索功能。在后续版本中，会添加更多的压缩策略，以及完善对NLP领域模型的支持。</p> <p>PaddleSlim是PaddlePaddle框架的一个子模块，主要用于压缩图像领域模型。在PaddleSlim中，不仅实现了目前主流的网络剪枝、量化、蒸馏三种压缩策略，还实现了超参数搜索和小模型网络结构搜索功能。在后续版本中，会添加更多的压缩策略，以及完善对NLP领域模型的支持。</p>
<h2 id="_1">功能</h2> <h2 id="_1">功能</h2>
<ul> <ul>
...@@ -215,83 +240,53 @@ python setup.py install ...@@ -215,83 +240,53 @@ python setup.py install
<li><a href="">Paddle分割库</a>：介绍如何在分割库中使用PaddleSlim。</li> <li><a href="">Paddle分割库</a>：介绍如何在分割库中使用PaddleSlim。</li>
<li><a href="">PaddleLite</a>：介绍如何使用预测库PaddleLite部署PaddleSlim产出的模型。</li> <li><a href="">PaddleLite</a>：介绍如何使用预测库PaddleLite部署PaddleSlim产出的模型。</li>
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