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                <h2 id="flops">FLOPs<a class="headerlink" href="#flops" title="Permanent link">#</a></h2>
<dl>
<dt>paddleslim.analysis.flops(program, detail=False) <a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/flops.py">源代码</a></dt>
<dd>
<p>获得指定网络的浮点运算次数(FLOPs)。</p>
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<p><strong>参数：</strong></p>
<ul>
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<p><strong>program(paddle.fluid.Program)</strong> - 待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</p>
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<p><strong>detail(bool)</strong> - 是否返回每个卷积层的FLOPs。默认为False。</p>
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<p><strong>only_conv(bool)</strong> - 如果设置为True，则仅计算卷积层和全连接层的FLOPs，即浮点数的乘加（multiplication-adds）操作次数。如果设置为False，则也会计算卷积和全连接层之外的操作的FLOPs。</p>
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<p><strong>返回值：</strong></p>
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<p><strong>flops(float)</strong> - 整个网络的FLOPs。</p>
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<p><strong>params2flops(dict)</strong> - 每层卷积对应的FLOPs，其中key为卷积层参数名称，value为FLOPs值。</p>
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<p><strong>示例：</strong></p>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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53</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="kn">as</span> <span class="nn">fluid</span>
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<span class="kn">from</span> <span class="nn">paddle.fluid.param_attr</span> <span class="kn">import</span> <span class="n">ParamAttr</span>
<span class="kn">from</span> <span class="nn">paddleslim.analysis</span> <span class="kn">import</span> <span class="n">flops</span>

<span class="k">def</span> <span class="nf">conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span>
                  <span class="n">num_filters</span><span class="p">,</span>
                  <span class="n">filter_size</span><span class="p">,</span>
                  <span class="n">name</span><span class="p">,</span>
                  <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                  <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                  <span class="n">act</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
    <span class="n">conv</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span>
        <span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
        <span class="n">num_filters</span><span class="o">=</span><span class="n">num_filters</span><span class="p">,</span>
        <span class="n">filter_size</span><span class="o">=</span><span class="n">filter_size</span><span class="p">,</span>
        <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
        <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">filter_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
        <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
        <span class="n">act</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
        <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_weights&quot;</span><span class="p">),</span>
        <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
        <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_out&quot;</span><span class="p">)</span>
    <span class="n">bn_name</span> <span class="o">=</span> <span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_bn&quot;</span>
    <span class="k">return</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">(</span>
        <span class="nb">input</span><span class="o">=</span><span class="n">conv</span><span class="p">,</span>
        <span class="n">act</span><span class="o">=</span><span class="n">act</span><span class="p">,</span>
        <span class="n">name</span><span class="o">=</span><span class="n">bn_name</span> <span class="o">+</span> <span class="s1">&#39;_output&#39;</span><span class="p">,</span>
        <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">bn_name</span> <span class="o">+</span> <span class="s1">&#39;_scale&#39;</span><span class="p">),</span>
        <span class="n">bias_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">bn_name</span> <span class="o">+</span> <span class="s1">&#39;_offset&#39;</span><span class="p">),</span>
        <span class="n">moving_mean_name</span><span class="o">=</span><span class="n">bn_name</span> <span class="o">+</span> <span class="s1">&#39;_mean&#39;</span><span class="p">,</span>
        <span class="n">moving_variance_name</span><span class="o">=</span><span class="n">bn_name</span> <span class="o">+</span> <span class="s1">&#39;_variance&#39;</span><span class="p">,</span> <span class="p">)</span>

<span class="n">main_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="n">startup_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="c1">#   X       X              O       X              O</span>
<span class="c1"># conv1--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6</span>
<span class="c1">#     |            ^ |                    ^</span>
<span class="c1">#     |____________| |____________________|</span>
<span class="c1">#</span>
<span class="c1"># X: prune output channels</span>
<span class="c1"># O: prune input channels</span>
<span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">program_guard</span><span class="p">(</span><span class="n">main_program</span><span class="p">,</span> <span class="n">startup_program</span><span class="p">):</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv1&quot;</span><span class="p">)</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv2&quot;</span><span class="p">)</span>
    <span class="n">sum1</span> <span class="o">=</span> <span class="n">conv1</span> <span class="o">+</span> <span class="n">conv2</span>
    <span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">sum1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv3&quot;</span><span class="p">)</span>
    <span class="n">conv4</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">conv3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv4&quot;</span><span class="p">)</span>
    <span class="n">sum2</span> <span class="o">=</span> <span class="n">conv4</span> <span class="o">+</span> <span class="n">sum1</span>
    <span class="n">conv5</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">sum2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv5&quot;</span><span class="p">)</span>
    <span class="n">conv6</span> <span class="o">=</span> <span class="n">conv_bn_layer</span><span class="p">(</span><span class="n">conv5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv6&quot;</span><span class="p">)</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flops</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
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</pre></div>
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</td></tr></table>
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<h2 id="model_size">model_size<a class="headerlink" href="#model_size" title="Permanent link">#</a></h2>
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<p>paddleslim.analysis.model_size(program) <a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/model_size.py">源代码</a></p>
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<p>获得指定网络的参数数量。</p>
<p><strong>参数：</strong></p>
<ul>
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<li><strong>program(paddle.fluid.Program)</strong> - 待分析的目标网络。更多关于Program的介绍请参考：<a href="https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/Program_cn.html#program">Program概念介绍</a>。</li>
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</ul>
<p><strong>返回值：</strong></p>
<ul>
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<li><strong>model_size(int)</strong> - 整个网络的参数数量。</li>
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<p><strong>示例：</strong></p>
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<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span> 1
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45</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="kn">as</span> <span class="nn">fluid</span>
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<span class="kn">from</span> <span class="nn">paddle.fluid.param_attr</span> <span class="kn">import</span> <span class="n">ParamAttr</span>
<span class="kn">from</span> <span class="nn">paddleslim.analysis</span> <span class="kn">import</span> <span class="n">model_size</span>

<span class="k">def</span> <span class="nf">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span>
                  <span class="n">num_filters</span><span class="p">,</span>
                  <span class="n">filter_size</span><span class="p">,</span>
                  <span class="n">name</span><span class="p">,</span>
                  <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                  <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                  <span class="n">act</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
    <span class="n">conv</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span>
        <span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span>
        <span class="n">num_filters</span><span class="o">=</span><span class="n">num_filters</span><span class="p">,</span>
        <span class="n">filter_size</span><span class="o">=</span><span class="n">filter_size</span><span class="p">,</span>
        <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
        <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">filter_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
        <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
        <span class="n">act</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
        <span class="n">param_attr</span><span class="o">=</span><span class="n">ParamAttr</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_weights&quot;</span><span class="p">),</span>
        <span class="n">bias_attr</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
        <span class="n">name</span><span class="o">=</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_out&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">conv</span>

<span class="n">main_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="n">startup_program</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">Program</span><span class="p">()</span>
<span class="c1">#   X       X              O       X              O</span>
<span class="c1"># conv1--&gt;conv2--&gt;sum1--&gt;conv3--&gt;conv4--&gt;sum2--&gt;conv5--&gt;conv6</span>
<span class="c1">#     |            ^ |                    ^</span>
<span class="c1">#     |____________| |____________________|</span>
<span class="c1">#</span>
<span class="c1"># X: prune output channels</span>
<span class="c1"># O: prune input channels</span>
<span class="k">with</span> <span class="n">fluid</span><span class="o">.</span><span class="n">program_guard</span><span class="p">(</span><span class="n">main_program</span><span class="p">,</span> <span class="n">startup_program</span><span class="p">):</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;image&quot;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="bp">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv1&quot;</span><span class="p">)</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv2&quot;</span><span class="p">)</span>
    <span class="n">sum1</span> <span class="o">=</span> <span class="n">conv1</span> <span class="o">+</span> <span class="n">conv2</span>
    <span class="n">conv3</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">sum1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv3&quot;</span><span class="p">)</span>
    <span class="n">conv4</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">conv3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv4&quot;</span><span class="p">)</span>
    <span class="n">sum2</span> <span class="o">=</span> <span class="n">conv4</span> <span class="o">+</span> <span class="n">sum1</span>
    <span class="n">conv5</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">sum2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv5&quot;</span><span class="p">)</span>
    <span class="n">conv6</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="n">conv5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;conv6&quot;</span><span class="p">)</span>
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<span class="k">print</span><span class="p">(</span><span class="s2">&quot;FLOPs: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_size</span><span class="p">(</span><span class="n">main_program</span><span class="p">)))</span>
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</pre></div>
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</td></tr></table>
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<h2 id="tablelatencyevaluator">TableLatencyEvaluator<a class="headerlink" href="#tablelatencyevaluator" title="Permanent link">#</a></h2>
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<dl>
<dt>paddleslim.analysis.TableLatencyEvaluator(table_file, delimiter=",") <a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/latency.py">源代码</a></dt>
<dd>
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<p>基于硬件延时表的模型延时评估器。</p>
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</dd>
</dl>
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<p><strong>参数：</strong></p>
<ul>
<li>
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<p><strong>table_file(str)</strong> - 所使用的延时评估表的绝对路径。关于演示评估表格式请参考：<a href="../paddleslim/analysis/table_latency.md">PaddleSlim硬件延时评估表格式</a></p>
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</li>
<li>
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<p><strong>delimiter(str)</strong> - 硬件延时评估表中，操作信息之前所使用的分割符，默认为英文字符逗号。</p>
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</li>
</ul>
<p><strong>返回值：</strong></p>
<ul>
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<li><strong>Evaluator</strong> - 硬件延时评估器的实例。</li>
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</ul>
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<dl>
<dt>paddleslim.analysis.TableLatencyEvaluator.latency(graph) <a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/analysis/latency.py">源代码</a></dt>
<dd>
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<p>获得指定网络的预估延时。</p>
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</dd>
</dl>
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<p><strong>参数：</strong></p>
<ul>
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<li><strong>graph(Program)</strong> - 待预估的目标网络。</li>
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</ul>
<p><strong>返回值：</strong></p>
<ul>
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<li><strong>latency</strong> - 目标网络的预估延时。</li>
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</ul>
              
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