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<section id="conv2d">
<h1>Conv2d<a class="headerlink" href="#conv2d" title="Link to this heading"></a></h1>
<p>对输入 Tensor 计算二维卷积，输入的 shape 为 <span class="math notranslate nohighlight">\((N, H_{in}, W_{in}, C_{in})\)</span>，其中 <span class="math notranslate nohighlight">\(N\)</span> 为 batch size，<span class="math notranslate nohighlight">\(C\)</span> 为通道数，<span class="math notranslate nohighlight">\(H\)</span> 为特征图的高度，<span class="math notranslate nohighlight">\(W\)</span> 为特征图的宽度。</p>
<p>根据以下公式计算输出：</p>
<div class="math notranslate nohighlight">
\[out(N_i, C_{out_j}) = bias(C_{out_j}) + \sum_{k=0}^{C_{in}-1} \text{ccor}(\text{weight}(C_{out_j}, k), X(N_i, k))\]</div>
<p>其中，<span class="math notranslate nohighlight">\(bias\)</span> 为输出偏置，<span class="math notranslate nohighlight">\(\text{ccor}\)</span> 为 cross-correlation 操作，<span class="math notranslate nohighlight">\(weight\)</span> 为卷积核的值，<span class="math notranslate nohighlight">\(X\)</span> 为输入的特征图。</p>
<ul class="simple">
<li><p><span class="math notranslate nohighlight">\(i\)</span> 对应 batch 数，其范围为 <span class="math notranslate nohighlight">\([0, N-1]\)</span>，其中 <span class="math notranslate nohighlight">\(N\)</span> 为输入 batch。</p></li>
<li><p><span class="math notranslate nohighlight">\(j\)</span> 对应输出通道，其范围为 <span class="math notranslate nohighlight">\([0, C_{out}-1]\)</span>，其中 <span class="math notranslate nohighlight">\(C_{out}\)</span> 为输出通道数，该值也等于卷积核的个数。</p></li>
<li><p><span class="math notranslate nohighlight">\(k\)</span> 对应输入通道数，其范围为 <span class="math notranslate nohighlight">\([0, C_{in}-1]\)</span>，其中 <span class="math notranslate nohighlight">\(C_{in}\)</span> 为输入通道数，该值也等于卷积核的通道数。</p></li>
</ul>
<p>因此，上面的公式中，<span class="math notranslate nohighlight">\(bias(C_{out_j})\)</span> 为第 <span class="math notranslate nohighlight">\(j\)</span> 个输出通道的偏置，<span class="math notranslate nohighlight">\(weight(C_{out_j}, k)\)</span> 表示第 <span class="math notranslate nohighlight">\(j\)</span> 个卷积核在第 <span class="math notranslate nohighlight">\(k\)</span> 个输入通道的卷积核切片，<span class="math notranslate nohighlight">\(X(N_i, k)\)</span> 为特征图第 <span class="math notranslate nohighlight">\(i\)</span> 个 batch 第 <span class="math notranslate nohighlight">\(k\)</span> 个输入通道的切片。卷积核 shape 为 <span class="math notranslate nohighlight">\((\text{kernel\_size}[0], \text{kernel\_size}[1])\)</span>，其中 kernel_size[0] 和 kernel_size[1] 是卷积核的高度和宽度。若考虑到输入输出通道以及 group，则完整卷积核的 shape 为 <span class="math notranslate nohighlight">\((C_{out}, \text{kernel\_size}[0], \text{kernel\_size}[1], C_{in}/\text{group})\)</span>，其中 group 是分组卷积时在通道上分割输入 <span class="math notranslate nohighlight">\(x\)</span> 的组数。</p>
<dl class="simple">
<dt>输入：</dt><dd><ul class="simple">
<li><p><strong>input_x</strong> - 输入数据的地址</p></li>
<li><p><strong>input_w</strong> - 输入卷积核权重的地址</p></li>
<li><p><strong>bias</strong> - 输入偏置的地址</p></li>
<li><p><strong>conv_param</strong> - 算子计算所需参数的结构体。其各成员见下述。</p></li>
<li><p><strong>quant_param</strong> - 对int8类型进行量化计算所需参数的结构体。其各成员见下述。</p></li>
<li><p><strong>core_mask</strong> - 核掩码。</p></li>
</ul>
</dd>
</dl>
<p><strong>ConvParameter及ConvQuantParameter定义：</strong></p>
<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="k">typedef</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">ConvParameter</span><span class="w"> </span><span class="p">{</span>
<span class="linenos"> 2</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">workspace_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 用于存放中间计算结果</span>
<span class="linenos"> 3</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_batch_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据总批次</span>
<span class="linenos"> 4</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_batch_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输入数据总批次</span>
<span class="linenos"> 5</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_h_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输入数据h维度大小</span>
<span class="linenos"> 6</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_w_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输入数据w维度大小</span>
<span class="linenos"> 7</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_h_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据h维度大小</span>
<span class="linenos"> 8</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_w_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据w维度大小</span>
<span class="linenos"> 9</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_channel_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输入数据通道数</span>
<span class="linenos">10</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_channel_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 输出数据通道数</span>
<span class="linenos">11</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">kernel_h_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核h维度大小</span>
<span class="linenos">12</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">kernel_w_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核w维度大小</span>
<span class="linenos">13</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">group_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 组数</span>
<span class="linenos">14</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">pad_l_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 左填充大小</span>
<span class="linenos">15</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">pad_u_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 上填充大小</span>
<span class="linenos">16</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">dilation_h_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核h维度膨胀尺寸大小</span>
<span class="linenos">17</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">dilation_w_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核w维度膨胀尺寸大小</span>
<span class="linenos">18</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">stride_h_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核h维度步长</span>
<span class="linenos">19</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">stride_w_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积核w维度步长</span>
<span class="linenos">20</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">buffer_size_</span><span class="p">;</span><span class="w"> </span><span class="c1">// 为分块计算所分配的缓存大小</span>
<span class="linenos">21</span><span class="p">}</span><span class="w"> </span><span class="n">ConvParameter</span><span class="p">;</span>
<span class="linenos">22</span>
<span class="linenos">23</span><span class="k">typedef</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">ConvQuantParameter</span><span class="w"> </span><span class="p">{</span>
<span class="linenos">24</span><span class="w"> </span><span class="kt">int32_t</span><span class="o">*</span><span class="w"> </span><span class="n">left_shift_</span><span class="p">;</span>
<span class="linenos">25</span><span class="w"> </span><span class="kt">int32_t</span><span class="o">*</span><span class="w"> </span><span class="n">right_shift_</span><span class="p">;</span>
<span class="linenos">26</span><span class="w"> </span><span class="kt">int32_t</span><span class="o">*</span><span class="w"> </span><span class="n">multiplier_</span><span class="p">;</span>
<span class="linenos">27</span><span class="w"> </span><span class="kt">int32_t</span><span class="o">*</span><span class="w"> </span><span class="n">filter_zp_ptr_</span><span class="p">;</span>
<span class="linenos">28</span><span class="w"> </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">output_zp_</span><span class="p">;</span>
<span class="linenos">29</span><span class="w"> </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">mini_</span><span class="p">;</span>
<span class="linenos">30</span><span class="w"> </span><span class="kt">int32_t</span><span class="w"> </span><span class="n">maxi_</span><span class="p">;</span>
<span class="linenos">31</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">per_channel_</span><span class="p">;</span>
<span class="linenos">32</span><span class="p">}</span><span class="w"> </span><span class="n">ConvQuantParameter</span><span class="p">;</span>
</pre></div>
</div>
<dl class="simple">
<dt>输出：</dt><dd><ul class="simple">
<li><p><strong>out_y</strong> - 输出地址。</p></li>
</ul>
</dd>
<dt>支持平台：</dt><dd><p><code class="docutils literal notranslate"><span class="pre">FT78NE</span></code>
<code class="docutils literal notranslate"><span class="pre">MT7004</span></code></p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">备注</p>
<ul class="simple">
<li><p>FT78NE 支持int8, fp32</p></li>
<li><p>MT7004 支持fp16, fp32</p></li>
</ul>
</div>
<p><strong>共享存储版本:</strong></p>
<dl class="c function">
<dt class="sig sig-object c" id="c.i8_conv2d_s">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_conv2d_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="n"><span class="pre">ConvQuantParameter</span></span><span class="w"> </span><span class="n"><span class="pre">quant_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_conv2d_s" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<dl class="c function">
<dt class="sig sig-object c" id="c.hp_conv2d_s">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_conv2d_s</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_conv2d_s" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<dl class="c function">
<dt class="sig sig-object c" id="c.fp_conv2d_s">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_conv2d_s</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_conv2d_s" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<p><strong>C调用示例：</strong></p>
<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kt">void</span><span class="w"> </span><span class="nf">TestConvSMCFp32</span><span class="p">(</span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">input_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">output_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">padding</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">dilation</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">groups</span><span class="p">,</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_mask</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="linenos"> 2</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">get_core_id</span><span class="p">();</span>
<span class="linenos"> 3</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">logic_core_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GetLogicCoreId</span><span class="p">(</span><span class="n">core_mask</span><span class="p">,</span><span class="w"> </span><span class="n">core_id</span><span class="p">);</span>
<span class="linenos"> 4</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_num</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">GetCoreNum</span><span class="p">(</span><span class="n">core_mask</span><span class="p">);</span>
<span class="linenos"> 5</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">input_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x88000000</span><span class="p">;</span>
<span class="linenos"> 6</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">weight</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x89000000</span><span class="p">;</span>
<span class="linenos"> 7</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">output_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x90000000</span><span class="p">;</span>
<span class="linenos"> 8</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">bias_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x91000000</span><span class="p">;</span>
<span class="linenos"> 9</span><span class="w"> </span><span class="n">ConvParameter</span><span class="o">*</span><span class="w"> </span><span class="n">param</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">ConvParameter</span><span class="o">*</span><span class="p">)</span><span class="mh">0x92000000</span><span class="p">;</span>
<span class="linenos">10</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">logic_core_id</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">0</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="linenos">11</span><span class="w"> </span><span class="n">memcpy</span><span class="p">(</span><span class="n">bias_data</span><span class="p">,</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="kt">float</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]);</span>
<span class="linenos">12</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">dilation_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">13</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">dilation_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">14</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">group_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">groups</span><span class="p">;</span>
<span class="linenos">15</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">16</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">17</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">18</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_channel_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">];</span>
<span class="linenos">19</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">kernel_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">20</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">kernel_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">21</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">22</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">23</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">24</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_channel_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">];</span>
<span class="linenos">25</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">stride_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">26</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">stride_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">27</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">pad_u_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">28</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">pad_l_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">29</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">workspace_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10000000</span><span class="p">;</span><span class="w"> </span><span class="c1">// workspace空间需分配在AM内，计算过程中会将数据搬运到workspace空间内进行计算</span>
<span class="linenos">30</span><span class="w"> </span><span class="p">}</span>
<span class="linenos">31</span><span class="w"> </span><span class="n">sys_bar</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">core_num</span><span class="p">);</span><span class="w"> </span><span class="c1">// 初始化参数完成后进行同步</span>
<span class="hll"><span class="linenos">32</span><span class="w"> </span><span class="n">fp_conv2d_s</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span><span class="w"> </span><span class="n">weight</span><span class="p">,</span><span class="w"> </span><span class="n">output_data</span><span class="p">,</span><span class="w"> </span><span class="n">bias_data</span><span class="p">,</span><span class="w"> </span><span class="n">param</span><span class="p">,</span><span class="w"> </span><span class="n">core_mask</span><span class="p">);</span>
</span><span class="linenos">33</span><span class="p">}</span>
<span class="linenos">34</span>
<span class="linenos">35</span><span class="kt">void</span><span class="w"> </span><span class="nf">main</span><span class="p">(){</span>
<span class="linenos">36</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">in_channel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">37</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">out_channel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">38</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">groups</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">39</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">30</span><span class="p">,</span><span class="w"> </span><span class="mi">30</span><span class="p">,</span><span class="w"> </span><span class="n">in_channel</span><span class="p">};</span><span class="w"> </span><span class="c1">// NHWC</span>
<span class="linenos">40</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">out_channel</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="n">in_channel</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">groups</span><span class="p">};</span>
<span class="linenos">41</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">,</span><span class="w"> </span><span class="n">out_channel</span><span class="p">};</span><span class="w"> </span><span class="c1">// NHWC</span>
<span class="linenos">42</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
<span class="linenos">43</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">};</span>
<span class="linenos">44</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
<span class="linenos">45</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="n">bias</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">};</span>
<span class="linenos">46</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mb">0b1111</span><span class="p">;</span>
<span class="linenos">47</span><span class="w"> </span><span class="n">TestConvSMCFp32</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">,</span><span class="w"> </span><span class="n">output_shape</span><span class="p">,</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="n">padding</span><span class="p">,</span><span class="w"> </span><span class="n">dilation</span><span class="p">,</span><span class="w"> </span><span class="n">groups</span><span class="p">,</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="n">core_mask</span><span class="p">);</span>
<span class="linenos">48</span><span class="p">}</span>
</pre></div>
</div>
<p><strong>私有存储版本:</strong></p>
<dl class="c function">
<dt class="sig sig-object c" id="c.i8_conv2d_p">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">i8_conv2d_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="n"><span class="pre">int8_t</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="n"><span class="pre">ConvQuantParameter</span></span><span class="w"> </span><span class="n"><span class="pre">quant_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.i8_conv2d_p" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<dl class="c function">
<dt class="sig sig-object c" id="c.hp_conv2d_p">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">hp_conv2d_p</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="n"><span class="pre">half</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.hp_conv2d_p" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<dl class="c function">
<dt class="sig sig-object c" id="c.fp_conv2d_p">
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">fp_conv2d_p</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_x</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">input_w</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">out_y</span></span>, <span class="kt"><span class="pre">float</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">bias</span></span>, <span class="n"><span class="pre">ConvParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">conv_param</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">core_mask</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.fp_conv2d_p" title="Link to this definition"></a><br /></dt>
<dd></dd></dl>
<p><strong>C调用示例：</strong></p>
<div class="highlight-c notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kt">void</span><span class="w"> </span><span class="nf">TestConvL2Fp32</span><span class="p">(</span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">input_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">output_shape</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">padding</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">dilation</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">groups</span><span class="p">,</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_mask</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="linenos"> 2</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">input_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10010000</span><span class="p">;</span><span class="w"> </span><span class="c1">// 私有存储版本地址设置在AM内</span>
<span class="linenos"> 3</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">weight</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10020000</span><span class="p">;</span>
<span class="linenos"> 4</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">output_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10030000</span><span class="p">;</span>
<span class="linenos"> 5</span><span class="w"> </span><span class="kt">float</span><span class="o">*</span><span class="w"> </span><span class="n">bias_data</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10040000</span><span class="p">;</span>
<span class="linenos"> 6</span><span class="w"> </span><span class="n">ConvParameter</span><span class="o">*</span><span class="w"> </span><span class="n">param</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">ConvParameter</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10060000</span><span class="p">;</span>
<span class="linenos"> 7</span><span class="w"> </span><span class="n">memcpy</span><span class="p">(</span><span class="n">bias_data</span><span class="p">,</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="kt">float</span><span class="p">)</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]);</span>
<span class="linenos"> 8</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">dilation_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos"> 9</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">dilation_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">10</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">group_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">groups</span><span class="p">;</span>
<span class="linenos">11</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">12</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">13</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">14</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">input_channel_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">];</span>
<span class="linenos">15</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">kernel_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">16</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">kernel_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">17</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">18</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">];</span>
<span class="linenos">19</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">20</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">output_channel_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">];</span>
<span class="linenos">21</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">stride_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">22</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">stride_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">23</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">pad_u_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">0</span><span class="p">];</span>
<span class="linenos">24</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">pad_l_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">2</span><span class="p">];</span>
<span class="linenos">25</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">workspace_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="kt">float</span><span class="o">*</span><span class="p">)</span><span class="mh">0x10070000</span><span class="p">;</span>
<span class="linenos">26</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">buffer_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2048</span><span class="p">;</span><span class="w"> </span><span class="c1">// 私有存储版本中，必须设置该参数，用于确定分块计算的大小</span>
<span class="hll"><span class="linenos">27</span><span class="w"> </span><span class="n">fp_conv2d_p</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span><span class="w"> </span><span class="n">weight</span><span class="p">,</span><span class="w"> </span><span class="n">output_data</span><span class="p">,</span><span class="w"> </span><span class="n">bias_data</span><span class="p">,</span><span class="w"> </span><span class="n">param</span><span class="p">,</span><span class="w"> </span><span class="n">core_mask</span><span class="p">);</span>
</span><span class="linenos">28</span><span class="p">}</span>
<span class="linenos">29</span>
<span class="linenos">30</span><span class="kt">void</span><span class="w"> </span><span class="nf">main</span><span class="p">(){</span>
<span class="linenos">31</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">in_channel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">32</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">out_channel</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">33</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">groups</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span>
<span class="linenos">34</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">30</span><span class="p">,</span><span class="w"> </span><span class="mi">30</span><span class="p">,</span><span class="w"> </span><span class="n">in_channel</span><span class="p">};</span><span class="w"> </span><span class="c1">// NHWC</span>
<span class="linenos">35</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">out_channel</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="n">in_channel</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">groups</span><span class="p">};</span>
<span class="linenos">36</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">output_shape</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">,</span><span class="w"> </span><span class="n">out_channel</span><span class="p">};</span><span class="w"> </span><span class="c1">// NHWC</span>
<span class="linenos">37</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">stride</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
<span class="linenos">38</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">padding</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">};</span>
<span class="linenos">39</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">dilation</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">};</span>
<span class="linenos">40</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="n">bias</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">};</span>
<span class="linenos">41</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">core_mask</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mb">0b0001</span><span class="p">;</span><span class="w"> </span><span class="c1">// 私有存储版本只能设置为一个核心启动</span>
<span class="linenos">42</span><span class="w"> </span><span class="n">TestConvL2Fp32</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span><span class="w"> </span><span class="n">weight_shape</span><span class="p">,</span><span class="w"> </span><span class="n">output_shape</span><span class="p">,</span><span class="w"> </span><span class="n">stride</span><span class="p">,</span><span class="w"> </span><span class="n">padding</span><span class="p">,</span><span class="w"> </span><span class="n">dilation</span><span class="p">,</span><span class="w"> </span><span class="n">groups</span><span class="p">,</span><span class="w"> </span><span class="n">bias</span><span class="p">,</span><span class="w"> </span><span class="n">core_mask</span><span class="p">);</span>
<span class="linenos">43</span><span class="p">}</span>
</pre></div>
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