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<section id="conv2dbackpropinputfusion">
<h1>Conv2DBackpropInputFusion<a class="headerlink" href="#conv2dbackpropinputfusion" title="Link to this heading"></a></h1>
<p>计算二维卷积反向传播的输入梯度（Conv2D backprop input fusion），支持普通卷积、Depthwise 卷积以及 1x1 优化路径，多个核心通过核掩码协同完成批次并行。</p>
<blockquote>
<div><div class="math notranslate nohighlight">
\[dx = \text{Conv2D}^\top(dy, w)\]</div>
<dl>
<dt>输入：</dt><dd><ul class="simple">
<li><p><strong>dy</strong> - 输出梯度张量首地址，形状 <code class="docutils literal notranslate"><span class="pre">[batch,</span> <span class="pre">out_h,</span> <span class="pre">out_w,</span> <span class="pre">out_channel]</span></code>。</p></li>
<li><p><strong>w</strong> - 卷积权重张量首地址，形状 <code class="docutils literal notranslate"><span class="pre">[out_channel,</span> <span class="pre">kernel_h,</span> <span class="pre">kernel_w,</span> <span class="pre">in_channel/group]</span></code>。</p></li>
<li><p><strong>conv_param</strong> - 卷积参数结构体地址，包含 <code class="docutils literal notranslate"><span class="pre">stride</span></code>、<code class="docutils literal notranslate"><span class="pre">pad</span></code>、<code class="docutils literal notranslate"><span class="pre">dilation</span></code>、<code class="docutils literal notranslate"><span class="pre">group</span></code>、输入输出维度、批次数及共享工作空间指针等信息。</p></li>
</ul>
<p>ConvParameter 字段说明：</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">workspace_</span></code> - 指向算子运行时使用的临时工作空间，需满足对齐与容量要求。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">output_batch_</span></code> - 输出梯度 <code class="docutils literal notranslate"><span class="pre">dy</span></code> 的批次数（通常等于输入批次数）。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">input_batch_</span></code> - 正向输入 <code class="docutils literal notranslate"><span class="pre">x</span></code> 的批次数，用于与 <code class="docutils literal notranslate"><span class="pre">output_batch_</span></code> 校验。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">input_h_</span></code> / <code class="docutils literal notranslate"><span class="pre">input_w_</span></code> - 正向输入特征图的高度与宽度。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">output_h_</span></code> / <code class="docutils literal notranslate"><span class="pre">output_w_</span></code> - 输出梯度特征图的高度与宽度。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">input_channel_</span></code> / <code class="docutils literal notranslate"><span class="pre">output_channel_</span></code> - 输入与输出通道数，需与 <code class="docutils literal notranslate"><span class="pre">group_</span></code> 配合满足整除关系。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">kernel_h_</span></code> / <code class="docutils literal notranslate"><span class="pre">kernel_w_</span></code> - 卷积核的高与宽。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">group_</span></code> - 组卷积数量，<code class="docutils literal notranslate"><span class="pre">group_</span> <span class="pre">=</span> <span class="pre">1</span></code> 表示普通卷积。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">pad_l_</span></code> / <code class="docutils literal notranslate"><span class="pre">pad_r_</span></code> / <code class="docutils literal notranslate"><span class="pre">pad_u_</span></code> / <code class="docutils literal notranslate"><span class="pre">pad_d_</span></code> - 分别表示左右上下方向的填充大小。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">dilation_h_</span></code> / <code class="docutils literal notranslate"><span class="pre">dilation_w_</span></code> - 核心采样间隔（膨胀系数）。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">stride_h_</span></code> / <code class="docutils literal notranslate"><span class="pre">stride_w_</span></code> - 滑动窗口在高、宽方向的步长。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">buffer_size_</span></code> - 分配给 <code class="docutils literal notranslate"><span class="pre">workspace_</span></code> 的缓冲区字节数，在运行前需要正确设置。</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">nweights_</span></code> - 卷积权重 <code class="docutils literal notranslate"><span class="pre">w</span></code> 的元素总数，用于内部分块和校验。</p></li>
<li><p><strong>core_mask(int, 可选)</strong> - 核掩码（仅适用于共享存储版本）。</p></li>
</ul>
</dd>
<dt>输出：</dt><dd><ul class="simple">
<li><p><strong>dx</strong> - 输入梯度张量首地址，形状 <code class="docutils literal notranslate"><span class="pre">[batch,</span> <span class="pre">in_h,</span> <span class="pre">in_w,</span> <span class="pre">in_channel]</span></code>。</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 fp32 数据类型。</p></li>
<li><p>MT7004 支持 fp16、fp32 数据类型。</p></li>
<li><p>需在 <code class="docutils literal notranslate"><span class="pre">conv_param-&gt;workspace_</span></code> 中预先分配共享工作空间，并设置 <code class="docutils literal notranslate"><span class="pre">conv_param-&gt;buffer_size_</span></code>。</p></li>
</ul>
</div>
</div></blockquote>
<p><strong>共享存储版本:</strong></p>
<dl class="c function">
<dt class="sig sig-object c" id="c.hp_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_s</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </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">dy</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </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">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">dx</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_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_s</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </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">dy</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </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">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">dx</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_conv2dbackpropinputfusion_s" title="Link to this definition"></a><br /></dt>
<dd><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="c1">// FT78NE 多核示例</span>
<span class="linenos"> 2</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;stdio.h&gt;</span>
<span class="linenos"> 3</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;conv_parameter.h&quot;</span>
<span class="linenos"> 4</span>
<span class="linenos"> 5</span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="linenos"> 6</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">dy</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xA0000000</span><span class="p">;</span><span class="w"> </span><span class="c1">// DDR 存储</span>
<span class="linenos"> 7</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">w</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xB0000000</span><span class="p">;</span>
<span class="linenos"> 8</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="n">dx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">float</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xC0000000</span><span class="p">;</span>
<span class="linenos"> 9</span><span class="w"> </span><span class="n">ConvParameter</span><span class="w"> </span><span class="o">*</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="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xB0001000</span><span class="p">;</span><span class="w"> </span><span class="c1">// 卷积参数共享区域</span>
<span class="linenos">10</span><span class="w"> </span><span class="c1">// 设置 ConvParameter 字段</span>
<span class="linenos">11</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">void</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0xB0002000</span><span class="p">;</span><span class="w"> </span><span class="c1">// 共享工作空间</span>
<span class="linenos">12</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="mh">0x20000</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_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </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">input_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</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_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</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_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">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="mi">1</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="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">output_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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="mi">8</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">kernel_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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">kernel_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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">group_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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_u_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">pad_d_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">pad_l_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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_r_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">dilation_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">dilation_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
<span class="linenos">30</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="mi">2</span><span class="p">;</span>
<span class="linenos">31</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="mi">2</span><span class="p">;</span>
<span class="linenos">32</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">nweights_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">8</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="c1">// 示例值</span>
<span class="linenos">33</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="mh">0xff</span><span class="p">;</span>
<span class="hll"><span class="linenos">34</span><span class="w"> </span><span class="n">fp_conv2dbackpropinputfusion_s</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span><span class="w"> </span><span class="n">w</span><span class="p">,</span><span class="w"> </span><span class="n">dx</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">35</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="linenos">36</span><span class="p">}</span>
</pre></div>
</div>
</dd></dl>
<p><strong>私有存储版本:</strong></p>
<dl class="c function">
<dt class="sig sig-object c" id="c.hp_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_p</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </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">dy</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </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">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">dx</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="sig-paren">)</span><a class="headerlink" href="#c.hp_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_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_conv2dbackpropinputfusion_p</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </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">dy</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </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">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">dx</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="sig-paren">)</span><a class="headerlink" href="#c.fp_conv2dbackpropinputfusion_p" title="Link to this definition"></a><br /></dt>
<dd><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="c1">// MT7004 单核示例</span>
<span class="linenos"> 2</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;stdio.h&gt;</span>
<span class="linenos"> 3</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;conv_parameter.h&quot;</span>
<span class="linenos"> 4</span>
<span class="linenos"> 5</span><span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">void</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="linenos"> 6</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">half</span><span class="w"> </span><span class="o">*</span><span class="n">dy</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">half</span><span class="w"> </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">// L2 存储</span>
<span class="linenos"> 7</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">half</span><span class="w"> </span><span class="o">*</span><span class="n">w</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">half</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10020000</span><span class="p">;</span>
<span class="linenos"> 8</span><span class="w"> </span><span class="n">half</span><span class="w"> </span><span class="o">*</span><span class="n">dx</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">half</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10040000</span><span class="p">;</span>
<span class="linenos"> 9</span><span class="w"> </span><span class="n">ConvParameter</span><span class="w"> </span><span class="o">*</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="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10060000</span><span class="p">;</span>
<span class="linenos">10</span><span class="w"> </span><span class="c1">// 设置 ConvParameter 字段</span>
<span class="linenos">11</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">void</span><span class="w"> </span><span class="o">*</span><span class="p">)</span><span class="mh">0x10070000</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">buffer_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mh">0x10000</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_batch_</span><span class="w"> </span><span class="o">=</span><span class="w"> </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">input_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</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_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">5</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_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">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="mi">1</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="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">output_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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="mi">8</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">kernel_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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">kernel_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</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">group_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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_u_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">pad_d_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">pad_l_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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_r_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">dilation_h_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</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">dilation_w_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
<span class="linenos">30</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="mi">2</span><span class="p">;</span>
<span class="linenos">31</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="mi">2</span><span class="p">;</span>
<span class="linenos">32</span><span class="w"> </span><span class="n">param</span><span class="o">-&gt;</span><span class="n">nweights_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">8</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"> </span><span class="c1">// 示例值</span>
<span class="hll"><span class="linenos">33</span><span class="w"> </span><span class="n">hp_conv2dbackpropinputfusion_p</span><span class="p">(</span><span class="n">dy</span><span class="p">,</span><span class="w"> </span><span class="n">w</span><span class="p">,</span><span class="w"> </span><span class="n">dx</span><span class="p">,</span><span class="w"> </span><span class="n">param</span><span class="p">);</span>
</span><span class="linenos">34</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
<span class="linenos">35</span><span class="p">}</span>
</pre></div>
</div>
</dd></dl>
</section>
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