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MindSpore Signal+ 使用手册
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<li class="breadcrumb-item active">Shape</li>
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<a href="../../_sources/functionlib/dsplib/shape.rst.txt" rel="nofollow"> 查看页面源码</a>
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<div itemprop="articleBody">
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<section id="shape">
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<h1>Shape<a class="headerlink" href="#shape" title="此标题的永久链接"></a></h1>
|
||
<p>形状推断函数（Shape Inference Function）。该函数根据输入张量的形状和算子参数，推断输出张量的形状。该函数不区分数据类型，只处理张量的形状信息。</p>
|
||
<p>如果所有输出张量都是常量（ConstTensor 或 ConstScalar），则直接返回，不进行形状推断。否则，根据算子类型调用相应的形状推断函数。</p>
|
||
<dl class="simple">
|
||
<dt>支持的算子类型：</dt><dd><ul class="simple">
|
||
<li><p><strong>Arithmetic_InferShape</strong> - 算术运算的形状推断</p></li>
|
||
<li><p><strong>Common_InferShape</strong> - 通用算子的形状推断</p></li>
|
||
<li><p><strong>Softmax_InferShape</strong> - Softmax 算子的形状推断</p></li>
|
||
<li><p><strong>MaxMinGrad_InferShape</strong> - MaxMin 梯度算子的形状推断</p></li>
|
||
<li><p><strong>Dropout_InferShape</strong> - Dropout 算子的形状推断</p></li>
|
||
<li><p><strong>DynamicQuant_InferShape</strong> - 动态量化算子的形状推断</p></li>
|
||
<li><p><strong>Fft_InferShape</strong> - FFT 算子的形状推断</p></li>
|
||
<li><p><strong>Flatten_InferShape</strong> - Flatten 算子的形状推断</p></li>
|
||
<li><p><strong>LayerNorm_InferShape</strong> - LayerNorm 算子的形状推断</p></li>
|
||
<li><p><strong>LogSoftmax_InferShape</strong> - LogSoftmax 算子的形状推断</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt>输入：</dt><dd><ul class="simple">
|
||
<li><p><strong>inputs</strong> - 输入张量数组（TensorC** 类型）。</p></li>
|
||
<li><p><strong>inputs_size</strong> - 输入张量的数量。</p></li>
|
||
<li><p><strong>outputs</strong> - 输出张量数组（TensorC** 类型）。</p></li>
|
||
<li><p><strong>outputs_size</strong> - 输出张量的数量。</p></li>
|
||
<li><p><strong>param</strong> - 算子参数（OpParameter* 类型），包含算子类型和其他参数信息。</p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt>输出：</dt><dd><ul class="simple">
|
||
<li><p><strong>outputs</strong> - 输出张量数组，其中的形状信息会被更新。</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>该函数不区分数据类型，适用于所有数据类型</p></li>
|
||
<li><p>函数会自动检查输出是否为常量，如果是常量则跳过形状推断</p></li>
|
||
</ul>
|
||
</div>
|
||
<p><strong>共享存储/私有存储版本:</strong></p>
|
||
<dl class="c function">
|
||
<dt class="sig sig-object c" id="c.shape">
|
||
<span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">shape</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">TensorC</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">inputs</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">inputs_size</span></span>, <span class="n"><span class="pre">TensorC</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">outputs</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n"><span class="pre">outputs_size</span></span>, <span class="n"><span class="pre">OpParameter</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n"><span class="pre">param</span></span><span class="sig-paren">)</span><a class="headerlink" href="#c.shape" title="永久链接至目标"></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="c1">//FT78NE示例</span>
|
||
<span class="linenos"> 2</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><stdio.h></span>
|
||
<span class="linenos"> 3</span><span class="cp">#include</span><span class="w"> </span><span class="cpf"><shape.h></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">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
|
||
<span class="linenos"> 6</span><span class="w"> </span><span class="n">TensorC</span><span class="o">**</span><span class="w"> </span><span class="n">input_tensors_ptrs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">TensorC</span><span class="o">**</span><span class="p">)</span><span class="mh">0x10010000</span><span class="p">;</span>
|
||
<span class="linenos"> 7</span><span class="w"> </span><span class="n">TensorC</span><span class="o">**</span><span class="w"> </span><span class="n">output_tensors_ptrs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">(</span><span class="n">TensorC</span><span class="o">**</span><span class="p">)</span><span class="mh">0x10011000</span><span class="p">;</span>
|
||
<span class="linenos"> 8</span>
|
||
<span class="linenos"> 9</span><span class="w"> </span><span class="n">TensorC</span><span class="w"> </span><span class="n">input0</span><span class="p">;</span>
|
||
<span class="linenos">10</span><span class="w"> </span><span class="n">TensorC</span><span class="w"> </span><span class="n">input1</span><span class="p">;</span>
|
||
<span class="linenos">11</span><span class="w"> </span><span class="n">TensorC</span><span class="w"> </span><span class="n">output</span><span class="p">;</span>
|
||
<span class="linenos">12</span>
|
||
<span class="linenos">13</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input0_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="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">};</span>
|
||
<span class="linenos">14</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">input1_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="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">};</span>
|
||
<span class="linenos">15</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="c1">//不用初始化</span>
|
||
<span class="linenos">16</span><span class="w"> </span><span class="n">memcpy</span><span class="p">(</span><span class="n">input0</span><span class="p">.</span><span class="n">shape_</span><span class="p">,</span><span class="w"> </span><span class="n">input0_shape</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="kt">int</span><span class="p">));</span>
|
||
<span class="linenos">17</span><span class="w"> </span><span class="n">input0</span><span class="p">.</span><span class="n">shape_size_</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">18</span><span class="w"> </span><span class="n">memcpy</span><span class="p">(</span><span class="n">input1</span><span class="p">.</span><span class="n">shape_</span><span class="p">,</span><span class="w"> </span><span class="n">input1_shape</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="k">sizeof</span><span class="p">(</span><span class="kt">int</span><span class="p">));</span>
|
||
<span class="linenos">19</span><span class="w"> </span><span class="n">input1</span><span class="p">.</span><span class="n">shape_size_</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">input0</span><span class="p">.</span><span class="n">data_type_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kNumberTypeFloat32</span><span class="p">;</span>
|
||
<span class="linenos">21</span><span class="w"> </span><span class="n">input1</span><span class="p">.</span><span class="n">data_type_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">kNumberTypeFloat32</span><span class="p">;</span>
|
||
<span class="linenos">22</span><span class="w"> </span><span class="n">input0</span><span class="p">.</span><span class="n">format_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Format_NCHW</span><span class="p">;</span>
|
||
<span class="linenos">23</span><span class="w"> </span><span class="n">input1</span><span class="p">.</span><span class="n">format_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Format_NCHW</span><span class="p">;</span>
|
||
<span class="linenos">24</span>
|
||
<span class="linenos">25</span><span class="w"> </span><span class="n">input_tensors_ptrs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">&</span><span class="n">input0</span><span class="p">;</span>
|
||
<span class="linenos">26</span><span class="w"> </span><span class="n">input_tensors_ptrs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">&</span><span class="n">input1</span><span class="p">;</span>
|
||
<span class="linenos">27</span><span class="w"> </span><span class="n">output_tensors_ptrs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">&</span><span class="n">output</span><span class="p">;</span>
|
||
<span class="linenos">28</span>
|
||
<span class="linenos">29</span><span class="w"> </span><span class="n">ArithmeticParameter</span><span class="w"> </span><span class="n">param</span><span class="p">;</span>
|
||
<span class="linenos">30</span><span class="w"> </span><span class="n">param</span><span class="p">.</span><span class="n">op_parameter_</span><span class="p">.</span><span class="n">type_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Arithmetic_InferShape</span><span class="p">;</span>
|
||
<span class="linenos">31</span>
|
||
<span class="hll"><span class="linenos">32</span><span class="w"> </span><span class="n">shape</span><span class="p">(</span><span class="n">input_tensors_ptrs</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="n">output_tensors_ptrs</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="p">(</span><span class="n">OpParameter</span><span class="o">*</span><span class="p">)</span><span class="o">&</span><span class="n">param</span><span class="p">);</span>
|
||
</span><span class="linenos">33</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">34</span><span class="p">}</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
|
||
|
||
</div>
|
||
</div>
|
||
|
||
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