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<li class="toctree-l3"><a class="reference internal" href="#mindspore.ops.FFTWithSize"><code class="docutils literal notranslate"><span class="pre">mindspore.ops.FFTWithSize</span></code></a></li>
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<section id="fftwithsize">
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<h1>FFTWithSize<a class="headerlink" href="#fftwithsize" title="Link to this heading"></a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mindspore.ops.FFTWithSize">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mindspore.ops.</span></span><span class="sig-name descname"><span class="pre">FFTWithSize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">signal_ndim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inverse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">real</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'backward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#mindspore.ops.FFTWithSize" title="Link to this definition"></a></dt>
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<dd><p>傅里叶变换，可以对参数进行调整，以实现FFT/IFFT/RFFT/IRFFT。</p>
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<p>对于FFT，它计算以下表达式：</p>
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<div class="math notranslate nohighlight">
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\[X[\omega_1, \dots, \omega_d] =
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\sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
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e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},\]</div>
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<p>其中， <span class="math notranslate nohighlight">\(d\)</span> = <cite>signal_ndim</cite> 是信号的维度，<span class="math notranslate nohighlight">\(N_i\)</span> 则是信号第 <span class="math notranslate nohighlight">\(i\)</span> 个维度的大小。</p>
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<p>对于IFFT，它计算以下表达式：</p>
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<div class="math notranslate nohighlight">
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\[X[\omega_1, \dots, \omega_d] =
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\frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
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e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},\]</div>
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<p>其中， <span class="math notranslate nohighlight">\(d\)</span> = <cite>signal_ndim</cite> 是信号的维度，<span class="math notranslate nohighlight">\(N_i\)</span> 则是信号第 <span class="math notranslate nohighlight">\(i\)</span> 维的大小。</p>
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<div class="admonition note">
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<p class="admonition-title">备注</p>
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<ul class="simple">
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<li><p>FFT/IFFT要求complex64或complex128类型的输入，返回complex64或complex128类型的输出。</p></li>
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<li><p>RFFT要求bool, uint8, int8, int16, int32, int64, float32或float64类型的输入，
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返回complex64或complex128类型的输出。</p></li>
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<li><p>IRFFT要求complex64或complex128类型的输入，返回float32或float64类型的输出。</p></li>
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</ul>
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</div>
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<div class="admonition warning">
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<p class="admonition-title">警告</p>
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<p>这是一个实验性API，后续可能修改或删除。</p>
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</div>
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<dl class="simple">
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<dt>参数：</dt><dd><ul class="simple">
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<li><p><strong>signal_ndim</strong> (int) - 表示每个信号中的维数，控制着傅里叶变换的维数，其值只能为1、2或3。</p></li>
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<li><p><strong>inverse</strong> (bool) - 表示该操作是否为逆变换，用以选择FFT 和 RFFT 或 IFFT 和 IRFFT。</p>
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<ul>
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<li><p>如果为 <code class="docutils literal notranslate"><span class="pre">True</span></code> ，则为IFFT 和 IRFFT。</p></li>
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<li><p>如果为 <code class="docutils literal notranslate"><span class="pre">False</span></code> ，FFT 和 RFFT。</p></li>
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</ul>
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</li>
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<li><p><strong>real</strong> (bool) - 表示该操作是否为实变换，与 <cite>inverse</cite> 共同决定具体的变换模式：</p>
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<ul>
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<li><p><cite>inverse</cite> 为 <code class="docutils literal notranslate"><span class="pre">False</span></code> ， <cite>real</cite> 为 <code class="docutils literal notranslate"><span class="pre">False</span></code> ：对应FFT模式。</p></li>
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<li><p><cite>inverse</cite> 为 <code class="docutils literal notranslate"><span class="pre">True</span></code> ， <cite>real</cite> 为 <code class="docutils literal notranslate"><span class="pre">False</span></code> ：对应IFFT模式。</p></li>
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<li><p><cite>inverse</cite> 为 <code class="docutils literal notranslate"><span class="pre">False</span></code> ， <cite>real</cite> 为 <code class="docutils literal notranslate"><span class="pre">True</span></code> ：对应RFFT模式。</p></li>
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<li><p><cite>inverse</cite> 为 <code class="docutils literal notranslate"><span class="pre">True</span></code> ， <cite>real</cite> 为 <code class="docutils literal notranslate"><span class="pre">True</span></code> ：对应IRFFT模式。</p></li>
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</ul>
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</li>
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<li><p><strong>norm</strong> (str，可选) - 表示该操作的规范化方式，可选值：[ <code class="docutils literal notranslate"><span class="pre">"backward"</span></code> , <code class="docutils literal notranslate"><span class="pre">"forward"</span></code> , <code class="docutils literal notranslate"><span class="pre">"ortho"</span></code> ]。默认值： <code class="docutils literal notranslate"><span class="pre">"backward"</span></code> 。</p>
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<ul>
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<li><p>"backward"，正向变换不缩放，逆变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，其中 <cite>n</cite> 表示输入 <cite>x</cite> 的元素数量。。</p></li>
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<li><p>"ortho"，正向变换与逆变换均按 <span class="math notranslate nohighlight">\(1/\sqrt n\)</span> 缩放。</p></li>
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<li><p>"forward"，正向变换按 <span class="math notranslate nohighlight">\(1/n\)</span> 缩放，逆变换不缩放。</p></li>
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</ul>
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</li>
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<li><p><strong>onesided</strong> (bool，可选) - 控制输入是否减半以避免冗余。默认值： <code class="docutils literal notranslate"><span class="pre">True</span></code> 。</p></li>
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<li><p><strong>signal_sizes</strong> (tuple，可选) - 原始信号的大小（RFFT变换之前的信号，不包含batch这一维），只有在IRFFT模式下和设置 <cite>onesided</cite> 为True时需要该参数，需要满足
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以下条件。默认值： <code class="docutils literal notranslate"><span class="pre">()</span></code> 。</p>
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<ul>
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<li><p><cite>signal_sizes</cite> 的长度等于IRFFT的 <cite>signal_ndim</cite> ： <span class="math notranslate nohighlight">\(len(signal\_sizes)=signal\_ndim\)</span> 。</p></li>
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<li><p><cite>signal_sizes</cite> 的最后一个维度除以2等于IRFFT输入的最后一个维度： <span class="math notranslate nohighlight">\(signal\_size[-1]/2+1=x.shape[-1]\)</span> 。</p></li>
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<li><p>除了最后一个维度外， <cite>signal_sizes</cite> 的维度与输入shape完全相同： <span class="math notranslate nohighlight">\(signal\_sizes[:-1]=x.shape[:-1]\)</span> 。</p></li>
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</ul>
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</li>
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</ul>
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</dd>
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<dt>输入：</dt><dd><ul class="simple">
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<li><p><strong>x</strong> (Tensor) - 输入Tensor的维数必须大于或等于 <cite>signal_ndim</cite> 。</p></li>
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</ul>
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</dd>
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<dt>输出：</dt><dd><p>Tensor，表示复数到复数、实数到复数或复数到实数傅里叶变换的结果。</p>
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</dd>
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<dt>异常：</dt><dd><ul class="simple">
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<li><p><strong>TypeError</strong> - 如果FFT/IFFT/IRFF的输入类型不是以下类型之一：complex64、complex128。</p></li>
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<li><p><strong>TypeError</strong> - 如果输入的类型不是Tensor。</p></li>
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<li><p><strong>ValueError</strong> - 如果输入 <cite>x</cite> 的维度小于 <cite>signal_ndim</cite> 。</p></li>
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<li><p><strong>ValueError</strong> - 如果 <cite>signal_ndim</cite> 大于3或小于1。</p></li>
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<li><p><strong>ValueError</strong> - 如果 <cite>norm</cite> 取值不是"backward"、"forward"或"ortho"。</p></li>
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</ul>
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</dd>
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</dl>
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