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计算CNN模型所需的计算量（FLOPs）和参数量（parameters） - 咕噜灵波
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<h1 class="entry-title">计算CNN模型所需的计算量（FLOPs）和参数量（parameters）</h1>
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<h2 id="toc_0">参数量：</h2>
<p>（以下均考虑bias）<br/>
普通卷积层：<br/>
params = (C_in * k * k + 1) * C_out</p>
<p>Depthwise卷积层：<br/>
params = C_in * (k * k * 1 + 1) + C_out * (1 * 1 * C_in + 1)</p>
<p><em>解释：<br/>
第一部分：对输入的每个通道使用单通道卷积核进行卷积；<br/>
第二部分：使用1x1卷积将输入通道数转变成输出通道数：</em></p>
<p>全连接层：<br/>
params = (I + 1) * O</p>
<h2 id="toc_1">计算量：</h2>
<p>通常需要先求出输出特征图的尺寸H_out和W_out<br/>
H_out = (H_in - k + 2*padding) / stride + 1</p>
<p>普通卷积层：<br/>
FLOPs =（2 * C_in * k * k) * H_out * W_out * C_out</p>
<p><em>解释：对位相乘计算k<sup>2</sup> 次，加法计算k<sup>2</sup> -1次，bias每个输出位置计算1次</em></p>
<p>全连接层：<br/>
FLOPs = （2 * I -1） * O + O = （2 * I) * O</p>
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