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<!DOCTYPE html>
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<h1>Source code for torch.nn.modules.pixelshuffle</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">.module</span> <span class="k">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<div class="viewcode-block" id="PixelShuffle"><a class="viewcode-back" href="../../../../nn.html#torch.nn.PixelShuffle">[docs]</a><span class="k">class</span> <span class="nc">PixelShuffle</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
<span class="sd">r"""Rearranges elements in a Tensor of shape :math:`(*, C * r^2, H, W]` to a</span>
<span class="sd"> tensor of shape :math:`(C, H * r, W * r)`.</span>
<span class="sd"> This is useful for implementing efficient sub-pixel convolution</span>
<span class="sd"> with a stride of :math:`1/r`.</span>
<span class="sd"> Look at the paper:</span>
<span class="sd"> `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_</span>
<span class="sd"> by Shi et. al (2016) for more details</span>
<span class="sd"> Args:</span>
<span class="sd"> upscale_factor (int): factor to increase spatial resolution by</span>
<span class="sd"> Shape:</span>
<span class="sd"> - Input: :math:`(N, C * {upscale\_factor}^2, H, W)`</span>
<span class="sd"> - Output: :math:`(N, C, H * {upscale\_factor}, W * {upscale\_factor})`</span>
<span class="sd"> Examples::</span>
<span class="sd"> >>> ps = nn.PixelShuffle(3)</span>
<span class="sd"> >>> input = autograd.Variable(torch.Tensor(1, 9, 4, 4))</span>
<span class="sd"> >>> output = ps(input)</span>
<span class="sd"> >>> print(output.size())</span>
<span class="sd"> torch.Size([1, 1, 12, 12])</span>
<span class="sd"> .. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:</span>
<span class="sd"> https://arxiv.org/abs/1609.05158</span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">upscale_factor</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">PixelShuffle</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">upscale_factor</span> <span class="o">=</span> <span class="n">upscale_factor</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">pixel_shuffle</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upscale_factor</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span> <span class="o">+</span> <span class="s1">' (upscale_factor='</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upscale_factor</span><span class="p">)</span> <span class="o">+</span> <span class="s1">')'</span></div>
</pre></div>
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