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<h1><a href="https://nn.labml.ai/gan/cycle_gan/index.html">循环增益</a></h1>
<p>这是论文《<a href="https://arxiv.org/abs/1703.10593">使用周期一致的对抗网络进行未配对的图像到图像转换》的 Py</a> <a href="https://pytorch.org">Torch</a> 实现/教程。</p>
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<h1>深度卷积生成对抗网络 (DCGAN)</h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a> 实现的论文《<a href="https://arxiv.org/abs/1511.06434">使用深度卷积生成对抗网络进行无监督表示学习</a>》。</p>
<p>此实现基于 <a href="https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html">PyTorch DCGAN 教程</a>。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">15</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">16</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">calculate</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_nn.gan.original.experiment</span> <span class="kn">import</span> <span class="n">Configs</span></pre></div>
</div>
</div>
<div class='section' id='section-1'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-1'>#</a>
</div>
<h3>卷积生成器网络</h3>
<p>这类似于用于 CeleBA 人脸的反卷积网络，但针对 MNIST 图像进行了修改。</p>
<p><img alt="DCGan Architecture" src="https://pytorch.org/tutorials/_images/dcgan_generator.png"></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">23</span><span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-2'>
<div class='docs'>
<div class='section-link'>
<a href='#section-2'>#</a>
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</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">33</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">34</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
</div>
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<p>输入<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqc" style=""><span class="mord" style="">1</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">1</span></span></span></span></span></span>有 100 个通道</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span></pre></div>
</div>
</div>
<div class='section' id='section-4'>
<div class='docs'>
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<a href='#section-4'>#</a>
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<p>这给出了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqd" style=""><span class="mord" style="">3</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">3</span></span></span></span></span></span>输出</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">38</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">39</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">1024</span><span class="p">),</span>
<span class="lineno">40</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">),</span></pre></div>
</div>
</div>
<div class='section' id='section-5'>
<div class='docs'>
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<a href='#section-5'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqe" style=""><span class="mord" style="">7</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">7</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">42</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">43</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">512</span><span class="p">),</span>
<span class="lineno">44</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">),</span></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">14</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">14</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">47</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
<span class="lineno">48</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">),</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqb" style=""><span class="mord" style="">28</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">28</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">50</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">51</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">()</span>
<span class="lineno">52</span> <span class="p">)</span>
<span class="lineno">53</span>
<span class="lineno">54</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_weights_init</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">56</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="n">x</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p>从形状改<code class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</span></code>
为<code class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">58</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="lineno">59</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">60</span> <span class="k">return</span> <span class="n">x</span></pre></div>
</div>
</div>
<div class='section' id='section-10'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-10'>#</a>
</div>
<h3>卷积鉴别器网络</h3>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">63</span><span class="k">class</span> <span class="nc">Discriminator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs'>
<div class='section-link'>
<a href='#section-11'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">68</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">69</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
<div class='docs'>
<div class='section-link'>
<a href='#section-12'>#</a>
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<p>输入<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqb" style=""><span class="mord" style="">28</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">28</span></span></span></span></span></span>使用一个通道</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">71</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span></pre></div>
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<a href='#section-13'>#</a>
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<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqa" style=""><span class="mord" style="">14</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">14</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">73</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">74</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span></pre></div>
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<a href='#section-14'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqe" style=""><span class="mord" style="">7</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">7</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">76</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">77</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">512</span><span class="p">),</span>
<span class="lineno">78</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span></pre></div>
</div>
</div>
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<div class='docs'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqd" style=""><span class="mord" style="">3</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">3</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">80</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">81</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">1024</span><span class="p">),</span>
<span class="lineno">82</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span></pre></div>
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</div>
<div class='section' id='section-16'>
<div class='docs'>
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<a href='#section-16'>#</a>
</div>
<p>这给了<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.72777em;vertical-align:-0.08333em;"></span><span class="mord coloredeq eqc" style=""><span class="mord" style="">1</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin" style="">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mord" style="">1</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">84</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1024</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">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
<span class="lineno">85</span> <span class="p">)</span>
<span class="lineno">86</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_weights_init</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">88</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="n">x</span><span class="p">):</span>
<span class="lineno">89</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="lineno">90</span> <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">93</span><span class="k">def</span> <span class="nf">_weights_init</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="lineno">94</span> <span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="lineno">95</span> <span class="k">if</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Conv&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="lineno">96</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="lineno">97</span> <span class="k">elif</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;BatchNorm&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="lineno">98</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="lineno">99</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<p>我们导入了<a href="../original/experiment.html">简单的 gan 实验</a>并更改了生成器和鉴别器网络</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">104</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Generator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
<span class="lineno">105</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator</span><span class="p">,</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Discriminator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span></pre></div>
</div>
</div>
<div class='section' id='section-20'>
<div class='docs'>
<div class='section-link'>
<a href='#section-20'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">108</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="lineno">109</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span>
<span class="lineno">110</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_dcgan&#39;</span><span class="p">)</span>
<span class="lineno">111</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">112</span> <span class="p">{</span><span class="s1">&#39;discriminator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">113</span> <span class="s1">&#39;generator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">114</span> <span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">})</span>
<span class="lineno">115</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">116</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">117</span>
<span class="lineno">118</span>
<span class="lineno">119</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">120</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1>使用 MNIST 进行生成对抗网络实验</h1>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">10</span><span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
<span class="lineno">11</span>
<span class="lineno">12</span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">13</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">14</span><span class="kn">import</span> <span class="nn">torch.utils.data</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>
<span class="lineno">16</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">tracker</span><span class="p">,</span> <span class="n">monit</span><span class="p">,</span> <span class="n">experiment</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span><span class="p">,</span> <span class="n">calculate</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml_helpers.datasets.mnist</span> <span class="kn">import</span> <span class="n">MNISTConfigs</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_helpers.device</span> <span class="kn">import</span> <span class="n">DeviceConfigs</span>
<span class="lineno">21</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">22</span><span class="kn">from</span> <span class="nn">labml_helpers.optimizer</span> <span class="kn">import</span> <span class="n">OptimizerConfigs</span>
<span class="lineno">23</span><span class="kn">from</span> <span class="nn">labml_helpers.train_valid</span> <span class="kn">import</span> <span class="n">TrainValidConfigs</span><span class="p">,</span> <span class="n">hook_model_outputs</span><span class="p">,</span> <span class="n">BatchIndex</span>
<span class="lineno">24</span><span class="kn">from</span> <span class="nn">labml_nn.gan.original</span> <span class="kn">import</span> <span class="n">DiscriminatorLogitsLoss</span><span class="p">,</span> <span class="n">GeneratorLogitsLoss</span></pre></div>
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<div class='section-link'>
<a href='#section-1'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">27</span><span class="k">def</span> <span class="nf">weights_init</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="lineno">28</span> <span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="lineno">29</span> <span class="k">if</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Linear&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="lineno">30</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="lineno">31</span> <span class="k">elif</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;BatchNorm&#39;</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="lineno">32</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">)</span>
<span class="lineno">33</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-2'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-2'>#</a>
</div>
<h3>简单的 MLP 生成器</h3>
<p>它有三个线性层，随着<code class="highlight"><span></span><span class="n">LeakyReLU</span></code>
激活的大小不断增加。最后一层已激<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">t</span><span class="mord mathnormal">anh</span></span></span></span></span>活。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">36</span><span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-3'>
<div class='docs'>
<div class='section-link'>
<a href='#section-3'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">44</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">45</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="lineno">46</span> <span class="n">layer_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1024</span><span class="p">]</span>
<span class="lineno">47</span> <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="lineno">48</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="mi">100</span>
<span class="lineno">49</span> <span class="k">for</span> <span class="n">size</span> <span class="ow">in</span> <span class="n">layer_sizes</span><span class="p">:</span>
<span class="lineno">50</span> <span class="n">layers</span> <span class="o">=</span> <span class="n">layers</span> <span class="o">+</span> <span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)]</span>
<span class="lineno">51</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="n">size</span>
<span class="lineno">52</span>
<span class="lineno">53</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">())</span>
<span class="lineno">54</span>
<span class="lineno">55</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init</span><span class="p">)</span></pre></div>
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</div>
<div class='section' id='section-4'>
<div class='docs'>
<div class='section-link'>
<a href='#section-4'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">57</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="n">x</span><span class="p">):</span>
<span class="lineno">58</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-5'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<h3>简单的 MLP 鉴别器</h3>
<p>它有三个线性层，随着<code class="highlight"><span></span><span class="n">LeakyReLU</span></code>
激活的大小逐渐减小。最后一层有一个单独的输出，它给出了输入是真实还是假的 logit。你可以通过计算它的乙状结肠来获得概率。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">61</span><span class="k">class</span> <span class="nc">Discriminator</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">70</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">71</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="lineno">72</span> <span class="n">layer_sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">256</span><span class="p">]</span>
<span class="lineno">73</span> <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
<span class="lineno">74</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span>
<span class="lineno">75</span> <span class="k">for</span> <span class="n">size</span> <span class="ow">in</span> <span class="n">layer_sizes</span><span class="p">:</span>
<span class="lineno">76</span> <span class="n">layers</span> <span class="o">=</span> <span class="n">layers</span> <span class="o">+</span> <span class="p">[</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="mf">0.2</span><span class="p">)]</span>
<span class="lineno">77</span> <span class="n">d_prev</span> <span class="o">=</span> <span class="n">size</span>
<span class="lineno">78</span>
<span class="lineno">79</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_prev</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="lineno">80</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">weights_init</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">82</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="n">x</span><span class="p">):</span>
<span class="lineno">83</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<h2>配置</h2>
<p>这扩展了 MNIST 配置以获取数据加载器以及训练和验证循环配置，从而简化了我们的实现。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">86</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">MNISTConfigs</span><span class="p">,</span> <span class="n">TrainValidConfigs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">94</span> <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">DeviceConfigs</span><span class="p">()</span>
<span class="lineno">95</span> <span class="n">dataset_transforms</span> <span class="o">=</span> <span class="s1">&#39;mnist_gan_transforms&#39;</span>
<span class="lineno">96</span> <span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span>
<span class="lineno">97</span>
<span class="lineno">98</span> <span class="n">is_save_models</span> <span class="o">=</span> <span class="kc">True</span>
<span class="lineno">99</span> <span class="n">discriminator</span><span class="p">:</span> <span class="n">Module</span> <span class="o">=</span> <span class="s1">&#39;mlp&#39;</span>
<span class="lineno">100</span> <span class="n">generator</span><span class="p">:</span> <span class="n">Module</span> <span class="o">=</span> <span class="s1">&#39;mlp&#39;</span>
<span class="lineno">101</span> <span class="n">generator_optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span>
<span class="lineno">102</span> <span class="n">discriminator_optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span>
<span class="lineno">103</span> <span class="n">generator_loss</span><span class="p">:</span> <span class="n">GeneratorLogitsLoss</span> <span class="o">=</span> <span class="s1">&#39;original&#39;</span>
<span class="lineno">104</span> <span class="n">discriminator_loss</span><span class="p">:</span> <span class="n">DiscriminatorLogitsLoss</span> <span class="o">=</span> <span class="s1">&#39;original&#39;</span>
<span class="lineno">105</span> <span class="n">label_smoothing</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span>
<span class="lineno">106</span> <span class="n">discriminator_k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span></pre></div>
</div>
</div>
<div class='section' id='section-10'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-10'>#</a>
</div>
<p>初始化</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">108</span> <span class="k">def</span> <span class="nf">init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs'>
<div class='section-link'>
<a href='#section-11'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">112</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_modules</span> <span class="o">=</span> <span class="p">[]</span>
<span class="lineno">113</span>
<span class="lineno">114</span> <span class="n">hook_model_outputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span> <span class="s1">&#39;generator&#39;</span><span class="p">)</span>
<span class="lineno">115</span> <span class="n">hook_model_outputs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">,</span> <span class="s1">&#39;discriminator&#39;</span><span class="p">)</span>
<span class="lineno">116</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_scalar</span><span class="p">(</span><span class="s2">&quot;loss.generator.*&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="lineno">117</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_scalar</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.*&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
<span class="lineno">118</span> <span class="n">tracker</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="s2">&quot;generated&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="mi">1</span> <span class="o">/</span> <span class="mi">100</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-12'>#</a>
</div>
<p><span ><span class="katex-display"><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∼</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mclose">)</span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">120</span> <span class="k">def</span> <span class="nf">sample_z</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-13'>
<div class='docs'>
<div class='section-link'>
<a href='#section-13'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">124</span> <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-14'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-14'>#</a>
</div>
<p>迈出训练一步</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">126</span> <span class="k">def</span> <span class="nf">step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">:</span> <span class="n">BatchIndex</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-15'>
<div class='docs'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p>设置模型状态</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">132</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">)</span>
<span class="lineno">133</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>获取 MNIST 图片</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">136</span> <span class="n">data</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>在训练模式中增加步数</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">139</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
<span class="lineno">140</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add_global_step</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p>训练鉴别器</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">143</span> <span class="k">with</span> <span class="n">monit</span><span class="o">.</span><span class="n">section</span><span class="p">(</span><span class="s2">&quot;discriminator&quot;</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<p>获得鉴别器损失</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">145</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_discriminator_loss</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-20'>
<div class='docs'>
<div class='section-link'>
<a href='#section-20'>#</a>
</div>
<p>火车</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">148</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
<span class="lineno">149</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="lineno">150</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="lineno">151</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_last</span><span class="p">:</span>
<span class="lineno">152</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;discriminator&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">)</span>
<span class="lineno">153</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-21'>
<div class='docs'>
<div class='section-link'>
<a href='#section-21'>#</a>
</div>
<p>每隔一次训练发电机<code class="highlight"><span></span><span class="n">discriminator_k</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">156</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_interval</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">discriminator_k</span><span class="p">):</span>
<span class="lineno">157</span> <span class="k">with</span> <span class="n">monit</span><span class="o">.</span><span class="n">section</span><span class="p">(</span><span class="s2">&quot;generator&quot;</span><span class="p">):</span>
<span class="lineno">158</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calc_generator_loss</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></pre></div>
</div>
</div>
<div class='section' id='section-22'>
<div class='docs'>
<div class='section-link'>
<a href='#section-22'>#</a>
</div>
<p>火车</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">161</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
<span class="lineno">162</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
<span class="lineno">163</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="lineno">164</span> <span class="k">if</span> <span class="n">batch_idx</span><span class="o">.</span><span class="n">is_last</span><span class="p">:</span>
<span class="lineno">165</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;generator&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">)</span>
<span class="lineno">166</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
<span class="lineno">167</span>
<span class="lineno">168</span> <span class="n">tracker</span><span class="o">.</span><span class="n">save</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-23'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-23'>#</a>
</div>
<p>计算鉴别器损失</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">170</span> <span class="k">def</span> <span class="nf">calc_discriminator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-24'>
<div class='docs'>
<div class='section-link'>
<a href='#section-24'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">174</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="lineno">175</span> <span class="n">logits_true</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="lineno">176</span> <span class="n">logits_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span>
<span class="lineno">177</span> <span class="n">loss_true</span><span class="p">,</span> <span class="n">loss_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">(</span><span class="n">logits_true</span><span class="p">,</span> <span class="n">logits_false</span><span class="p">)</span>
<span class="lineno">178</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span></pre></div>
</div>
</div>
<div class='section' id='section-25'>
<div class='docs'>
<div class='section-link'>
<a href='#section-25'>#</a>
</div>
<p>日志的东西</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">181</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.true.&quot;</span><span class="p">,</span> <span class="n">loss_true</span><span class="p">)</span>
<span class="lineno">182</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.false.&quot;</span><span class="p">,</span> <span class="n">loss_false</span><span class="p">)</span>
<span class="lineno">183</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
<span class="lineno">184</span>
<span class="lineno">185</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
</div>
</div>
<div class='section' id='section-26'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-26'>#</a>
</div>
<p>计算发电机损耗</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">187</span> <span class="k">def</span> <span class="nf">calc_generator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-27'>
<div class='docs'>
<div class='section-link'>
<a href='#section-27'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">191</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
<span class="lineno">192</span> <span class="n">generated_images</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span>
<span class="lineno">193</span> <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">generated_images</span><span class="p">)</span>
<span class="lineno">194</span> <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-28'>
<div class='docs'>
<div class='section-link'>
<a href='#section-28'>#</a>
</div>
<p>日志的东西</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">197</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;generated&#39;</span><span class="p">,</span> <span class="n">generated_images</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">6</span><span class="p">])</span>
<span class="lineno">198</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.generator.&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
<span class="lineno">199</span>
<span class="lineno">200</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
</div>
</div>
<div class='section' id='section-29'>
<div class='docs'>
<div class='section-link'>
<a href='#section-29'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">205</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">dataset_transforms</span><span class="p">)</span>
<span class="lineno">206</span><span class="k">def</span> <span class="nf">mnist_gan_transforms</span><span class="p">():</span>
<span class="lineno">207</span> <span class="k">return</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="lineno">208</span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="lineno">209</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))</span>
<span class="lineno">210</span> <span class="p">])</span>
<span class="lineno">211</span>
<span class="lineno">212</span>
<span class="lineno">213</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_optimizer</span><span class="p">)</span>
<span class="lineno">214</span><span class="k">def</span> <span class="nf">_discriminator_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span>
<span class="lineno">215</span> <span class="n">opt_conf</span> <span class="o">=</span> <span class="n">OptimizerConfigs</span><span class="p">()</span>
<span class="lineno">216</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="s1">&#39;Adam&#39;</span>
<span class="lineno">217</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">discriminator</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span>
<span class="lineno">218</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">2.5e-4</span></pre></div>
</div>
</div>
<div class='section' id='section-30'>
<div class='docs'>
<div class='section-link'>
<a href='#section-30'>#</a>
</div>
<p>设置梯度第一时刻的指数衰减率<code class="highlight"><span></span><span class="mf">0.5</span></code>
非常重要。<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqb" style=""><span class="mord" style=""><span class="mord mathnormal" style="margin-right:0.05278em">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.05278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style="">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span></span>默认为<code class="highlight"><span></span><span class="mf">0.9</span></code>
失败。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">222</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">betas</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">)</span>
<span class="lineno">223</span> <span class="k">return</span> <span class="n">opt_conf</span></pre></div>
</div>
</div>
<div class='section' id='section-31'>
<div class='docs'>
<div class='section-link'>
<a href='#section-31'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">226</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_optimizer</span><span class="p">)</span>
<span class="lineno">227</span><span class="k">def</span> <span class="nf">_generator_optimizer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span>
<span class="lineno">228</span> <span class="n">opt_conf</span> <span class="o">=</span> <span class="n">OptimizerConfigs</span><span class="p">()</span>
<span class="lineno">229</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="s1">&#39;Adam&#39;</span>
<span class="lineno">230</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">generator</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span>
<span class="lineno">231</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">2.5e-4</span></pre></div>
</div>
</div>
<div class='section' id='section-32'>
<div class='docs'>
<div class='section-link'>
<a href='#section-32'>#</a>
</div>
<p>设置梯度第一时刻的指数衰减率<code class="highlight"><span></span><span class="mf">0.5</span></code>
非常重要。<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqb" style=""><span class="mord" style=""><span class="mord mathnormal" style="margin-right:0.05278em">β</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.30110799999999993em;"><span style="top:-2.5500000000000003em;margin-left:-0.05278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight" style=""><span class="mord mtight" style="">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span></span>默认为<code class="highlight"><span></span><span class="mf">0.9</span></code>
失败。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">235</span> <span class="n">opt_conf</span><span class="o">.</span><span class="n">betas</span> <span class="o">=</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.999</span><span class="p">)</span>
<span class="lineno">236</span> <span class="k">return</span> <span class="n">opt_conf</span>
<span class="lineno">237</span>
<span class="lineno">238</span>
<span class="lineno">239</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator</span><span class="p">,</span> <span class="s1">&#39;mlp&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Generator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
<span class="lineno">240</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator</span><span class="p">,</span> <span class="s1">&#39;mlp&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">Discriminator</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
<span class="lineno">241</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">,</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">GeneratorLogitsLoss</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">label_smoothing</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
<span class="lineno">242</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">,</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">DiscriminatorLogitsLoss</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">label_smoothing</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">))</span></pre></div>
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<div class='section' id='section-33'>
<div class='docs'>
<div class='section-link'>
<a href='#section-33'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">245</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="lineno">246</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span>
<span class="lineno">247</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_gan&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span>
<span class="lineno">248</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">249</span> <span class="p">{</span><span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">})</span>
<span class="lineno">250</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">251</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">252</span>
<span class="lineno">253</span>
<span class="lineno">254</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">255</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1>WGAN 使用 MNIST 进行实验</h1>
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<div class="highlight"><pre><span class="lineno">9</span><span></span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">10</span>
<span class="lineno">11</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">calculate</span></pre></div>
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<p>从 <a href="../dcgan/index.html">DCGAN 实验</a>导入配置</p>
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<div class="highlight"><pre><span class="lineno">13</span><span class="kn">from</span> <span class="nn">labml_nn.gan.dcgan</span> <span class="kn">import</span> <span class="n">Configs</span></pre></div>
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<p>进口 <a href="./index.html">Wasserstein GAN 亏损</a></p>
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<div class="highlight"><pre><span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein</span> <span class="kn">import</span> <span class="n">GeneratorLoss</span><span class="p">,</span> <span class="n">DiscriminatorLoss</span></pre></div>
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<p>为 Wasserstein GAN 损耗设置配置选项</p>
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<div class="highlight"><pre><span class="lineno">19</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">generator_loss</span><span class="p">,</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">GeneratorLoss</span><span class="p">())</span>
<span class="lineno">20</span><span class="n">calculate</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">,</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">DiscriminatorLoss</span><span class="p">())</span></pre></div>
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<div class="highlight"><pre><span class="lineno">23</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<p>创建配置对象</p>
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<div class="highlight"><pre><span class="lineno">25</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
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<p>创建实验</p>
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<div class="highlight"><pre><span class="lineno">27</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_wassertein_dcgan&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">)</span></pre></div>
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<p>覆盖配置</p>
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<div class="highlight"><pre><span class="lineno">29</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">30</span> <span class="p">{</span>
<span class="lineno">31</span> <span class="s1">&#39;discriminator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">32</span> <span class="s1">&#39;generator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">33</span> <span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="lineno">34</span> <span class="s1">&#39;generator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">35</span> <span class="s1">&#39;discriminator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">36</span> <span class="p">})</span></pre></div>
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<p>开始实验并运行训练循环</p>
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<div class="highlight"><pre><span class="lineno">39</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">40</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">41</span>
<span class="lineno">42</span>
<span class="lineno">43</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">44</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1>WGAN-GP 使用 MNIST 进行实验</h1>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">10</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">11</span>
<span class="lineno">12</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">tracker</span></pre></div>
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<p>从 <a href="../experiment.html">Wasserstein 实验</a>导入配置</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein.experiment</span> <span class="kn">import</span> <span class="n">Configs</span> <span class="k">as</span> <span class="n">OriginalConfigs</span></pre></div>
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<a href='#section-2'>#</a>
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<div class="highlight"><pre><span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.gan.wasserstein.gradient_penalty</span> <span class="kn">import</span> <span class="n">GradientPenalty</span></pre></div>
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<a href='#section-3'>#</a>
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<h2>配置类</h2>
<p>我们扩展了<a href="../../original/experiment.html">最初的 GAN 实现</a>，并覆盖了鉴别器（批评者）损失计算，以包括梯度惩罚。</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">19</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">OriginalConfigs</span><span class="p">):</span></pre></div>
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<p>梯度惩罚系数<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathnormal">λ</span></span></span></span></span></p>
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<div class="highlight"><pre><span class="lineno">28</span> <span class="n">gradient_penalty_coefficient</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10.0</span></pre></div>
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</div>
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<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<p></p>
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<div class="highlight"><pre><span class="lineno">30</span> <span class="n">gradient_penalty</span> <span class="o">=</span> <span class="n">GradientPenalty</span><span class="p">()</span></pre></div>
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</div>
<div class='section' id='section-6'>
<div class='docs doc-strings'>
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<a href='#section-6'>#</a>
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<p>这会覆盖最初的鉴别器损耗计算，并包括梯度惩罚。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">32</span> <span class="k">def</span> <span class="nf">calc_discriminator_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
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</div>
<div class='section' id='section-7'>
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<a href='#section-7'>#</a>
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<p>需要开启梯度<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord coloredeq eqe" style=""><span class="mord mathnormal" style="">x</span></span></span></span></span></span>才能计算梯度损失</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">38</span> <span class="n">data</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">()</span></pre></div>
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</div>
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<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>样本<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">∼</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mclose">)</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">40</span> <span class="n">latent</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_z</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mopen">(</span><span class="mord coloredeq eqe" style=""><span class="mord mathnormal" style="">x</span></span><span class="mclose">)</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">42</span> <span class="n">f_real</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></pre></div>
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<a href='#section-10'>#</a>
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<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">G</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.33610799999999996em;"><span style="top:-2.5500000000000003em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.02778em;">θ</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.04398em;">z</span><span class="mclose">))</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">44</span> <span class="n">f_fake</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">generator</span><span class="p">(</span><span class="n">latent</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">())</span></pre></div>
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<div class='section-link'>
<a href='#section-11'>#</a>
</div>
<p>获得鉴别器损失</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="n">loss_true</span><span class="p">,</span> <span class="n">loss_false</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">discriminator_loss</span><span class="p">(</span><span class="n">f_real</span><span class="p">,</span> <span class="n">f_fake</span><span class="p">)</span></pre></div>
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</div>
<div class='section' id='section-12'>
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<div class='section-link'>
<a href='#section-12'>#</a>
</div>
<p>计算训练模式中的梯度惩罚</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="o">.</span><span class="n">is_train</span><span class="p">:</span>
<span class="lineno">49</span> <span class="n">gradient_penalty</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_penalty</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">f_real</span><span class="p">)</span>
<span class="lineno">50</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.gp.&quot;</span><span class="p">,</span> <span class="n">gradient_penalty</span><span class="p">)</span>
<span class="lineno">51</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">gradient_penalty_coefficient</span> <span class="o">*</span> <span class="n">gradient_penalty</span></pre></div>
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<div class='section' id='section-13'>
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<div class='section-link'>
<a href='#section-13'>#</a>
</div>
<p>否则跳过梯度惩罚</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">53</span> <span class="k">else</span><span class="p">:</span>
<span class="lineno">54</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_true</span> <span class="o">+</span> <span class="n">loss_false</span></pre></div>
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<div class='section' id='section-14'>
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<div class='section-link'>
<a href='#section-14'>#</a>
</div>
<p>日志的东西</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">57</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.true.&quot;</span><span class="p">,</span> <span class="n">loss_true</span><span class="p">)</span>
<span class="lineno">58</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.false.&quot;</span><span class="p">,</span> <span class="n">loss_false</span><span class="p">)</span>
<span class="lineno">59</span> <span class="n">tracker</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s2">&quot;loss.discriminator.&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
<span class="lineno">60</span>
<span class="lineno">61</span> <span class="k">return</span> <span class="n">loss</span></pre></div>
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<div class='section-link'>
<a href='#section-15'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">64</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>创建配置对象</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">66</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">68</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_wassertein_gp_dcgan&#39;</span><span class="p">)</span></pre></div>
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<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p>覆盖配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">70</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span>
<span class="lineno">71</span> <span class="p">{</span>
<span class="lineno">72</span> <span class="s1">&#39;discriminator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">73</span> <span class="s1">&#39;generator&#39;</span><span class="p">:</span> <span class="s1">&#39;cnn&#39;</span><span class="p">,</span>
<span class="lineno">74</span> <span class="s1">&#39;label_smoothing&#39;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="lineno">75</span> <span class="s1">&#39;generator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">76</span> <span class="s1">&#39;discriminator_loss&#39;</span><span class="p">:</span> <span class="s1">&#39;wasserstein&#39;</span><span class="p">,</span>
<span class="lineno">77</span> <span class="s1">&#39;discriminator_k&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
<span class="lineno">78</span> <span class="p">})</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">81</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">82</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="lineno">83</span>
<span class="lineno">84</span>
<span class="lineno">85</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">86</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<p>这是 <a href="https://arxiv.org/abs/1704.00028">Wasserstein GAN 改进训练的</a>实现。</p>
<p><a href="https://nn.labml.ai/gan/wasserstein/index.html">WGAN</a> 建议削减权重以对鉴别器网络强制执行 Lipschitz 限制（评论家）。这个和其他权重约束，如L2标准削减、权重标准化、L1、L2权重衰减都有问题：</p>
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