chore: import upstream snapshot with attribution
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<h1>Train a <a href="index.html">ResNet</a> on CIFAR 10</h1>
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<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">List</span><span class="p">,</span> <span class="n">Optional</span>
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<span class="lineno">11</span>
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<span class="lineno">12</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
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<span class="lineno">13</span>
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<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
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<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
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<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span>
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<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.resnet</span> <span class="kn">import</span> <span class="n">ResNetBase</span></pre></div>
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<a href='#section-1'>#</a>
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<h2>Configurations</h2>
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<p>We use <a href="../experiments/cifar10.html"><code class="highlight"><span></span><span class="n">CIFAR10Configs</span></code>
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</a> which defines all the dataset related configurations, optimizer, and a training loop.</p>
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<div class="highlight"><pre><span class="lineno">20</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<a href='#section-2'>#</a>
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<p>Number fo blocks for each feature map size </p>
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<div class="highlight"><pre><span class="lineno">29</span> <span class="n">n_blocks</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span></pre></div>
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<p>Number of channels for each feature map size </p>
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<div class="highlight"><pre><span class="lineno">31</span> <span class="n">n_channels</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">]</span></pre></div>
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<a href='#section-4'>#</a>
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<p>Bottleneck sizes </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">33</span> <span class="n">bottlenecks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span></pre></div>
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<a href='#section-5'>#</a>
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<p>Kernel size of the initial convolution layer </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">35</span> <span class="n">first_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span></pre></div>
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<div class='docs doc-strings'>
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<a href='#section-6'>#</a>
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<h3>Create model</h3>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">38</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
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<span class="lineno">39</span><span class="k">def</span> <span class="nf">_resnet</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-7'>#</a>
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<p><a href="index.html">ResNet</a> </p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">44</span> <span class="n">base</span> <span class="o">=</span> <span class="n">ResNetBase</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">n_blocks</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">bottlenecks</span><span class="p">,</span> <span class="n">img_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">first_kernel_size</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">first_kernel_size</span><span class="p">)</span></pre></div>
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<div class='section' id='section-8'>
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<a href='#section-8'>#</a>
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<p>Linear layer for classification </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">46</span> <span class="n">classification</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">n_channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">10</span><span class="p">)</span></pre></div>
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<div class='section' id='section-9'>
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<div class='section-link'>
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<a href='#section-9'>#</a>
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<p>Stack them </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">49</span> <span class="n">model</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="n">base</span><span class="p">,</span> <span class="n">classification</span><span class="p">)</span></pre></div>
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<a href='#section-10'>#</a>
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<p>Move the model to the device </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">51</span> <span class="k">return</span> <span class="n">model</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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<a href='#section-11'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">54</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<p>Create experiment </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">56</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">'resnet'</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'cifar10'</span><span class="p">)</span></pre></div>
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<a href='#section-13'>#</a>
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<p>Create configurations </p>
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<div class="highlight"><pre><span class="lineno">58</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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<a href='#section-14'>#</a>
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<p>Load configurations </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">60</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="p">{</span>
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<span class="lineno">61</span> <span class="s1">'bottlenecks'</span><span class="p">:</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">],</span>
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<span class="lineno">62</span> <span class="s1">'n_blocks'</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span>
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<span class="lineno">63</span>
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<span class="lineno">64</span> <span class="s1">'optimizer.optimizer'</span><span class="p">:</span> <span class="s1">'Adam'</span><span class="p">,</span>
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<span class="lineno">65</span> <span class="s1">'optimizer.learning_rate'</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
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<span class="lineno">66</span>
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<span class="lineno">67</span> <span class="s1">'epochs'</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span>
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<span class="lineno">68</span> <span class="s1">'train_batch_size'</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span>
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<span class="lineno">69</span>
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<span class="lineno">70</span> <span class="s1">'train_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_train_augmented'</span><span class="p">,</span>
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<span class="lineno">71</span> <span class="s1">'valid_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_valid_no_augment'</span><span class="p">,</span>
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<span class="lineno">72</span> <span class="p">})</span></pre></div>
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<div class='section-link'>
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<a href='#section-15'>#</a>
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<p>Set model for saving/loading </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">74</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
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<div class='section-link'>
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<a href='#section-16'>#</a>
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<p>Start the experiment and run the training loop </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">76</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
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<span class="lineno">77</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<a href='#section-17'>#</a>
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<p> </p>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">81</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
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<span class="lineno">82</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1><a href="https://nn.labml.ai/resnet/index.html">Deep Residual Learning for Image Recognition (ResNet)</a></h1>
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<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper <a href="https://arxiv.org/abs/1512.03385">Deep Residual Learning for Image Recognition</a>.</p>
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<p>ResNets train layers as residual functions to overcome the <em>degradation problem</em>. The degradation problem is the accuracy of deep neural networks degrading when the number of layers becomes very high. The accuracy increases as the number of layers increase, then saturates, and then starts to degrade.</p>
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