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Varuna Jayasiri 5e56ba1964 update docs
2021-10-29 09:32:09 +05:30

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<p>
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<a class="parent" href="index.html">distillation</a>
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<h1>Train a large model on CIFAR 10</h1>
<p>This trains a large model on CIFAR 10 for <a href="index.html">distillation</a>.</p>
<p><a href="https://app.labml.ai/run/d46cd53edaec11eb93c38d6538aee7d6"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen"></a></p>
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<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="p">,</span> <span class="n">logger</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="lineno">19</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span><span class="p">,</span> <span class="n">CIFAR10VGGModel</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.batch_norm</span> <span class="kn">import</span> <span class="n">BatchNorm</span></pre></div>
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<h2>Configurations</h2>
<p>We use <a href="../experiments/cifar10.html"><code class="highlight"><span></span><span class="n">CIFAR10Configs</span></code>
</a> which defines all the dataset related configurations, optimizer, and a training loop.</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">23</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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<div class='code'>
<div class="highlight"><pre><span class="lineno">30</span> <span class="k">pass</span></pre></div>
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<h3>VGG style model for CIFAR-10 classification</h3>
<p>This derives from the <a href="../experiments/cifar10.html">generic VGG style architecture</a>.</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">33</span><span class="k">class</span> <span class="nc">LargeModel</span><span class="p">(</span><span class="n">CIFAR10VGGModel</span><span class="p">):</span></pre></div>
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<p> Create a convolution layer and the activations</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">40</span> <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span></pre></div>
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<div class="highlight"><pre><span class="lineno">44</span> <span class="k">return</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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<p>Dropout </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.1</span><span class="p">),</span></pre></div>
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<p>Convolution layer </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span></pre></div>
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<p>Batch normalization </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">50</span> <span class="n">BatchNorm</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">track_running_stats</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span></pre></div>
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<p>ReLU activation </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">52</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="lineno">53</span> <span class="p">)</span></pre></div>
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<a href='#section-10'>#</a>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">55</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
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<p>Create a model with given convolution sizes (channels) </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">57</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="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">]])</span></pre></div>
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<h3>Create model</h3>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">60</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>
<span class="lineno">61</span><span class="k">def</span> <span class="nf">_large_model</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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<a href='#section-13'>#</a>
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<div class="highlight"><pre><span class="lineno">65</span> <span class="k">return</span> <span class="n">LargeModel</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='code'>
<div class="highlight"><pre><span class="lineno">68</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'>
<div class="highlight"><pre><span class="lineno">70</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;cifar10&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;large model&#39;</span><span class="p">)</span></pre></div>
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<p>Create configurations </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">72</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>Load configurations </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">74</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>
<span class="lineno">75</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">76</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
<span class="lineno">77</span> <span class="s1">&#39;is_save_models&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
<span class="lineno">78</span> <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="mi">20</span><span class="p">,</span>
<span class="lineno">79</span> <span class="p">})</span></pre></div>
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<p>Set model for saving/loading </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">81</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">&#39;model&#39;</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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<p>Print number of parameters in the model </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">83</span> <span class="n">logger</span><span class="o">.</span><span class="n">inspect</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)))</span></pre></div>
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<p>Start the experiment and run the training loop </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">85</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">86</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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<p> </p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">90</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">91</span> <span class="n">main</span><span class="p">()</span></pre></div>
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