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<h1 id="training-101">Training 101<a class="headerlink" href="#training-101" title="Permanent link">¶</a></h1>
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<p>This guide will walk you through training a model using Refiners. We built the <code>training_utils</code> module to provide a simple, flexible, statically type-safe interface.</p>
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|
<p>We will use a simple model and a toy dataset for demonstration purposes. The model will be a simple <a href="https://en.wikipedia.org/wiki/Autoencoder">autoencoder</a>, and the dataset will be a synthetic dataset of rectangles
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of different sizes.</p>
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<h2 id="pre-requisites">Pre-requisites<a class="headerlink" href="#pre-requisites" title="Permanent link">¶</a></h2>
|
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<p>We recommend installing Refiners targeting a specific commit hash to avoid unexpected changes in the API. You also
|
|
get the benefit of having a perfectly reproducible environment.</p>
|
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<ul>
|
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<li>
|
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<p>with rye (recommended):
|
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<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>rye<span class="w"> </span>add<span class="w"> </span>refiners<span class="o">[</span>training<span class="o">]</span><span class="w"> </span>--git<span class="o">=</span>https://github.com/finegrain-ai/refiners.git<span class="w"> </span>--branch<span class="o">=</span><insert-latest-commit-hash>
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</span></code></pre></div></p>
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</li>
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<li>
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<p>with pip:
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<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span><span class="s2">"git+https://github.com/finegrain-ai/refiners.git@<insert-latest-commit-hash>#egg=refiners[training]"</span>
|
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</span></code></pre></div></p>
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</li>
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</ul>
|
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<h2 id="model">Model<a class="headerlink" href="#model" title="Permanent link">¶</a></h2>
|
|
<p>Let's start by building our autoencoder using Refiners.</p>
|
|
<details class="autoencoder">
|
|
<summary>Expand to see the autoencoder model.</summary>
|
|
<div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">refiners.fluxion</span> <span class="kn">import</span> <span class="n">layers</span> <span class="k">as</span> <span class="n">fl</span>
|
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</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
|
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</span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
|
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</span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a> <span class="k">class</span> <span class="nc">ConvBlock</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
|
|
</span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a> <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="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-2-6"><a id="__codelineno-2-6" name="__codelineno-2-6" href="#__codelineno-2-6"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
|
</span><span id="__span-2-7"><a id="__codelineno-2-7" name="__codelineno-2-7" href="#__codelineno-2-7"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
|
|
</span><span id="__span-2-8"><a id="__codelineno-2-8" name="__codelineno-2-8" href="#__codelineno-2-8"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
|
|
</span><span id="__span-2-9"><a id="__codelineno-2-9" name="__codelineno-2-9" href="#__codelineno-2-9"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
|
|
</span><span id="__span-2-10"><a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
|
|
</span><span id="__span-2-11"><a id="__codelineno-2-11" name="__codelineno-2-11" href="#__codelineno-2-11"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
|
</span><span id="__span-2-12"><a id="__codelineno-2-12" name="__codelineno-2-12" href="#__codelineno-2-12"></a> <span class="n">groups</span><span class="o">=</span><span class="nb">min</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>
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|
</span><span id="__span-2-13"><a id="__codelineno-2-13" name="__codelineno-2-13" href="#__codelineno-2-13"></a> <span class="p">),</span>
|
|
</span><span id="__span-2-14"><a id="__codelineno-2-14" name="__codelineno-2-14" href="#__codelineno-2-14"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
|
|
</span><span id="__span-2-15"><a id="__codelineno-2-15" name="__codelineno-2-15" href="#__codelineno-2-15"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
|
</span><span id="__span-2-16"><a id="__codelineno-2-16" name="__codelineno-2-16" href="#__codelineno-2-16"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
|
|
</span><span id="__span-2-17"><a id="__codelineno-2-17" name="__codelineno-2-17" href="#__codelineno-2-17"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
|
|
</span><span id="__span-2-18"><a id="__codelineno-2-18" name="__codelineno-2-18" href="#__codelineno-2-18"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
|
|
</span><span id="__span-2-19"><a id="__codelineno-2-19" name="__codelineno-2-19" href="#__codelineno-2-19"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
|
</span><span id="__span-2-20"><a id="__codelineno-2-20" name="__codelineno-2-20" href="#__codelineno-2-20"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
|
</span><span id="__span-2-21"><a id="__codelineno-2-21" name="__codelineno-2-21" href="#__codelineno-2-21"></a> <span class="p">),</span>
|
|
</span><span id="__span-2-22"><a id="__codelineno-2-22" name="__codelineno-2-22" href="#__codelineno-2-22"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
|
|
</span><span id="__span-2-23"><a id="__codelineno-2-23" name="__codelineno-2-23" href="#__codelineno-2-23"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
|
</span><span id="__span-2-24"><a id="__codelineno-2-24" name="__codelineno-2-24" href="#__codelineno-2-24"></a> <span class="p">)</span>
|
|
</span><span id="__span-2-25"><a id="__codelineno-2-25" name="__codelineno-2-25" href="#__codelineno-2-25"></a>
|
|
</span><span id="__span-2-26"><a id="__codelineno-2-26" name="__codelineno-2-26" href="#__codelineno-2-26"></a>
|
|
</span><span id="__span-2-27"><a id="__codelineno-2-27" name="__codelineno-2-27" href="#__codelineno-2-27"></a><span class="k">class</span> <span class="nc">ResidualBlock</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Sum</span><span class="p">):</span>
|
|
</span><span id="__span-2-28"><a id="__codelineno-2-28" name="__codelineno-2-28" href="#__codelineno-2-28"></a> <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="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-2-29"><a id="__codelineno-2-29" name="__codelineno-2-29" href="#__codelineno-2-29"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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</span><span id="__span-2-30"><a id="__codelineno-2-30" name="__codelineno-2-30" href="#__codelineno-2-30"></a> <span class="n">ConvBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">),</span>
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</span><span id="__span-2-31"><a id="__codelineno-2-31" name="__codelineno-2-31" href="#__codelineno-2-31"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
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</span><span id="__span-2-32"><a id="__codelineno-2-32" name="__codelineno-2-32" href="#__codelineno-2-32"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
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</span><span id="__span-2-33"><a id="__codelineno-2-33" name="__codelineno-2-33" href="#__codelineno-2-33"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
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</span><span id="__span-2-34"><a id="__codelineno-2-34" name="__codelineno-2-34" href="#__codelineno-2-34"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
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</span><span id="__span-2-35"><a id="__codelineno-2-35" name="__codelineno-2-35" href="#__codelineno-2-35"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
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</span><span id="__span-2-36"><a id="__codelineno-2-36" name="__codelineno-2-36" href="#__codelineno-2-36"></a> <span class="p">),</span>
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</span><span id="__span-2-37"><a id="__codelineno-2-37" name="__codelineno-2-37" href="#__codelineno-2-37"></a> <span class="p">)</span>
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</span><span id="__span-2-38"><a id="__codelineno-2-38" name="__codelineno-2-38" href="#__codelineno-2-38"></a>
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</span><span id="__span-2-39"><a id="__codelineno-2-39" name="__codelineno-2-39" href="#__codelineno-2-39"></a>
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</span><span id="__span-2-40"><a id="__codelineno-2-40" name="__codelineno-2-40" href="#__codelineno-2-40"></a><span class="k">class</span> <span class="nc">Encoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-2-41"><a id="__codelineno-2-41" name="__codelineno-2-41" href="#__codelineno-2-41"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-2-42"><a id="__codelineno-2-42" name="__codelineno-2-42" href="#__codelineno-2-42"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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</span><span id="__span-2-43"><a id="__codelineno-2-43" name="__codelineno-2-43" href="#__codelineno-2-43"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
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</span><span id="__span-2-44"><a id="__codelineno-2-44" name="__codelineno-2-44" href="#__codelineno-2-44"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
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</span><span id="__span-2-45"><a id="__codelineno-2-45" name="__codelineno-2-45" href="#__codelineno-2-45"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
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</span><span id="__span-2-46"><a id="__codelineno-2-46" name="__codelineno-2-46" href="#__codelineno-2-46"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
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</span><span id="__span-2-47"><a id="__codelineno-2-47" name="__codelineno-2-47" href="#__codelineno-2-47"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
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</span><span id="__span-2-48"><a id="__codelineno-2-48" name="__codelineno-2-48" href="#__codelineno-2-48"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
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</span><span id="__span-2-49"><a id="__codelineno-2-49" name="__codelineno-2-49" href="#__codelineno-2-49"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="mi">2048</span><span class="p">),</span>
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</span><span id="__span-2-50"><a id="__codelineno-2-50" name="__codelineno-2-50" href="#__codelineno-2-50"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
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</span><span id="__span-2-51"><a id="__codelineno-2-51" name="__codelineno-2-51" href="#__codelineno-2-51"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
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</span><span id="__span-2-52"><a id="__codelineno-2-52" name="__codelineno-2-52" href="#__codelineno-2-52"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
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</span><span id="__span-2-53"><a id="__codelineno-2-53" name="__codelineno-2-53" href="#__codelineno-2-53"></a> <span class="p">)</span>
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</span><span id="__span-2-54"><a id="__codelineno-2-54" name="__codelineno-2-54" href="#__codelineno-2-54"></a>
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</span><span id="__span-2-55"><a id="__codelineno-2-55" name="__codelineno-2-55" href="#__codelineno-2-55"></a>
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</span><span id="__span-2-56"><a id="__codelineno-2-56" name="__codelineno-2-56" href="#__codelineno-2-56"></a><span class="k">class</span> <span class="nc">Decoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-2-57"><a id="__codelineno-2-57" name="__codelineno-2-57" href="#__codelineno-2-57"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-2-58"><a id="__codelineno-2-58" name="__codelineno-2-58" href="#__codelineno-2-58"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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</span><span id="__span-2-59"><a id="__codelineno-2-59" name="__codelineno-2-59" href="#__codelineno-2-59"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
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</span><span id="__span-2-60"><a id="__codelineno-2-60" name="__codelineno-2-60" href="#__codelineno-2-60"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
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</span><span id="__span-2-61"><a id="__codelineno-2-61" name="__codelineno-2-61" href="#__codelineno-2-61"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">2048</span><span class="p">),</span>
|
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</span><span id="__span-2-62"><a id="__codelineno-2-62" name="__codelineno-2-62" href="#__codelineno-2-62"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-2-63"><a id="__codelineno-2-63" name="__codelineno-2-63" href="#__codelineno-2-63"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
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</span><span id="__span-2-64"><a id="__codelineno-2-64" name="__codelineno-2-64" href="#__codelineno-2-64"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
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</span><span id="__span-2-65"><a id="__codelineno-2-65" name="__codelineno-2-65" href="#__codelineno-2-65"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
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</span><span id="__span-2-66"><a id="__codelineno-2-66" name="__codelineno-2-66" href="#__codelineno-2-66"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
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</span><span id="__span-2-67"><a id="__codelineno-2-67" name="__codelineno-2-67" href="#__codelineno-2-67"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
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</span><span id="__span-2-68"><a id="__codelineno-2-68" name="__codelineno-2-68" href="#__codelineno-2-68"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
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</span><span id="__span-2-69"><a id="__codelineno-2-69" name="__codelineno-2-69" href="#__codelineno-2-69"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
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</span><span id="__span-2-70"><a id="__codelineno-2-70" name="__codelineno-2-70" href="#__codelineno-2-70"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-2-71"><a id="__codelineno-2-71" name="__codelineno-2-71" href="#__codelineno-2-71"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
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</span><span id="__span-2-72"><a id="__codelineno-2-72" name="__codelineno-2-72" href="#__codelineno-2-72"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
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</span><span id="__span-2-73"><a id="__codelineno-2-73" name="__codelineno-2-73" href="#__codelineno-2-73"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
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</span><span id="__span-2-74"><a id="__codelineno-2-74" name="__codelineno-2-74" href="#__codelineno-2-74"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">(),</span>
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</span><span id="__span-2-75"><a id="__codelineno-2-75" name="__codelineno-2-75" href="#__codelineno-2-75"></a> <span class="p">)</span>
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</span><span id="__span-2-76"><a id="__codelineno-2-76" name="__codelineno-2-76" href="#__codelineno-2-76"></a>
|
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</span><span id="__span-2-77"><a id="__codelineno-2-77" name="__codelineno-2-77" href="#__codelineno-2-77"></a>
|
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</span><span id="__span-2-78"><a id="__codelineno-2-78" name="__codelineno-2-78" href="#__codelineno-2-78"></a><span class="k">class</span> <span class="nc">Autoencoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-2-79"><a id="__codelineno-2-79" name="__codelineno-2-79" href="#__codelineno-2-79"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-2-80"><a id="__codelineno-2-80" name="__codelineno-2-80" href="#__codelineno-2-80"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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</span><span id="__span-2-81"><a id="__codelineno-2-81" name="__codelineno-2-81" href="#__codelineno-2-81"></a> <span class="n">Encoder</span><span class="p">(),</span>
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</span><span id="__span-2-82"><a id="__codelineno-2-82" name="__codelineno-2-82" href="#__codelineno-2-82"></a> <span class="n">Decoder</span><span class="p">(),</span>
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</span><span id="__span-2-83"><a id="__codelineno-2-83" name="__codelineno-2-83" href="#__codelineno-2-83"></a> <span class="p">)</span>
|
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</span><span id="__span-2-84"><a id="__codelineno-2-84" name="__codelineno-2-84" href="#__codelineno-2-84"></a>
|
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</span><span id="__span-2-85"><a id="__codelineno-2-85" name="__codelineno-2-85" href="#__codelineno-2-85"></a> <span class="nd">@property</span>
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</span><span id="__span-2-86"><a id="__codelineno-2-86" name="__codelineno-2-86" href="#__codelineno-2-86"></a> <span class="k">def</span> <span class="nf">encoder</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Encoder</span><span class="p">:</span>
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</span><span id="__span-2-87"><a id="__codelineno-2-87" name="__codelineno-2-87" href="#__codelineno-2-87"></a> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensure_find</span><span class="p">(</span><span class="n">Encoder</span><span class="p">)</span>
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</span><span id="__span-2-88"><a id="__codelineno-2-88" name="__codelineno-2-88" href="#__codelineno-2-88"></a>
|
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</span><span id="__span-2-89"><a id="__codelineno-2-89" name="__codelineno-2-89" href="#__codelineno-2-89"></a> <span class="nd">@property</span>
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</span><span id="__span-2-90"><a id="__codelineno-2-90" name="__codelineno-2-90" href="#__codelineno-2-90"></a> <span class="k">def</span> <span class="nf">decoder</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Decoder</span><span class="p">:</span>
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</span><span id="__span-2-91"><a id="__codelineno-2-91" name="__codelineno-2-91" href="#__codelineno-2-91"></a> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensure_find</span><span class="p">(</span><span class="n">Decoder</span><span class="p">)</span>
|
|
</span></code></pre></div>
|
|
</details>
|
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<p>We now have a fully functional autoencoder that takes an image with one channel of
|
|
size 64x64 and compresses it to a vector of size 256 (x16 compression). The decoder then takes this vector and reconstructs the original image.</p>
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<div class="language-py highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">import</span> <span class="nn">torch</span>
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</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
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</span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="n">autoencoder</span> <span class="o">=</span> <span class="n">Autoencoder</span><span class="p">()</span>
|
|
</span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a>
|
|
</span><span id="__span-3-5"><a id="__codelineno-3-5" name="__codelineno-3-5" href="#__codelineno-3-5"></a><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</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="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span> <span class="c1"># batch of 2 images</span>
|
|
</span><span id="__span-3-6"><a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a>
|
|
</span><span id="__span-3-7"><a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="n">z</span> <span class="o">=</span> <span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># [2, 256]</span>
|
|
</span><span id="__span-3-8"><a id="__codelineno-3-8" name="__codelineno-3-8" href="#__codelineno-3-8"></a>
|
|
</span><span id="__span-3-9"><a id="__codelineno-3-9" name="__codelineno-3-9" href="#__codelineno-3-9"></a><span class="n">x_reconstructed</span> <span class="o">=</span> <span class="n">autoencoder</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="c1"># [2, 1, 64, 64]</span>
|
|
</span></code></pre></div>
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<h2 id="dataset">Dataset<a class="headerlink" href="#dataset" title="Permanent link">¶</a></h2>
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<p>We will use a synthetic dataset of rectangles of different sizes. The dataset will be generated on the fly using this
|
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simple function:</p>
|
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<div class="language-python highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">import</span> <span class="nn">random</span>
|
|
</span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Generator</span>
|
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</span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
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</span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a>
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</span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">image_to_tensor</span>
|
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</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a>
|
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</span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="k">def</span> <span class="nf">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Generator</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
|
|
</span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a><span class="w"> </span><span class="sd">"""Generate a tensor of a binary mask of size `size` using random rectangles."""</span>
|
|
</span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a> <span class="n">seed</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">**</span><span class="mi">32</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
|
|
</span><span id="__span-4-11"><a id="__codelineno-4-11" name="__codelineno-4-11" href="#__codelineno-4-11"></a> <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
|
|
</span><span id="__span-4-12"><a id="__codelineno-4-12" name="__codelineno-4-12" href="#__codelineno-4-12"></a>
|
|
</span><span id="__span-4-13"><a id="__codelineno-4-13" name="__codelineno-4-13" href="#__codelineno-4-13"></a> <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
|
|
</span><span id="__span-4-14"><a id="__codelineno-4-14" name="__codelineno-4-14" href="#__codelineno-4-14"></a> <span class="n">rectangle</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">new</span><span class="p">(</span>
|
|
</span><span id="__span-4-15"><a id="__codelineno-4-15" name="__codelineno-4-15" href="#__codelineno-4-15"></a> <span class="s2">"L"</span><span class="p">,</span> <span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="p">)),</span> <span class="n">color</span><span class="o">=</span><span class="mi">255</span>
|
|
</span><span id="__span-4-16"><a id="__codelineno-4-16" name="__codelineno-4-16" href="#__codelineno-4-16"></a> <span class="p">)</span>
|
|
</span><span id="__span-4-17"><a id="__codelineno-4-17" name="__codelineno-4-17" href="#__codelineno-4-17"></a> <span class="n">mask</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="s2">"L"</span><span class="p">,</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">))</span>
|
|
</span><span id="__span-4-18"><a id="__codelineno-4-18" name="__codelineno-4-18" href="#__codelineno-4-18"></a> <span class="n">mask</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span>
|
|
</span><span id="__span-4-19"><a id="__codelineno-4-19" name="__codelineno-4-19" href="#__codelineno-4-19"></a> <span class="n">rectangle</span><span class="p">,</span>
|
|
</span><span id="__span-4-20"><a id="__codelineno-4-20" name="__codelineno-4-20" href="#__codelineno-4-20"></a> <span class="p">(</span>
|
|
</span><span id="__span-4-21"><a id="__codelineno-4-21" name="__codelineno-4-21" href="#__codelineno-4-21"></a> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="n">rectangle</span><span class="o">.</span><span class="n">width</span><span class="p">),</span>
|
|
</span><span id="__span-4-22"><a id="__codelineno-4-22" name="__codelineno-4-22" href="#__codelineno-4-22"></a> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="n">rectangle</span><span class="o">.</span><span class="n">height</span><span class="p">),</span>
|
|
</span><span id="__span-4-23"><a id="__codelineno-4-23" name="__codelineno-4-23" href="#__codelineno-4-23"></a> <span class="p">),</span>
|
|
</span><span id="__span-4-24"><a id="__codelineno-4-24" name="__codelineno-4-24" href="#__codelineno-4-24"></a> <span class="p">)</span>
|
|
</span><span id="__span-4-25"><a id="__codelineno-4-25" name="__codelineno-4-25" href="#__codelineno-4-25"></a> <span class="n">tensor</span> <span class="o">=</span> <span class="n">image_to_tensor</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
|
|
</span><span id="__span-4-26"><a id="__codelineno-4-26" name="__codelineno-4-26" href="#__codelineno-4-26"></a>
|
|
</span><span id="__span-4-27"><a id="__codelineno-4-27" name="__codelineno-4-27" href="#__codelineno-4-27"></a> <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span>
|
|
</span><span id="__span-4-28"><a id="__codelineno-4-28" name="__codelineno-4-28" href="#__codelineno-4-28"></a> <span class="n">tensor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">tensor</span>
|
|
</span><span id="__span-4-29"><a id="__codelineno-4-29" name="__codelineno-4-29" href="#__codelineno-4-29"></a>
|
|
</span><span id="__span-4-30"><a id="__codelineno-4-30" name="__codelineno-4-30" href="#__codelineno-4-30"></a> <span class="k">yield</span> <span class="n">tensor</span>
|
|
</span></code></pre></div>
|
|
<p>To generate a mask, do:</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">tensor_to_image</span>
|
|
</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>
|
|
</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="n">mask</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">generate_mask</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">))</span>
|
|
</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="n">tensor_to_image</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"mask.png"</span><span class="p">)</span>
|
|
</span></code></pre></div>
|
|
<p>Here are a two examples of generated masks:
|
|
<img alt="alt text" src="sample-0.png" />
|
|
<img alt="alt text" src="sample-1.png" /></p>
|
|
<h2 id="trainer">Trainer<a class="headerlink" href="#trainer" title="Permanent link">¶</a></h2>
|
|
<p>We will now create a Trainer class to handle the training loop. This class will manage the model, the optimizer, the loss function, and the dataset. It will also orchestrate the training loop and the evaluation loop.</p>
|
|
<p>But first, we need to define the batch type that will be used to represent a batch for the forward and backward pass and the configuration associated with the trainer.</p>
|
|
<h3 id="batch">Batch<a class="headerlink" href="#batch" title="Permanent link">¶</a></h3>
|
|
<p>Our batches are composed of a single tensor representing the images. We will define a simple <code>Batch</code> type to implement this.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
|
|
</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>
|
|
</span><span id="__span-6-3"><a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a><span class="nd">@dataclass</span>
|
|
</span><span id="__span-6-4"><a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a><span class="k">class</span> <span class="nc">Batch</span><span class="p">:</span>
|
|
</span><span id="__span-6-5"><a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a> <span class="n">image</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
|
|
</span></code></pre></div>
|
|
<h3 id="config">Config<a class="headerlink" href="#config" title="Permanent link">¶</a></h3>
|
|
<p>We will now define the configuration for the autoencoder. It holds the configuration for the training loop, the optimizer, and the learning rate scheduler. It should inherit <code>refiners.training_utils.BaseConfig</code> and has the following mandatory attributes:</p>
|
|
<ul>
|
|
<li><code>TrainingConfig</code>: The configuration for the training loop, including the duration of the training, the batch size, device, dtype, etc.</li>
|
|
<li><code>OptimizerConfig</code>: The configuration for the optimizer, including the learning rate, weight decay, etc.</li>
|
|
<li><code>LRSchedulerConfig</code>: The configuration for the learning rate scheduler, including the scheduler type, parameters, etc.</li>
|
|
</ul>
|
|
<p>Example:</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">BaseConfig</span><span class="p">,</span> <span class="n">TrainingConfig</span><span class="p">,</span> <span class="n">OptimizerConfig</span><span class="p">,</span> <span class="n">LRSchedulerConfig</span><span class="p">,</span> <span class="n">Optimizers</span><span class="p">,</span> <span class="n">LRSchedulerType</span><span class="p">,</span> <span class="n">Epoch</span>
|
|
</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>
|
|
</span><span id="__span-7-3"><a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
|
|
</span><span id="__span-7-4"><a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a> <span class="o">...</span>
|
|
</span><span id="__span-7-5"><a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a>
|
|
</span><span id="__span-7-6"><a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a><span class="n">training</span> <span class="o">=</span> <span class="n">TrainingConfig</span><span class="p">(</span>
|
|
</span><span id="__span-7-7"><a id="__codelineno-7-7" name="__codelineno-7-7" href="#__codelineno-7-7"></a> <span class="n">duration</span><span class="o">=</span><span class="n">Epoch</span><span class="p">(</span><span class="mi">1000</span><span class="p">),</span>
|
|
</span><span id="__span-7-8"><a id="__codelineno-7-8" name="__codelineno-7-8" href="#__codelineno-7-8"></a> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">,</span>
|
|
</span><span id="__span-7-9"><a id="__codelineno-7-9" name="__codelineno-7-9" href="#__codelineno-7-9"></a> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span>
|
|
</span><span id="__span-7-10"><a id="__codelineno-7-10" name="__codelineno-7-10" href="#__codelineno-7-10"></a><span class="p">)</span>
|
|
</span><span id="__span-7-11"><a id="__codelineno-7-11" name="__codelineno-7-11" href="#__codelineno-7-11"></a>
|
|
</span><span id="__span-7-12"><a id="__codelineno-7-12" name="__codelineno-7-12" href="#__codelineno-7-12"></a><span class="n">optimizer</span> <span class="o">=</span> <span class="n">OptimizerConfig</span><span class="p">(</span>
|
|
</span><span id="__span-7-13"><a id="__codelineno-7-13" name="__codelineno-7-13" href="#__codelineno-7-13"></a> <span class="n">optimizer</span><span class="o">=</span><span class="n">Optimizers</span><span class="o">.</span><span class="n">AdamW</span><span class="p">,</span>
|
|
</span><span id="__span-7-14"><a id="__codelineno-7-14" name="__codelineno-7-14" href="#__codelineno-7-14"></a> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span>
|
|
</span><span id="__span-7-15"><a id="__codelineno-7-15" name="__codelineno-7-15" href="#__codelineno-7-15"></a><span class="p">)</span>
|
|
</span><span id="__span-7-16"><a id="__codelineno-7-16" name="__codelineno-7-16" href="#__codelineno-7-16"></a>
|
|
</span><span id="__span-7-17"><a id="__codelineno-7-17" name="__codelineno-7-17" href="#__codelineno-7-17"></a><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">LRSchedulerConfig</span><span class="p">(</span>
|
|
</span><span id="__span-7-18"><a id="__codelineno-7-18" name="__codelineno-7-18" href="#__codelineno-7-18"></a> <span class="nb">type</span><span class="o">=</span><span class="n">LRSchedulerType</span><span class="o">.</span><span class="n">ConstantLR</span>
|
|
</span><span id="__span-7-19"><a id="__codelineno-7-19" name="__codelineno-7-19" href="#__codelineno-7-19"></a><span class="p">)</span>
|
|
</span><span id="__span-7-20"><a id="__codelineno-7-20" name="__codelineno-7-20" href="#__codelineno-7-20"></a>
|
|
</span><span id="__span-7-21"><a id="__codelineno-7-21" name="__codelineno-7-21" href="#__codelineno-7-21"></a><span class="n">config</span> <span class="o">=</span> <span class="n">AutoencoderConfig</span><span class="p">(</span>
|
|
</span><span id="__span-7-22"><a id="__codelineno-7-22" name="__codelineno-7-22" href="#__codelineno-7-22"></a> <span class="n">training</span><span class="o">=</span><span class="n">training</span><span class="p">,</span>
|
|
</span><span id="__span-7-23"><a id="__codelineno-7-23" name="__codelineno-7-23" href="#__codelineno-7-23"></a> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
|
|
</span><span id="__span-7-24"><a id="__codelineno-7-24" name="__codelineno-7-24" href="#__codelineno-7-24"></a> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler</span><span class="p">,</span>
|
|
</span><span id="__span-7-25"><a id="__codelineno-7-25" name="__codelineno-7-25" href="#__codelineno-7-25"></a><span class="p">)</span>
|
|
</span></code></pre></div>
|
|
<h3 id="subclass">Subclass<a class="headerlink" href="#subclass" title="Permanent link">¶</a></h3>
|
|
<p>We can now define the Trainer subclass. It should inherit from <code>refiners.training_utils.Trainer</code> and implement the following methods:</p>
|
|
<ul>
|
|
<li><code>create_data_iterable</code>: The <code>Trainer</code> will call this method to create and cache the data iterable. During training, the loop will pull batches from this iterable and pass them to the <code>compute_loss</code> method. Every time the iterable is exhausted, an epoch ends.</li>
|
|
<li><code>compute_loss</code>: This method should take a Batch and return the loss tensor.</li>
|
|
</ul>
|
|
<p>Here is a simple implementation of the <code>create_data_iterable</code> method. For this toy example, we will generate a simple list of <code>Batch</code> objects containing random masks. Later you can replace this with <code>torch.utils.data.DataLoader</code> or any other data loader with more complex features that support shuffling, parallel loading, etc.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span>
|
|
</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">Trainer</span>
|
|
</span><span id="__span-8-3"><a id="__codelineno-8-3" name="__codelineno-8-3" href="#__codelineno-8-3"></a>
|
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</span><span id="__span-8-4"><a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a>
|
|
</span><span id="__span-8-5"><a id="__codelineno-8-5" name="__codelineno-8-5" href="#__codelineno-8-5"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
|
|
</span><span id="__span-8-6"><a id="__codelineno-8-6" name="__codelineno-8-6" href="#__codelineno-8-6"></a> <span class="n">num_images</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span>
|
|
</span><span id="__span-8-7"><a id="__codelineno-8-7" name="__codelineno-8-7" href="#__codelineno-8-7"></a> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span>
|
|
</span><span id="__span-8-8"><a id="__codelineno-8-8" name="__codelineno-8-8" href="#__codelineno-8-8"></a>
|
|
</span><span id="__span-8-9"><a id="__codelineno-8-9" name="__codelineno-8-9" href="#__codelineno-8-9"></a>
|
|
</span><span id="__span-8-10"><a id="__codelineno-8-10" name="__codelineno-8-10" href="#__codelineno-8-10"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
|
|
</span><span id="__span-8-11"><a id="__codelineno-8-11" name="__codelineno-8-11" href="#__codelineno-8-11"></a> <span class="k">def</span> <span class="nf">create_data_iterable</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">[</span><span class="n">Batch</span><span class="p">]:</span>
|
|
</span><span id="__span-8-12"><a id="__codelineno-8-12" name="__codelineno-8-12" href="#__codelineno-8-12"></a> <span class="n">dataset</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Batch</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
|
</span><span id="__span-8-13"><a id="__codelineno-8-13" name="__codelineno-8-13" href="#__codelineno-8-13"></a> <span class="n">generator</span> <span class="o">=</span> <span class="n">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
|
|
</span><span id="__span-8-14"><a id="__codelineno-8-14" name="__codelineno-8-14" href="#__codelineno-8-14"></a>
|
|
</span><span id="__span-8-15"><a id="__codelineno-8-15" name="__codelineno-8-15" href="#__codelineno-8-15"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_images</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">):</span>
|
|
</span><span id="__span-8-16"><a id="__codelineno-8-16" name="__codelineno-8-16" href="#__codelineno-8-16"></a> <span class="n">masks</span> <span class="o">=</span> <span class="p">[</span><span class="nb">next</span><span class="p">(</span><span class="n">generator</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)]</span>
|
|
</span><span id="__span-8-17"><a id="__codelineno-8-17" name="__codelineno-8-17" href="#__codelineno-8-17"></a> <span class="n">dataset</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Batch</span><span class="p">(</span><span class="n">image</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">masks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span>
|
|
</span><span id="__span-8-18"><a id="__codelineno-8-18" name="__codelineno-8-18" href="#__codelineno-8-18"></a>
|
|
</span><span id="__span-8-19"><a id="__codelineno-8-19" name="__codelineno-8-19" href="#__codelineno-8-19"></a> <span class="k">return</span> <span class="n">dataset</span>
|
|
</span><span id="__span-8-20"><a id="__codelineno-8-20" name="__codelineno-8-20" href="#__codelineno-8-20"></a>
|
|
</span><span id="__span-8-21"><a id="__codelineno-8-21" name="__codelineno-8-21" href="#__codelineno-8-21"></a> <span class="k">def</span> <span class="nf">compute_loss</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">Batch</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
|
|
</span><span id="__span-8-22"><a id="__codelineno-8-22" name="__codelineno-8-22" href="#__codelineno-8-22"></a> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"We'll implement this later"</span><span class="p">)</span>
|
|
</span><span id="__span-8-23"><a id="__codelineno-8-23" name="__codelineno-8-23" href="#__codelineno-8-23"></a>
|
|
</span><span id="__span-8-24"><a id="__codelineno-8-24" name="__codelineno-8-24" href="#__codelineno-8-24"></a>
|
|
</span><span id="__span-8-25"><a id="__codelineno-8-25" name="__codelineno-8-25" href="#__codelineno-8-25"></a><span class="n">trainer</span> <span class="o">=</span> <span class="n">AutoencoderTrainer</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
|
|
</span></code></pre></div>
|
|
<h3 id="model-registration">Model registration<a class="headerlink" href="#model-registration" title="Permanent link">¶</a></h3>
|
|
<p>For the Trainer to be able to handle the model, we need to register it. </p>
|
|
<p>We need two things to do so: </p>
|
|
<ul>
|
|
<li>Add <code>refiners.training_utils.ModelConfig</code> attribute to the Config named <code>autoencoder</code>.</li>
|
|
<li>Add a method to the Trainer subclass that returns the model decorated with <code>@register_model</code> decorator. This method should take the <code>ModelConfig</code> as an argument. The Trainer's <code>__init__</code> will register the models and add any parameters to the optimizer that have <code>requires_grad</code> enabled.</li>
|
|
</ul>
|
|
<p>After registering the model, the <code>self.autoencoder</code> attribute will be available in the Trainer.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">ModelConfig</span><span class="p">,</span> <span class="n">register_model</span>
|
|
</span><span id="__span-9-2"><a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a>
|
|
</span><span id="__span-9-3"><a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a>
|
|
</span><span id="__span-9-4"><a id="__codelineno-9-4" name="__codelineno-9-4" href="#__codelineno-9-4"></a><span class="k">class</span> <span class="nc">AutoencoderModelConfig</span><span class="p">(</span><span class="n">ModelConfig</span><span class="p">):</span>
|
|
</span><span id="__span-9-5"><a id="__codelineno-9-5" name="__codelineno-9-5" href="#__codelineno-9-5"></a> <span class="k">pass</span>
|
|
</span><span id="__span-9-6"><a id="__codelineno-9-6" name="__codelineno-9-6" href="#__codelineno-9-6"></a>
|
|
</span><span id="__span-9-7"><a id="__codelineno-9-7" name="__codelineno-9-7" href="#__codelineno-9-7"></a>
|
|
</span><span id="__span-9-8"><a id="__codelineno-9-8" name="__codelineno-9-8" href="#__codelineno-9-8"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
|
|
</span><span id="__span-9-9"><a id="__codelineno-9-9" name="__codelineno-9-9" href="#__codelineno-9-9"></a> <span class="n">num_images</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span>
|
|
</span><span id="__span-9-10"><a id="__codelineno-9-10" name="__codelineno-9-10" href="#__codelineno-9-10"></a> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span>
|
|
</span><span id="__span-9-11"><a id="__codelineno-9-11" name="__codelineno-9-11" href="#__codelineno-9-11"></a> <span class="n">autoencoder</span><span class="p">:</span> <span class="n">AutoencoderModelConfig</span>
|
|
</span><span id="__span-9-12"><a id="__codelineno-9-12" name="__codelineno-9-12" href="#__codelineno-9-12"></a>
|
|
</span><span id="__span-9-13"><a id="__codelineno-9-13" name="__codelineno-9-13" href="#__codelineno-9-13"></a>
|
|
</span><span id="__span-9-14"><a id="__codelineno-9-14" name="__codelineno-9-14" href="#__codelineno-9-14"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
|
|
</span><span id="__span-9-15"><a id="__codelineno-9-15" name="__codelineno-9-15" href="#__codelineno-9-15"></a> <span class="c1"># ... other methods</span>
|
|
</span><span id="__span-9-16"><a id="__codelineno-9-16" name="__codelineno-9-16" href="#__codelineno-9-16"></a>
|
|
</span><span id="__span-9-17"><a id="__codelineno-9-17" name="__codelineno-9-17" href="#__codelineno-9-17"></a> <span class="nd">@register_model</span><span class="p">()</span>
|
|
</span><span id="__span-9-18"><a id="__codelineno-9-18" name="__codelineno-9-18" href="#__codelineno-9-18"></a> <span class="k">def</span> <span class="nf">autoencoder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">AutoencoderModelConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">Autoencoder</span><span class="p">:</span>
|
|
</span><span id="__span-9-19"><a id="__codelineno-9-19" name="__codelineno-9-19" href="#__codelineno-9-19"></a> <span class="k">return</span> <span class="n">Autoencoder</span><span class="p">()</span>
|
|
</span><span id="__span-9-20"><a id="__codelineno-9-20" name="__codelineno-9-20" href="#__codelineno-9-20"></a>
|
|
</span><span id="__span-9-21"><a id="__codelineno-9-21" name="__codelineno-9-21" href="#__codelineno-9-21"></a> <span class="k">def</span> <span class="nf">compute_loss</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">Batch</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
|
|
</span><span id="__span-9-22"><a id="__codelineno-9-22" name="__codelineno-9-22" href="#__codelineno-9-22"></a> <span class="n">batch</span><span class="o">.</span><span class="n">image</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">image</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> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
|
</span><span id="__span-9-23"><a id="__codelineno-9-23" name="__codelineno-9-23" href="#__codelineno-9-23"></a> <span class="n">x_reconstructed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span>
|
|
</span><span id="__span-9-24"><a id="__codelineno-9-24" name="__codelineno-9-24" href="#__codelineno-9-24"></a> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">image</span><span class="p">)</span>
|
|
</span><span id="__span-9-25"><a id="__codelineno-9-25" name="__codelineno-9-25" href="#__codelineno-9-25"></a> <span class="p">)</span>
|
|
</span><span id="__span-9-26"><a id="__codelineno-9-26" name="__codelineno-9-26" href="#__codelineno-9-26"></a> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">x_reconstructed</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">image</span><span class="p">)</span>
|
|
</span></code></pre></div>
|
|
<p>We now have a fully functional Trainer that can train our autoencoder. We can now call the <code>train</code> method to start the training loop.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-10-1"><a id="__codelineno-10-1" name="__codelineno-10-1" href="#__codelineno-10-1"></a><span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
|
</span></code></pre></div>
|
|
<p><img alt="alt text" src="terminal-logging.png" /></p>
|
|
<h2 id="logging">Logging<a class="headerlink" href="#logging" title="Permanent link">¶</a></h2>
|
|
<p>Let's write a simple logging callback to log the loss and the reconstructed images during training. A callback is a class that inherits from <code>refiners.training_utils.Callback</code> and implement any of the following methods:</p>
|
|
<ul>
|
|
<li><code>on_init_begin</code></li>
|
|
<li><code>on_init_end</code></li>
|
|
<li><code>on_train_begin</code></li>
|
|
<li><code>on_train_end</code></li>
|
|
<li><code>on_epoch_begin</code></li>
|
|
<li><code>on_epoch_end</code></li>
|
|
<li><code>on_step_begin</code></li>
|
|
<li><code>on_step_end</code></li>
|
|
<li><code>on_backward_begin</code></li>
|
|
<li><code>on_backward_end</code></li>
|
|
<li><code>on_optimizer_step_begin</code></li>
|
|
<li><code>on_optimizer_step_end</code></li>
|
|
<li><code>on_compute_loss_begin</code></li>
|
|
<li><code>on_compute_loss_end</code></li>
|
|
<li><code>on_evaluate_begin</code></li>
|
|
<li><code>on_evaluate_end</code></li>
|
|
<li><code>on_lr_scheduler_step_begin</code></li>
|
|
<li><code>on_lr_scheduler_step_end</code></li>
|
|
</ul>
|
|
<p>We will implement the <code>on_epoch_end</code> method to log the loss and the reconstructed images and the <code>on_compute_loss_end</code> method to store the loss in a list.</p>
|
|
<div class="language-python highlight"><pre><span></span><code><span id="__span-11-1"><a id="__codelineno-11-1" name="__codelineno-11-1" href="#__codelineno-11-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">Callback</span>
|
|
</span><span id="__span-11-2"><a id="__codelineno-11-2" name="__codelineno-11-2" href="#__codelineno-11-2"></a><span class="kn">from</span> <span class="nn">loguru</span> <span class="kn">import</span> <span class="n">logger</span>
|
|
</span><span id="__span-11-3"><a id="__codelineno-11-3" name="__codelineno-11-3" href="#__codelineno-11-3"></a><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
|
|
</span><span id="__span-11-4"><a id="__codelineno-11-4" name="__codelineno-11-4" href="#__codelineno-11-4"></a>
|
|
</span><span id="__span-11-5"><a id="__codelineno-11-5" name="__codelineno-11-5" href="#__codelineno-11-5"></a>
|
|
</span><span id="__span-11-6"><a id="__codelineno-11-6" name="__codelineno-11-6" href="#__codelineno-11-6"></a><span class="k">class</span> <span class="nc">LoggingCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">[</span><span class="n">Any</span><span class="p">]):</span>
|
|
</span><span id="__span-11-7"><a id="__codelineno-11-7" name="__codelineno-11-7" href="#__codelineno-11-7"></a> <span class="n">losses</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
|
</span><span id="__span-11-8"><a id="__codelineno-11-8" name="__codelineno-11-8" href="#__codelineno-11-8"></a>
|
|
</span><span id="__span-11-9"><a id="__codelineno-11-9" name="__codelineno-11-9" href="#__codelineno-11-9"></a> <span class="k">def</span> <span class="nf">on_compute_loss_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-11-10"><a id="__codelineno-11-10" name="__codelineno-11-10" href="#__codelineno-11-10"></a> <span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
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</span><span id="__span-11-11"><a id="__codelineno-11-11" name="__codelineno-11-11" href="#__codelineno-11-11"></a>
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</span><span id="__span-11-12"><a id="__codelineno-11-12" name="__codelineno-11-12" href="#__codelineno-11-12"></a> <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epoch</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-11-13"><a id="__codelineno-11-13" name="__codelineno-11-13" href="#__codelineno-11-13"></a> <span class="n">mean_loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span>
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</span><span id="__span-11-14"><a id="__codelineno-11-14" name="__codelineno-11-14" href="#__codelineno-11-14"></a> <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean loss: </span><span class="si">{</span><span class="n">mean_loss</span><span class="si">}</span><span class="s2">, epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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</span><span id="__span-11-15"><a id="__codelineno-11-15" name="__codelineno-11-15" href="#__codelineno-11-15"></a> <span class="bp">self</span><span class="o">.</span><span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
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</span></code></pre></div>
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<p>Exactly like models, we need to register the callback to the Trainer. We can do so by adding a <code>CallbackConfig</code> attribute to the config named <code>logging</code> and adding a method to the Trainer class that returns the callback decorated with <code>@register_callback</code> decorator. </p>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-12-1"><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">CallbackConfig</span><span class="p">,</span> <span class="n">register_callback</span>
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</span><span id="__span-12-2"><a id="__codelineno-12-2" name="__codelineno-12-2" href="#__codelineno-12-2"></a>
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</span><span id="__span-12-3"><a id="__codelineno-12-3" name="__codelineno-12-3" href="#__codelineno-12-3"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
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</span><span id="__span-12-4"><a id="__codelineno-12-4" name="__codelineno-12-4" href="#__codelineno-12-4"></a> <span class="c1"># ... other properties</span>
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</span><span id="__span-12-5"><a id="__codelineno-12-5" name="__codelineno-12-5" href="#__codelineno-12-5"></a> <span class="n">logging</span><span class="p">:</span> <span class="n">CallbackConfig</span> <span class="o">=</span> <span class="n">CallbackConfig</span><span class="p">()</span>
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</span><span id="__span-12-6"><a id="__codelineno-12-6" name="__codelineno-12-6" href="#__codelineno-12-6"></a>
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</span><span id="__span-12-7"><a id="__codelineno-12-7" name="__codelineno-12-7" href="#__codelineno-12-7"></a>
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</span><span id="__span-12-8"><a id="__codelineno-12-8" name="__codelineno-12-8" href="#__codelineno-12-8"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
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</span><span id="__span-12-9"><a id="__codelineno-12-9" name="__codelineno-12-9" href="#__codelineno-12-9"></a> <span class="c1"># ... other methods</span>
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</span><span id="__span-12-10"><a id="__codelineno-12-10" name="__codelineno-12-10" href="#__codelineno-12-10"></a>
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</span><span id="__span-12-11"><a id="__codelineno-12-11" name="__codelineno-12-11" href="#__codelineno-12-11"></a> <span class="nd">@register_callback</span><span class="p">()</span>
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</span><span id="__span-12-12"><a id="__codelineno-12-12" name="__codelineno-12-12" href="#__codelineno-12-12"></a> <span class="k">def</span> <span class="nf">logging</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">CallbackConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">LoggingCallback</span><span class="p">:</span>
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</span><span id="__span-12-13"><a id="__codelineno-12-13" name="__codelineno-12-13" href="#__codelineno-12-13"></a> <span class="k">return</span> <span class="n">LoggingCallback</span><span class="p">()</span>
|
|
</span></code></pre></div>
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<p><img alt="alt text" src="loss-logging.png" /></p>
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<h2 id="evaluation">Evaluation<a class="headerlink" href="#evaluation" title="Permanent link">¶</a></h2>
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<p>Let's add an evaluation step to the Trainer. We will generate a few masks and their reconstructions and save them to a file. We start by implementing a <code>compute_evaluation</code> method, then we register a callback to call this method at regular intervals.</p>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-13-1"><a id="__codelineno-13-1" name="__codelineno-13-1" href="#__codelineno-13-1"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
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</span><span id="__span-13-2"><a id="__codelineno-13-2" name="__codelineno-13-2" href="#__codelineno-13-2"></a> <span class="c1"># ... other methods</span>
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</span><span id="__span-13-3"><a id="__codelineno-13-3" name="__codelineno-13-3" href="#__codelineno-13-3"></a>
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</span><span id="__span-13-4"><a id="__codelineno-13-4" name="__codelineno-13-4" href="#__codelineno-13-4"></a> <span class="k">def</span> <span class="nf">compute_evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-13-5"><a id="__codelineno-13-5" name="__codelineno-13-5" href="#__codelineno-13-5"></a> <span class="n">generator</span> <span class="o">=</span> <span class="n">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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</span><span id="__span-13-6"><a id="__codelineno-13-6" name="__codelineno-13-6" href="#__codelineno-13-6"></a>
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</span><span id="__span-13-7"><a id="__codelineno-13-7" name="__codelineno-13-7" href="#__codelineno-13-7"></a> <span class="n">grid</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Image</span><span class="o">.</span><span class="n">Image</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">Image</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-13-8"><a id="__codelineno-13-8" name="__codelineno-13-8" href="#__codelineno-13-8"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
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</span><span id="__span-13-9"><a id="__codelineno-13-9" name="__codelineno-13-9" href="#__codelineno-13-9"></a> <span class="n">mask</span> <span class="o">=</span> <span class="nb">next</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="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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</span><span id="__span-13-10"><a id="__codelineno-13-10" name="__codelineno-13-10" href="#__codelineno-13-10"></a> <span class="n">x_reconstructed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span>
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</span><span id="__span-13-11"><a id="__codelineno-13-11" name="__codelineno-13-11" href="#__codelineno-13-11"></a> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
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</span><span id="__span-13-12"><a id="__codelineno-13-12" name="__codelineno-13-12" href="#__codelineno-13-12"></a> <span class="p">)</span>
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</span><span id="__span-13-13"><a id="__codelineno-13-13" name="__codelineno-13-13" href="#__codelineno-13-13"></a> <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">x_reconstructed</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
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</span><span id="__span-13-14"><a id="__codelineno-13-14" name="__codelineno-13-14" href="#__codelineno-13-14"></a> <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Validation loss: </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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</span><span id="__span-13-15"><a id="__codelineno-13-15" name="__codelineno-13-15" href="#__codelineno-13-15"></a> <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
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</span><span id="__span-13-16"><a id="__codelineno-13-16" name="__codelineno-13-16" href="#__codelineno-13-16"></a> <span class="p">(</span><span class="n">tensor_to_image</span><span class="p">(</span><span class="n">mask</span><span class="p">),</span> <span class="n">tensor_to_image</span><span class="p">((</span><span class="n">x_reconstructed</span><span class="o">></span><span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()))</span>
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</span><span id="__span-13-17"><a id="__codelineno-13-17" name="__codelineno-13-17" href="#__codelineno-13-17"></a> <span class="p">)</span>
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</span><span id="__span-13-18"><a id="__codelineno-13-18" name="__codelineno-13-18" href="#__codelineno-13-18"></a>
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</span><span id="__span-13-19"><a id="__codelineno-13-19" name="__codelineno-13-19" href="#__codelineno-13-19"></a> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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</span><span id="__span-13-20"><a id="__codelineno-13-20" name="__codelineno-13-20" href="#__codelineno-13-20"></a>
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</span><span id="__span-13-21"><a id="__codelineno-13-21" name="__codelineno-13-21" href="#__codelineno-13-21"></a> <span class="n">_</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">))</span>
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</span><span id="__span-13-22"><a id="__codelineno-13-22" name="__codelineno-13-22" href="#__codelineno-13-22"></a>
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</span><span id="__span-13-23"><a id="__codelineno-13-23" name="__codelineno-13-23" href="#__codelineno-13-23"></a> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">reconstructed</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">grid</span><span class="p">):</span>
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</span><span id="__span-13-24"><a id="__codelineno-13-24" name="__codelineno-13-24" href="#__codelineno-13-24"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'gray'</span><span class="p">)</span>
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</span><span id="__span-13-25"><a id="__codelineno-13-25" name="__codelineno-13-25" href="#__codelineno-13-25"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
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</span><span id="__span-13-26"><a id="__codelineno-13-26" name="__codelineno-13-26" href="#__codelineno-13-26"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Mask'</span><span class="p">)</span>
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</span><span id="__span-13-27"><a id="__codelineno-13-27" name="__codelineno-13-27" href="#__codelineno-13-27"></a>
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</span><span id="__span-13-28"><a id="__codelineno-13-28" name="__codelineno-13-28" href="#__codelineno-13-28"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">reconstructed</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'gray'</span><span class="p">)</span>
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</span><span id="__span-13-29"><a id="__codelineno-13-29" name="__codelineno-13-29" href="#__codelineno-13-29"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
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</span><span id="__span-13-30"><a id="__codelineno-13-30" name="__codelineno-13-30" href="#__codelineno-13-30"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Reconstructed'</span><span class="p">)</span>
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</span><span id="__span-13-31"><a id="__codelineno-13-31" name="__codelineno-13-31" href="#__codelineno-13-31"></a>
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</span><span id="__span-13-32"><a id="__codelineno-13-32" name="__codelineno-13-32" href="#__codelineno-13-32"></a> <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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</span><span id="__span-13-33"><a id="__codelineno-13-33" name="__codelineno-13-33" href="#__codelineno-13-33"></a> <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"result_</span><span class="si">{</span><span class="n">trainer</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">epoch</span><span class="si">}</span><span class="s2">.png"</span><span class="p">)</span>
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</span><span id="__span-13-34"><a id="__codelineno-13-34" name="__codelineno-13-34" href="#__codelineno-13-34"></a> <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
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</span></code></pre></div>
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<p>We starting by implementing an <code>EvaluationConfig</code> that controls the evaluation interval and the seed for the random generator.</p>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-14-1"><a id="__codelineno-14-1" name="__codelineno-14-1" href="#__codelineno-14-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils.config</span> <span class="kn">import</span> <span class="n">TimeValueField</span>
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</span><span id="__span-14-2"><a id="__codelineno-14-2" name="__codelineno-14-2" href="#__codelineno-14-2"></a>
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</span><span id="__span-14-3"><a id="__codelineno-14-3" name="__codelineno-14-3" href="#__codelineno-14-3"></a><span class="k">class</span> <span class="nc">EvaluationConfig</span><span class="p">(</span><span class="n">CallbackConfig</span><span class="p">):</span>
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</span><span id="__span-14-4"><a id="__codelineno-14-4" name="__codelineno-14-4" href="#__codelineno-14-4"></a> <span class="n">interval</span><span class="p">:</span> <span class="n">TimeValueField</span>
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</span><span id="__span-14-5"><a id="__codelineno-14-5" name="__codelineno-14-5" href="#__codelineno-14-5"></a> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span>
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</span></code></pre></div>
|
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<p>The <code>TimeValueField</code> is a custom field that allow Pydantic to parse a string representing a time value (e.g., <code>"50:epochs"</code>) into a <code>TimeValue</code> object. This is useful to specify the evaluation interval in the configuration file.</p>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-15-1"><a id="__codelineno-15-1" name="__codelineno-15-1" href="#__codelineno-15-1"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="n">scoped_seed</span><span class="p">,</span> <span class="n">Callback</span>
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</span><span id="__span-15-2"><a id="__codelineno-15-2" name="__codelineno-15-2" href="#__codelineno-15-2"></a>
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</span><span id="__span-15-3"><a id="__codelineno-15-3" name="__codelineno-15-3" href="#__codelineno-15-3"></a><span class="k">class</span> <span class="nc">EvaluationCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">[</span><span class="n">Any</span><span class="p">]):</span>
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</span><span id="__span-15-4"><a id="__codelineno-15-4" name="__codelineno-15-4" href="#__codelineno-15-4"></a> <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="n">config</span><span class="p">:</span> <span class="n">EvaluationConfig</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-15-5"><a id="__codelineno-15-5" name="__codelineno-15-5" href="#__codelineno-15-5"></a> <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
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</span><span id="__span-15-6"><a id="__codelineno-15-6" name="__codelineno-15-6" href="#__codelineno-15-6"></a>
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</span><span id="__span-15-7"><a id="__codelineno-15-7" name="__codelineno-15-7" href="#__codelineno-15-7"></a> <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">:</span> <span class="n">Trainer</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-15-8"><a id="__codelineno-15-8" name="__codelineno-15-8" href="#__codelineno-15-8"></a> <span class="c1"># The `is_due` method checks if the current epoch is a multiple of the interval.</span>
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</span><span id="__span-15-9"><a id="__codelineno-15-9" name="__codelineno-15-9" href="#__codelineno-15-9"></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">trainer</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">is_due</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">interval</span><span class="p">):</span>
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</span><span id="__span-15-10"><a id="__codelineno-15-10" name="__codelineno-15-10" href="#__codelineno-15-10"></a> <span class="k">return</span>
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</span><span id="__span-15-11"><a id="__codelineno-15-11" name="__codelineno-15-11" href="#__codelineno-15-11"></a>
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</span><span id="__span-15-12"><a id="__codelineno-15-12" name="__codelineno-15-12" href="#__codelineno-15-12"></a> <span class="c1"># The `scoped_seed` context manager encapsulates the random state for the evaluation and restores it after the </span>
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</span><span id="__span-15-13"><a id="__codelineno-15-13" name="__codelineno-15-13" href="#__codelineno-15-13"></a> <span class="c1"># evaluation.</span>
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</span><span id="__span-15-14"><a id="__codelineno-15-14" name="__codelineno-15-14" href="#__codelineno-15-14"></a> <span class="k">with</span> <span class="n">scoped_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">):</span>
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</span><span id="__span-15-15"><a id="__codelineno-15-15" name="__codelineno-15-15" href="#__codelineno-15-15"></a> <span class="n">trainer</span><span class="o">.</span><span class="n">compute_evaluation</span><span class="p">()</span>
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</span></code></pre></div>
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<p>We can now register the callback to the Trainer.</p>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-16-1"><a id="__codelineno-16-1" name="__codelineno-16-1" href="#__codelineno-16-1"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
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</span><span id="__span-16-2"><a id="__codelineno-16-2" name="__codelineno-16-2" href="#__codelineno-16-2"></a> <span class="c1"># ... other properties</span>
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</span><span id="__span-16-3"><a id="__codelineno-16-3" name="__codelineno-16-3" href="#__codelineno-16-3"></a> <span class="n">evaluation</span><span class="p">:</span> <span class="n">EvaluationConfig</span>
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</span></code></pre></div>
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<div class="language-python highlight"><pre><span></span><code><span id="__span-17-1"><a id="__codelineno-17-1" name="__codelineno-17-1" href="#__codelineno-17-1"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
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</span><span id="__span-17-2"><a id="__codelineno-17-2" name="__codelineno-17-2" href="#__codelineno-17-2"></a> <span class="c1"># ... other methods</span>
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</span><span id="__span-17-3"><a id="__codelineno-17-3" name="__codelineno-17-3" href="#__codelineno-17-3"></a>
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</span><span id="__span-17-4"><a id="__codelineno-17-4" name="__codelineno-17-4" href="#__codelineno-17-4"></a> <span class="nd">@register_callback</span><span class="p">()</span>
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</span><span id="__span-17-5"><a id="__codelineno-17-5" name="__codelineno-17-5" href="#__codelineno-17-5"></a> <span class="k">def</span> <span class="nf">evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">EvaluationConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">EvaluationCallback</span><span class="p">:</span>
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</span><span id="__span-17-6"><a id="__codelineno-17-6" name="__codelineno-17-6" href="#__codelineno-17-6"></a> <span class="k">return</span> <span class="n">EvaluationCallback</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
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</span></code></pre></div>
|
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<p>We can now train the model and see the results in the <code>result_{epoch}.png</code> files.</p>
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<p><img alt="alt text" src="evaluation.png" /></p>
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<h2 id="wrap-up">Wrap up<a class="headerlink" href="#wrap-up" title="Permanent link">¶</a></h2>
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<p>You can train this toy model using the code below:</p>
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<details class="complete end-to-end code">
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<summary>Expand to see the full code.</summary>
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<div class="language-py highlight"><pre><span></span><code><span id="__span-18-1"><a id="__codelineno-18-1" name="__codelineno-18-1" href="#__codelineno-18-1"></a><span class="kn">import</span> <span class="nn">random</span>
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</span><span id="__span-18-2"><a id="__codelineno-18-2" name="__codelineno-18-2" href="#__codelineno-18-2"></a><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span>
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</span><span id="__span-18-3"><a id="__codelineno-18-3" name="__codelineno-18-3" href="#__codelineno-18-3"></a><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Generator</span>
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</span><span id="__span-18-4"><a id="__codelineno-18-4" name="__codelineno-18-4" href="#__codelineno-18-4"></a>
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</span><span id="__span-18-5"><a id="__codelineno-18-5" name="__codelineno-18-5" href="#__codelineno-18-5"></a><span class="kn">import</span> <span class="nn">torch</span>
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</span><span id="__span-18-6"><a id="__codelineno-18-6" name="__codelineno-18-6" href="#__codelineno-18-6"></a><span class="kn">from</span> <span class="nn">loguru</span> <span class="kn">import</span> <span class="n">logger</span>
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</span><span id="__span-18-7"><a id="__codelineno-18-7" name="__codelineno-18-7" href="#__codelineno-18-7"></a><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
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</span><span id="__span-18-8"><a id="__codelineno-18-8" name="__codelineno-18-8" href="#__codelineno-18-8"></a><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
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</span><span id="__span-18-9"><a id="__codelineno-18-9" name="__codelineno-18-9" href="#__codelineno-18-9"></a>
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</span><span id="__span-18-10"><a id="__codelineno-18-10" name="__codelineno-18-10" href="#__codelineno-18-10"></a><span class="kn">from</span> <span class="nn">refiners.fluxion</span> <span class="kn">import</span> <span class="n">layers</span> <span class="k">as</span> <span class="n">fl</span>
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</span><span id="__span-18-11"><a id="__codelineno-18-11" name="__codelineno-18-11" href="#__codelineno-18-11"></a><span class="kn">from</span> <span class="nn">refiners.fluxion.utils</span> <span class="kn">import</span> <span class="n">image_to_tensor</span><span class="p">,</span> <span class="n">tensor_to_image</span>
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</span><span id="__span-18-12"><a id="__codelineno-18-12" name="__codelineno-18-12" href="#__codelineno-18-12"></a><span class="kn">from</span> <span class="nn">refiners.training_utils</span> <span class="kn">import</span> <span class="p">(</span>
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</span><span id="__span-18-13"><a id="__codelineno-18-13" name="__codelineno-18-13" href="#__codelineno-18-13"></a> <span class="n">BaseConfig</span><span class="p">,</span>
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</span><span id="__span-18-14"><a id="__codelineno-18-14" name="__codelineno-18-14" href="#__codelineno-18-14"></a> <span class="n">Callback</span><span class="p">,</span>
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</span><span id="__span-18-15"><a id="__codelineno-18-15" name="__codelineno-18-15" href="#__codelineno-18-15"></a> <span class="n">CallbackConfig</span><span class="p">,</span>
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</span><span id="__span-18-16"><a id="__codelineno-18-16" name="__codelineno-18-16" href="#__codelineno-18-16"></a> <span class="n">ClockConfig</span><span class="p">,</span>
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</span><span id="__span-18-17"><a id="__codelineno-18-17" name="__codelineno-18-17" href="#__codelineno-18-17"></a> <span class="n">Epoch</span><span class="p">,</span>
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</span><span id="__span-18-18"><a id="__codelineno-18-18" name="__codelineno-18-18" href="#__codelineno-18-18"></a> <span class="n">LRSchedulerConfig</span><span class="p">,</span>
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</span><span id="__span-18-19"><a id="__codelineno-18-19" name="__codelineno-18-19" href="#__codelineno-18-19"></a> <span class="n">LRSchedulerType</span><span class="p">,</span>
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</span><span id="__span-18-20"><a id="__codelineno-18-20" name="__codelineno-18-20" href="#__codelineno-18-20"></a> <span class="n">ModelConfig</span><span class="p">,</span>
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</span><span id="__span-18-21"><a id="__codelineno-18-21" name="__codelineno-18-21" href="#__codelineno-18-21"></a> <span class="n">OptimizerConfig</span><span class="p">,</span>
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</span><span id="__span-18-22"><a id="__codelineno-18-22" name="__codelineno-18-22" href="#__codelineno-18-22"></a> <span class="n">Optimizers</span><span class="p">,</span>
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</span><span id="__span-18-23"><a id="__codelineno-18-23" name="__codelineno-18-23" href="#__codelineno-18-23"></a> <span class="n">Trainer</span><span class="p">,</span>
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</span><span id="__span-18-24"><a id="__codelineno-18-24" name="__codelineno-18-24" href="#__codelineno-18-24"></a> <span class="n">TrainingConfig</span><span class="p">,</span>
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</span><span id="__span-18-25"><a id="__codelineno-18-25" name="__codelineno-18-25" href="#__codelineno-18-25"></a> <span class="n">register_callback</span><span class="p">,</span>
|
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</span><span id="__span-18-26"><a id="__codelineno-18-26" name="__codelineno-18-26" href="#__codelineno-18-26"></a> <span class="n">register_model</span><span class="p">,</span>
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</span><span id="__span-18-27"><a id="__codelineno-18-27" name="__codelineno-18-27" href="#__codelineno-18-27"></a><span class="p">)</span>
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</span><span id="__span-18-28"><a id="__codelineno-18-28" name="__codelineno-18-28" href="#__codelineno-18-28"></a><span class="kn">from</span> <span class="nn">refiners.training_utils.common</span> <span class="kn">import</span> <span class="n">scoped_seed</span>
|
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</span><span id="__span-18-29"><a id="__codelineno-18-29" name="__codelineno-18-29" href="#__codelineno-18-29"></a><span class="kn">from</span> <span class="nn">refiners.training_utils.config</span> <span class="kn">import</span> <span class="n">TimeValueField</span>
|
|
</span><span id="__span-18-30"><a id="__codelineno-18-30" name="__codelineno-18-30" href="#__codelineno-18-30"></a>
|
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</span><span id="__span-18-31"><a id="__codelineno-18-31" name="__codelineno-18-31" href="#__codelineno-18-31"></a>
|
|
</span><span id="__span-18-32"><a id="__codelineno-18-32" name="__codelineno-18-32" href="#__codelineno-18-32"></a><span class="k">class</span> <span class="nc">ConvBlock</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-18-33"><a id="__codelineno-18-33" name="__codelineno-18-33" href="#__codelineno-18-33"></a> <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="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-34"><a id="__codelineno-18-34" name="__codelineno-18-34" href="#__codelineno-18-34"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
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</span><span id="__span-18-35"><a id="__codelineno-18-35" name="__codelineno-18-35" href="#__codelineno-18-35"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
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</span><span id="__span-18-36"><a id="__codelineno-18-36" name="__codelineno-18-36" href="#__codelineno-18-36"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
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</span><span id="__span-18-37"><a id="__codelineno-18-37" name="__codelineno-18-37" href="#__codelineno-18-37"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
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</span><span id="__span-18-38"><a id="__codelineno-18-38" name="__codelineno-18-38" href="#__codelineno-18-38"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
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</span><span id="__span-18-39"><a id="__codelineno-18-39" name="__codelineno-18-39" href="#__codelineno-18-39"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
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</span><span id="__span-18-40"><a id="__codelineno-18-40" name="__codelineno-18-40" href="#__codelineno-18-40"></a> <span class="n">groups</span><span class="o">=</span><span class="nb">min</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>
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</span><span id="__span-18-41"><a id="__codelineno-18-41" name="__codelineno-18-41" href="#__codelineno-18-41"></a> <span class="p">),</span>
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</span><span id="__span-18-42"><a id="__codelineno-18-42" name="__codelineno-18-42" href="#__codelineno-18-42"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
|
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</span><span id="__span-18-43"><a id="__codelineno-18-43" name="__codelineno-18-43" href="#__codelineno-18-43"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
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</span><span id="__span-18-44"><a id="__codelineno-18-44" name="__codelineno-18-44" href="#__codelineno-18-44"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
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</span><span id="__span-18-45"><a id="__codelineno-18-45" name="__codelineno-18-45" href="#__codelineno-18-45"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
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</span><span id="__span-18-46"><a id="__codelineno-18-46" name="__codelineno-18-46" href="#__codelineno-18-46"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
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</span><span id="__span-18-47"><a id="__codelineno-18-47" name="__codelineno-18-47" href="#__codelineno-18-47"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
|
|
</span><span id="__span-18-48"><a id="__codelineno-18-48" name="__codelineno-18-48" href="#__codelineno-18-48"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
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</span><span id="__span-18-49"><a id="__codelineno-18-49" name="__codelineno-18-49" href="#__codelineno-18-49"></a> <span class="p">),</span>
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</span><span id="__span-18-50"><a id="__codelineno-18-50" name="__codelineno-18-50" href="#__codelineno-18-50"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
|
|
</span><span id="__span-18-51"><a id="__codelineno-18-51" name="__codelineno-18-51" href="#__codelineno-18-51"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
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</span><span id="__span-18-52"><a id="__codelineno-18-52" name="__codelineno-18-52" href="#__codelineno-18-52"></a> <span class="p">)</span>
|
|
</span><span id="__span-18-53"><a id="__codelineno-18-53" name="__codelineno-18-53" href="#__codelineno-18-53"></a>
|
|
</span><span id="__span-18-54"><a id="__codelineno-18-54" name="__codelineno-18-54" href="#__codelineno-18-54"></a>
|
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</span><span id="__span-18-55"><a id="__codelineno-18-55" name="__codelineno-18-55" href="#__codelineno-18-55"></a><span class="k">class</span> <span class="nc">ResidualBlock</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Sum</span><span class="p">):</span>
|
|
</span><span id="__span-18-56"><a id="__codelineno-18-56" name="__codelineno-18-56" href="#__codelineno-18-56"></a> <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="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-18-57"><a id="__codelineno-18-57" name="__codelineno-18-57" href="#__codelineno-18-57"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
|
</span><span id="__span-18-58"><a id="__codelineno-18-58" name="__codelineno-18-58" href="#__codelineno-18-58"></a> <span class="n">ConvBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">),</span>
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|
</span><span id="__span-18-59"><a id="__codelineno-18-59" name="__codelineno-18-59" href="#__codelineno-18-59"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
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</span><span id="__span-18-60"><a id="__codelineno-18-60" name="__codelineno-18-60" href="#__codelineno-18-60"></a> <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
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|
</span><span id="__span-18-61"><a id="__codelineno-18-61" name="__codelineno-18-61" href="#__codelineno-18-61"></a> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
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</span><span id="__span-18-62"><a id="__codelineno-18-62" name="__codelineno-18-62" href="#__codelineno-18-62"></a> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
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|
</span><span id="__span-18-63"><a id="__codelineno-18-63" name="__codelineno-18-63" href="#__codelineno-18-63"></a> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
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</span><span id="__span-18-64"><a id="__codelineno-18-64" name="__codelineno-18-64" href="#__codelineno-18-64"></a> <span class="p">),</span>
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</span><span id="__span-18-65"><a id="__codelineno-18-65" name="__codelineno-18-65" href="#__codelineno-18-65"></a> <span class="p">)</span>
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</span><span id="__span-18-66"><a id="__codelineno-18-66" name="__codelineno-18-66" href="#__codelineno-18-66"></a>
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</span><span id="__span-18-67"><a id="__codelineno-18-67" name="__codelineno-18-67" href="#__codelineno-18-67"></a>
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|
</span><span id="__span-18-68"><a id="__codelineno-18-68" name="__codelineno-18-68" href="#__codelineno-18-68"></a><span class="k">class</span> <span class="nc">Encoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-18-69"><a id="__codelineno-18-69" name="__codelineno-18-69" href="#__codelineno-18-69"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-70"><a id="__codelineno-18-70" name="__codelineno-18-70" href="#__codelineno-18-70"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
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</span><span id="__span-18-71"><a id="__codelineno-18-71" name="__codelineno-18-71" href="#__codelineno-18-71"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
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</span><span id="__span-18-72"><a id="__codelineno-18-72" name="__codelineno-18-72" href="#__codelineno-18-72"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
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</span><span id="__span-18-73"><a id="__codelineno-18-73" name="__codelineno-18-73" href="#__codelineno-18-73"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
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|
</span><span id="__span-18-74"><a id="__codelineno-18-74" name="__codelineno-18-74" href="#__codelineno-18-74"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
|
|
</span><span id="__span-18-75"><a id="__codelineno-18-75" name="__codelineno-18-75" href="#__codelineno-18-75"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
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|
</span><span id="__span-18-76"><a id="__codelineno-18-76" name="__codelineno-18-76" href="#__codelineno-18-76"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Downsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">register_shape</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
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|
</span><span id="__span-18-77"><a id="__codelineno-18-77" name="__codelineno-18-77" href="#__codelineno-18-77"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="mi">2048</span><span class="p">),</span>
|
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</span><span id="__span-18-78"><a id="__codelineno-18-78" name="__codelineno-18-78" href="#__codelineno-18-78"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
|
|
</span><span id="__span-18-79"><a id="__codelineno-18-79" name="__codelineno-18-79" href="#__codelineno-18-79"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
|
</span><span id="__span-18-80"><a id="__codelineno-18-80" name="__codelineno-18-80" href="#__codelineno-18-80"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
|
|
</span><span id="__span-18-81"><a id="__codelineno-18-81" name="__codelineno-18-81" href="#__codelineno-18-81"></a> <span class="p">)</span>
|
|
</span><span id="__span-18-82"><a id="__codelineno-18-82" name="__codelineno-18-82" href="#__codelineno-18-82"></a>
|
|
</span><span id="__span-18-83"><a id="__codelineno-18-83" name="__codelineno-18-83" href="#__codelineno-18-83"></a>
|
|
</span><span id="__span-18-84"><a id="__codelineno-18-84" name="__codelineno-18-84" href="#__codelineno-18-84"></a><span class="k">class</span> <span class="nc">Decoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
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</span><span id="__span-18-85"><a id="__codelineno-18-85" name="__codelineno-18-85" href="#__codelineno-18-85"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-18-86"><a id="__codelineno-18-86" name="__codelineno-18-86" href="#__codelineno-18-86"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
|
</span><span id="__span-18-87"><a id="__codelineno-18-87" name="__codelineno-18-87" href="#__codelineno-18-87"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">256</span><span class="p">),</span>
|
|
</span><span id="__span-18-88"><a id="__codelineno-18-88" name="__codelineno-18-88" href="#__codelineno-18-88"></a> <span class="n">fl</span><span class="o">.</span><span class="n">SiLU</span><span class="p">(),</span>
|
|
</span><span id="__span-18-89"><a id="__codelineno-18-89" name="__codelineno-18-89" href="#__codelineno-18-89"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="mi">2048</span><span class="p">),</span>
|
|
</span><span id="__span-18-90"><a id="__codelineno-18-90" name="__codelineno-18-90" href="#__codelineno-18-90"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Reshape</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-18-91"><a id="__codelineno-18-91" name="__codelineno-18-91" href="#__codelineno-18-91"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
|
|
</span><span id="__span-18-92"><a id="__codelineno-18-92" name="__codelineno-18-92" href="#__codelineno-18-92"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">),</span>
|
|
</span><span id="__span-18-93"><a id="__codelineno-18-93" name="__codelineno-18-93" href="#__codelineno-18-93"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
|
</span><span id="__span-18-94"><a id="__codelineno-18-94" name="__codelineno-18-94" href="#__codelineno-18-94"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
|
|
</span><span id="__span-18-95"><a id="__codelineno-18-95" name="__codelineno-18-95" href="#__codelineno-18-95"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">),</span>
|
|
</span><span id="__span-18-96"><a id="__codelineno-18-96" name="__codelineno-18-96" href="#__codelineno-18-96"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
|
</span><span id="__span-18-97"><a id="__codelineno-18-97" name="__codelineno-18-97" href="#__codelineno-18-97"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-18-98"><a id="__codelineno-18-98" name="__codelineno-18-98" href="#__codelineno-18-98"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-18-99"><a id="__codelineno-18-99" name="__codelineno-18-99" href="#__codelineno-18-99"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">upsample_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
|
|
</span><span id="__span-18-100"><a id="__codelineno-18-100" name="__codelineno-18-100" href="#__codelineno-18-100"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">),</span>
|
|
</span><span id="__span-18-101"><a id="__codelineno-18-101" name="__codelineno-18-101" href="#__codelineno-18-101"></a> <span class="n">ResidualBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
|
|
</span><span id="__span-18-102"><a id="__codelineno-18-102" name="__codelineno-18-102" href="#__codelineno-18-102"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">(),</span>
|
|
</span><span id="__span-18-103"><a id="__codelineno-18-103" name="__codelineno-18-103" href="#__codelineno-18-103"></a> <span class="p">)</span>
|
|
</span><span id="__span-18-104"><a id="__codelineno-18-104" name="__codelineno-18-104" href="#__codelineno-18-104"></a>
|
|
</span><span id="__span-18-105"><a id="__codelineno-18-105" name="__codelineno-18-105" href="#__codelineno-18-105"></a>
|
|
</span><span id="__span-18-106"><a id="__codelineno-18-106" name="__codelineno-18-106" href="#__codelineno-18-106"></a><span class="k">class</span> <span class="nc">Autoencoder</span><span class="p">(</span><span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">):</span>
|
|
</span><span id="__span-18-107"><a id="__codelineno-18-107" name="__codelineno-18-107" href="#__codelineno-18-107"></a> <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="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
|
</span><span id="__span-18-108"><a id="__codelineno-18-108" name="__codelineno-18-108" href="#__codelineno-18-108"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
|
|
</span><span id="__span-18-109"><a id="__codelineno-18-109" name="__codelineno-18-109" href="#__codelineno-18-109"></a> <span class="n">Encoder</span><span class="p">(),</span>
|
|
</span><span id="__span-18-110"><a id="__codelineno-18-110" name="__codelineno-18-110" href="#__codelineno-18-110"></a> <span class="n">Decoder</span><span class="p">(),</span>
|
|
</span><span id="__span-18-111"><a id="__codelineno-18-111" name="__codelineno-18-111" href="#__codelineno-18-111"></a> <span class="p">)</span>
|
|
</span><span id="__span-18-112"><a id="__codelineno-18-112" name="__codelineno-18-112" href="#__codelineno-18-112"></a>
|
|
</span><span id="__span-18-113"><a id="__codelineno-18-113" name="__codelineno-18-113" href="#__codelineno-18-113"></a> <span class="nd">@property</span>
|
|
</span><span id="__span-18-114"><a id="__codelineno-18-114" name="__codelineno-18-114" href="#__codelineno-18-114"></a> <span class="k">def</span> <span class="nf">encoder</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Encoder</span><span class="p">:</span>
|
|
</span><span id="__span-18-115"><a id="__codelineno-18-115" name="__codelineno-18-115" href="#__codelineno-18-115"></a> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensure_find</span><span class="p">(</span><span class="n">Encoder</span><span class="p">)</span>
|
|
</span><span id="__span-18-116"><a id="__codelineno-18-116" name="__codelineno-18-116" href="#__codelineno-18-116"></a>
|
|
</span><span id="__span-18-117"><a id="__codelineno-18-117" name="__codelineno-18-117" href="#__codelineno-18-117"></a> <span class="nd">@property</span>
|
|
</span><span id="__span-18-118"><a id="__codelineno-18-118" name="__codelineno-18-118" href="#__codelineno-18-118"></a> <span class="k">def</span> <span class="nf">decoder</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Decoder</span><span class="p">:</span>
|
|
</span><span id="__span-18-119"><a id="__codelineno-18-119" name="__codelineno-18-119" href="#__codelineno-18-119"></a> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensure_find</span><span class="p">(</span><span class="n">Decoder</span><span class="p">)</span>
|
|
</span><span id="__span-18-120"><a id="__codelineno-18-120" name="__codelineno-18-120" href="#__codelineno-18-120"></a>
|
|
</span><span id="__span-18-121"><a id="__codelineno-18-121" name="__codelineno-18-121" href="#__codelineno-18-121"></a>
|
|
</span><span id="__span-18-122"><a id="__codelineno-18-122" name="__codelineno-18-122" href="#__codelineno-18-122"></a><span class="k">def</span> <span class="nf">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">Generator</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
|
|
</span><span id="__span-18-123"><a id="__codelineno-18-123" name="__codelineno-18-123" href="#__codelineno-18-123"></a><span class="w"> </span><span class="sd">"""Generate a tensor of a binary mask of size `size` using random rectangles."""</span>
|
|
</span><span id="__span-18-124"><a id="__codelineno-18-124" name="__codelineno-18-124" href="#__codelineno-18-124"></a> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
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</span><span id="__span-18-125"><a id="__codelineno-18-125" name="__codelineno-18-125" href="#__codelineno-18-125"></a> <span class="n">seed</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">**</span><span class="mi">32</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
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</span><span id="__span-18-126"><a id="__codelineno-18-126" name="__codelineno-18-126" href="#__codelineno-18-126"></a> <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
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</span><span id="__span-18-127"><a id="__codelineno-18-127" name="__codelineno-18-127" href="#__codelineno-18-127"></a>
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</span><span id="__span-18-128"><a id="__codelineno-18-128" name="__codelineno-18-128" href="#__codelineno-18-128"></a> <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
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</span><span id="__span-18-129"><a id="__codelineno-18-129" name="__codelineno-18-129" href="#__codelineno-18-129"></a> <span class="n">rectangle</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="s2">"L"</span><span class="p">,</span> <span class="p">(</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="p">),</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="p">)),</span> <span class="n">color</span><span class="o">=</span><span class="mi">255</span><span class="p">)</span>
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</span><span id="__span-18-130"><a id="__codelineno-18-130" name="__codelineno-18-130" href="#__codelineno-18-130"></a> <span class="n">mask</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="s2">"L"</span><span class="p">,</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">))</span>
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</span><span id="__span-18-131"><a id="__codelineno-18-131" name="__codelineno-18-131" href="#__codelineno-18-131"></a> <span class="n">mask</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span>
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</span><span id="__span-18-132"><a id="__codelineno-18-132" name="__codelineno-18-132" href="#__codelineno-18-132"></a> <span class="n">rectangle</span><span class="p">,</span>
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</span><span id="__span-18-133"><a id="__codelineno-18-133" name="__codelineno-18-133" href="#__codelineno-18-133"></a> <span class="p">(</span>
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</span><span id="__span-18-134"><a id="__codelineno-18-134" name="__codelineno-18-134" href="#__codelineno-18-134"></a> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="n">rectangle</span><span class="o">.</span><span class="n">width</span><span class="p">),</span>
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</span><span id="__span-18-135"><a id="__codelineno-18-135" name="__codelineno-18-135" href="#__codelineno-18-135"></a> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">size</span> <span class="o">-</span> <span class="n">rectangle</span><span class="o">.</span><span class="n">height</span><span class="p">),</span>
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</span><span id="__span-18-136"><a id="__codelineno-18-136" name="__codelineno-18-136" href="#__codelineno-18-136"></a> <span class="p">),</span>
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</span><span id="__span-18-137"><a id="__codelineno-18-137" name="__codelineno-18-137" href="#__codelineno-18-137"></a> <span class="p">)</span>
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</span><span id="__span-18-138"><a id="__codelineno-18-138" name="__codelineno-18-138" href="#__codelineno-18-138"></a> <span class="n">tensor</span> <span class="o">=</span> <span class="n">image_to_tensor</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
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</span><span id="__span-18-139"><a id="__codelineno-18-139" name="__codelineno-18-139" href="#__codelineno-18-139"></a>
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</span><span id="__span-18-140"><a id="__codelineno-18-140" name="__codelineno-18-140" href="#__codelineno-18-140"></a> <span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span>
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</span><span id="__span-18-141"><a id="__codelineno-18-141" name="__codelineno-18-141" href="#__codelineno-18-141"></a> <span class="n">tensor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">tensor</span>
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</span><span id="__span-18-142"><a id="__codelineno-18-142" name="__codelineno-18-142" href="#__codelineno-18-142"></a>
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</span><span id="__span-18-143"><a id="__codelineno-18-143" name="__codelineno-18-143" href="#__codelineno-18-143"></a> <span class="k">yield</span> <span class="n">tensor</span>
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</span><span id="__span-18-144"><a id="__codelineno-18-144" name="__codelineno-18-144" href="#__codelineno-18-144"></a>
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</span><span id="__span-18-145"><a id="__codelineno-18-145" name="__codelineno-18-145" href="#__codelineno-18-145"></a>
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</span><span id="__span-18-146"><a id="__codelineno-18-146" name="__codelineno-18-146" href="#__codelineno-18-146"></a><span class="nd">@dataclass</span>
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</span><span id="__span-18-147"><a id="__codelineno-18-147" name="__codelineno-18-147" href="#__codelineno-18-147"></a><span class="k">class</span> <span class="nc">Batch</span><span class="p">:</span>
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</span><span id="__span-18-148"><a id="__codelineno-18-148" name="__codelineno-18-148" href="#__codelineno-18-148"></a> <span class="n">image</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
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</span><span id="__span-18-149"><a id="__codelineno-18-149" name="__codelineno-18-149" href="#__codelineno-18-149"></a>
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</span><span id="__span-18-150"><a id="__codelineno-18-150" name="__codelineno-18-150" href="#__codelineno-18-150"></a>
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</span><span id="__span-18-151"><a id="__codelineno-18-151" name="__codelineno-18-151" href="#__codelineno-18-151"></a><span class="k">class</span> <span class="nc">AutoencoderModelConfig</span><span class="p">(</span><span class="n">ModelConfig</span><span class="p">):</span>
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</span><span id="__span-18-152"><a id="__codelineno-18-152" name="__codelineno-18-152" href="#__codelineno-18-152"></a> <span class="k">pass</span>
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</span><span id="__span-18-153"><a id="__codelineno-18-153" name="__codelineno-18-153" href="#__codelineno-18-153"></a>
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</span><span id="__span-18-154"><a id="__codelineno-18-154" name="__codelineno-18-154" href="#__codelineno-18-154"></a>
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</span><span id="__span-18-155"><a id="__codelineno-18-155" name="__codelineno-18-155" href="#__codelineno-18-155"></a><span class="k">class</span> <span class="nc">LoggingCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">[</span><span class="n">Trainer</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]):</span>
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</span><span id="__span-18-156"><a id="__codelineno-18-156" name="__codelineno-18-156" href="#__codelineno-18-156"></a> <span class="n">losses</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-18-157"><a id="__codelineno-18-157" name="__codelineno-18-157" href="#__codelineno-18-157"></a>
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</span><span id="__span-18-158"><a id="__codelineno-18-158" name="__codelineno-18-158" href="#__codelineno-18-158"></a> <span class="k">def</span> <span class="nf">on_compute_loss_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">:</span> <span class="n">Trainer</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-159"><a id="__codelineno-18-159" name="__codelineno-18-159" href="#__codelineno-18-159"></a> <span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">loss</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
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</span><span id="__span-18-160"><a id="__codelineno-18-160" name="__codelineno-18-160" href="#__codelineno-18-160"></a>
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</span><span id="__span-18-161"><a id="__codelineno-18-161" name="__codelineno-18-161" href="#__codelineno-18-161"></a> <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">:</span> <span class="n">Trainer</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-162"><a id="__codelineno-18-162" name="__codelineno-18-162" href="#__codelineno-18-162"></a> <span class="n">mean_loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span>
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</span><span id="__span-18-163"><a id="__codelineno-18-163" name="__codelineno-18-163" href="#__codelineno-18-163"></a> <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean loss: </span><span class="si">{</span><span class="n">mean_loss</span><span class="si">}</span><span class="s2">, epoch: </span><span class="si">{</span><span class="n">trainer</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">epoch</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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</span><span id="__span-18-164"><a id="__codelineno-18-164" name="__codelineno-18-164" href="#__codelineno-18-164"></a> <span class="bp">self</span><span class="o">.</span><span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-18-165"><a id="__codelineno-18-165" name="__codelineno-18-165" href="#__codelineno-18-165"></a>
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</span><span id="__span-18-166"><a id="__codelineno-18-166" name="__codelineno-18-166" href="#__codelineno-18-166"></a>
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</span><span id="__span-18-167"><a id="__codelineno-18-167" name="__codelineno-18-167" href="#__codelineno-18-167"></a><span class="k">class</span> <span class="nc">EvaluationConfig</span><span class="p">(</span><span class="n">CallbackConfig</span><span class="p">):</span>
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</span><span id="__span-18-168"><a id="__codelineno-18-168" name="__codelineno-18-168" href="#__codelineno-18-168"></a> <span class="n">interval</span><span class="p">:</span> <span class="n">TimeValueField</span>
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</span><span id="__span-18-169"><a id="__codelineno-18-169" name="__codelineno-18-169" href="#__codelineno-18-169"></a> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span>
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</span><span id="__span-18-170"><a id="__codelineno-18-170" name="__codelineno-18-170" href="#__codelineno-18-170"></a>
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</span><span id="__span-18-171"><a id="__codelineno-18-171" name="__codelineno-18-171" href="#__codelineno-18-171"></a>
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</span><span id="__span-18-172"><a id="__codelineno-18-172" name="__codelineno-18-172" href="#__codelineno-18-172"></a><span class="k">class</span> <span class="nc">EvaluationCallback</span><span class="p">(</span><span class="n">Callback</span><span class="p">[</span><span class="s2">"AutoencoderTrainer"</span><span class="p">]):</span>
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</span><span id="__span-18-173"><a id="__codelineno-18-173" name="__codelineno-18-173" href="#__codelineno-18-173"></a> <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="n">config</span><span class="p">:</span> <span class="n">EvaluationConfig</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-174"><a id="__codelineno-18-174" name="__codelineno-18-174" href="#__codelineno-18-174"></a> <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span>
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</span><span id="__span-18-175"><a id="__codelineno-18-175" name="__codelineno-18-175" href="#__codelineno-18-175"></a>
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</span><span id="__span-18-176"><a id="__codelineno-18-176" name="__codelineno-18-176" href="#__codelineno-18-176"></a> <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trainer</span><span class="p">:</span> <span class="s2">"AutoencoderTrainer"</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-177"><a id="__codelineno-18-177" name="__codelineno-18-177" href="#__codelineno-18-177"></a> <span class="c1"># The `is_due` method checks if the current epoch is a multiple of the interval.</span>
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</span><span id="__span-18-178"><a id="__codelineno-18-178" name="__codelineno-18-178" href="#__codelineno-18-178"></a> <span class="k">if</span> <span class="ow">not</span> <span class="n">trainer</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">is_due</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">interval</span><span class="p">):</span>
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</span><span id="__span-18-179"><a id="__codelineno-18-179" name="__codelineno-18-179" href="#__codelineno-18-179"></a> <span class="k">return</span>
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</span><span id="__span-18-180"><a id="__codelineno-18-180" name="__codelineno-18-180" href="#__codelineno-18-180"></a>
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</span><span id="__span-18-181"><a id="__codelineno-18-181" name="__codelineno-18-181" href="#__codelineno-18-181"></a> <span class="c1"># The `scoped_seed` context manager encapsulates the random state for the evaluation and restores it after the</span>
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</span><span id="__span-18-182"><a id="__codelineno-18-182" name="__codelineno-18-182" href="#__codelineno-18-182"></a> <span class="c1"># evaluation.</span>
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</span><span id="__span-18-183"><a id="__codelineno-18-183" name="__codelineno-18-183" href="#__codelineno-18-183"></a> <span class="k">with</span> <span class="n">scoped_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">seed</span><span class="p">):</span>
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</span><span id="__span-18-184"><a id="__codelineno-18-184" name="__codelineno-18-184" href="#__codelineno-18-184"></a> <span class="n">trainer</span><span class="o">.</span><span class="n">compute_evaluation</span><span class="p">()</span>
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</span><span id="__span-18-185"><a id="__codelineno-18-185" name="__codelineno-18-185" href="#__codelineno-18-185"></a>
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</span><span id="__span-18-186"><a id="__codelineno-18-186" name="__codelineno-18-186" href="#__codelineno-18-186"></a>
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</span><span id="__span-18-187"><a id="__codelineno-18-187" name="__codelineno-18-187" href="#__codelineno-18-187"></a><span class="k">class</span> <span class="nc">AutoencoderConfig</span><span class="p">(</span><span class="n">BaseConfig</span><span class="p">):</span>
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</span><span id="__span-18-188"><a id="__codelineno-18-188" name="__codelineno-18-188" href="#__codelineno-18-188"></a> <span class="n">num_images</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span>
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</span><span id="__span-18-189"><a id="__codelineno-18-189" name="__codelineno-18-189" href="#__codelineno-18-189"></a> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span>
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</span><span id="__span-18-190"><a id="__codelineno-18-190" name="__codelineno-18-190" href="#__codelineno-18-190"></a> <span class="n">autoencoder</span><span class="p">:</span> <span class="n">AutoencoderModelConfig</span>
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</span><span id="__span-18-191"><a id="__codelineno-18-191" name="__codelineno-18-191" href="#__codelineno-18-191"></a> <span class="n">evaluation</span><span class="p">:</span> <span class="n">EvaluationConfig</span>
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</span><span id="__span-18-192"><a id="__codelineno-18-192" name="__codelineno-18-192" href="#__codelineno-18-192"></a> <span class="n">logging</span><span class="p">:</span> <span class="n">CallbackConfig</span> <span class="o">=</span> <span class="n">CallbackConfig</span><span class="p">()</span>
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</span><span id="__span-18-193"><a id="__codelineno-18-193" name="__codelineno-18-193" href="#__codelineno-18-193"></a>
|
|
</span><span id="__span-18-194"><a id="__codelineno-18-194" name="__codelineno-18-194" href="#__codelineno-18-194"></a>
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|
</span><span id="__span-18-195"><a id="__codelineno-18-195" name="__codelineno-18-195" href="#__codelineno-18-195"></a><span class="n">autoencoder_config</span> <span class="o">=</span> <span class="n">AutoencoderModelConfig</span><span class="p">(</span>
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</span><span id="__span-18-196"><a id="__codelineno-18-196" name="__codelineno-18-196" href="#__codelineno-18-196"></a> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># set during registration to set the requires_grad attribute of the model.</span>
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</span><span id="__span-18-197"><a id="__codelineno-18-197" name="__codelineno-18-197" href="#__codelineno-18-197"></a><span class="p">)</span>
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|
</span><span id="__span-18-198"><a id="__codelineno-18-198" name="__codelineno-18-198" href="#__codelineno-18-198"></a>
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</span><span id="__span-18-199"><a id="__codelineno-18-199" name="__codelineno-18-199" href="#__codelineno-18-199"></a><span class="n">training</span> <span class="o">=</span> <span class="n">TrainingConfig</span><span class="p">(</span>
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|
</span><span id="__span-18-200"><a id="__codelineno-18-200" name="__codelineno-18-200" href="#__codelineno-18-200"></a> <span class="n">duration</span><span class="o">=</span><span class="n">Epoch</span><span class="p">(</span><span class="mi">200</span><span class="p">),</span>
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</span><span id="__span-18-201"><a id="__codelineno-18-201" name="__codelineno-18-201" href="#__codelineno-18-201"></a> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">,</span>
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</span><span id="__span-18-202"><a id="__codelineno-18-202" name="__codelineno-18-202" href="#__codelineno-18-202"></a> <span class="n">dtype</span><span class="o">=</span><span class="s2">"float32"</span><span class="p">,</span>
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</span><span id="__span-18-203"><a id="__codelineno-18-203" name="__codelineno-18-203" href="#__codelineno-18-203"></a><span class="p">)</span>
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</span><span id="__span-18-204"><a id="__codelineno-18-204" name="__codelineno-18-204" href="#__codelineno-18-204"></a>
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</span><span id="__span-18-205"><a id="__codelineno-18-205" name="__codelineno-18-205" href="#__codelineno-18-205"></a><span class="n">optimizer</span> <span class="o">=</span> <span class="n">OptimizerConfig</span><span class="p">(</span>
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</span><span id="__span-18-206"><a id="__codelineno-18-206" name="__codelineno-18-206" href="#__codelineno-18-206"></a> <span class="n">optimizer</span><span class="o">=</span><span class="n">Optimizers</span><span class="o">.</span><span class="n">AdamW</span><span class="p">,</span>
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</span><span id="__span-18-207"><a id="__codelineno-18-207" name="__codelineno-18-207" href="#__codelineno-18-207"></a> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span>
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</span><span id="__span-18-208"><a id="__codelineno-18-208" name="__codelineno-18-208" href="#__codelineno-18-208"></a><span class="p">)</span>
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</span><span id="__span-18-209"><a id="__codelineno-18-209" name="__codelineno-18-209" href="#__codelineno-18-209"></a>
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</span><span id="__span-18-210"><a id="__codelineno-18-210" name="__codelineno-18-210" href="#__codelineno-18-210"></a><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">LRSchedulerConfig</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">LRSchedulerType</span><span class="o">.</span><span class="n">CONSTANT_LR</span><span class="p">)</span>
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</span><span id="__span-18-211"><a id="__codelineno-18-211" name="__codelineno-18-211" href="#__codelineno-18-211"></a>
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</span><span id="__span-18-212"><a id="__codelineno-18-212" name="__codelineno-18-212" href="#__codelineno-18-212"></a><span class="n">config</span> <span class="o">=</span> <span class="n">AutoencoderConfig</span><span class="p">(</span>
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</span><span id="__span-18-213"><a id="__codelineno-18-213" name="__codelineno-18-213" href="#__codelineno-18-213"></a> <span class="n">training</span><span class="o">=</span><span class="n">training</span><span class="p">,</span>
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</span><span id="__span-18-214"><a id="__codelineno-18-214" name="__codelineno-18-214" href="#__codelineno-18-214"></a> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
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</span><span id="__span-18-215"><a id="__codelineno-18-215" name="__codelineno-18-215" href="#__codelineno-18-215"></a> <span class="n">lr_scheduler</span><span class="o">=</span><span class="n">lr_scheduler</span><span class="p">,</span>
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</span><span id="__span-18-216"><a id="__codelineno-18-216" name="__codelineno-18-216" href="#__codelineno-18-216"></a> <span class="n">autoencoder</span><span class="o">=</span><span class="n">autoencoder_config</span><span class="p">,</span>
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</span><span id="__span-18-217"><a id="__codelineno-18-217" name="__codelineno-18-217" href="#__codelineno-18-217"></a> <span class="n">evaluation</span><span class="o">=</span><span class="n">EvaluationConfig</span><span class="p">(</span><span class="n">interval</span><span class="o">=</span><span class="n">Epoch</span><span class="p">(</span><span class="mi">50</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
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</span><span id="__span-18-218"><a id="__codelineno-18-218" name="__codelineno-18-218" href="#__codelineno-18-218"></a> <span class="n">clock</span><span class="o">=</span><span class="n">ClockConfig</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="c1"># to disable the default clock logging</span>
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</span><span id="__span-18-219"><a id="__codelineno-18-219" name="__codelineno-18-219" href="#__codelineno-18-219"></a><span class="p">)</span>
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</span><span id="__span-18-220"><a id="__codelineno-18-220" name="__codelineno-18-220" href="#__codelineno-18-220"></a>
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</span><span id="__span-18-221"><a id="__codelineno-18-221" name="__codelineno-18-221" href="#__codelineno-18-221"></a>
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</span><span id="__span-18-222"><a id="__codelineno-18-222" name="__codelineno-18-222" href="#__codelineno-18-222"></a><span class="k">class</span> <span class="nc">AutoencoderTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">[</span><span class="n">AutoencoderConfig</span><span class="p">,</span> <span class="n">Batch</span><span class="p">]):</span>
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</span><span id="__span-18-223"><a id="__codelineno-18-223" name="__codelineno-18-223" href="#__codelineno-18-223"></a> <span class="k">def</span> <span class="nf">create_data_iterable</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">[</span><span class="n">Batch</span><span class="p">]:</span>
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</span><span id="__span-18-224"><a id="__codelineno-18-224" name="__codelineno-18-224" href="#__codelineno-18-224"></a> <span class="n">dataset</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Batch</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-18-225"><a id="__codelineno-18-225" name="__codelineno-18-225" href="#__codelineno-18-225"></a> <span class="n">generator</span> <span class="o">=</span> <span class="n">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">64</span><span class="p">)</span>
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</span><span id="__span-18-226"><a id="__codelineno-18-226" name="__codelineno-18-226" href="#__codelineno-18-226"></a>
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</span><span id="__span-18-227"><a id="__codelineno-18-227" name="__codelineno-18-227" href="#__codelineno-18-227"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">num_images</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">):</span>
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</span><span id="__span-18-228"><a id="__codelineno-18-228" name="__codelineno-18-228" href="#__codelineno-18-228"></a> <span class="n">masks</span> <span class="o">=</span> <span class="p">[</span><span class="nb">next</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="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)]</span>
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</span><span id="__span-18-229"><a id="__codelineno-18-229" name="__codelineno-18-229" href="#__codelineno-18-229"></a> <span class="n">dataset</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Batch</span><span class="p">(</span><span class="n">image</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">masks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span>
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</span><span id="__span-18-230"><a id="__codelineno-18-230" name="__codelineno-18-230" href="#__codelineno-18-230"></a>
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</span><span id="__span-18-231"><a id="__codelineno-18-231" name="__codelineno-18-231" href="#__codelineno-18-231"></a> <span class="k">return</span> <span class="n">dataset</span>
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</span><span id="__span-18-232"><a id="__codelineno-18-232" name="__codelineno-18-232" href="#__codelineno-18-232"></a>
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</span><span id="__span-18-233"><a id="__codelineno-18-233" name="__codelineno-18-233" href="#__codelineno-18-233"></a> <span class="nd">@register_model</span><span class="p">()</span>
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</span><span id="__span-18-234"><a id="__codelineno-18-234" name="__codelineno-18-234" href="#__codelineno-18-234"></a> <span class="k">def</span> <span class="nf">autoencoder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">AutoencoderModelConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">Autoencoder</span><span class="p">:</span>
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</span><span id="__span-18-235"><a id="__codelineno-18-235" name="__codelineno-18-235" href="#__codelineno-18-235"></a> <span class="k">return</span> <span class="n">Autoencoder</span><span class="p">()</span>
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</span><span id="__span-18-236"><a id="__codelineno-18-236" name="__codelineno-18-236" href="#__codelineno-18-236"></a>
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</span><span id="__span-18-237"><a id="__codelineno-18-237" name="__codelineno-18-237" href="#__codelineno-18-237"></a> <span class="k">def</span> <span class="nf">compute_loss</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">Batch</span><span class="p">)</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
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</span><span id="__span-18-238"><a id="__codelineno-18-238" name="__codelineno-18-238" href="#__codelineno-18-238"></a> <span class="n">batch</span><span class="o">.</span><span class="n">image</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">image</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> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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</span><span id="__span-18-239"><a id="__codelineno-18-239" name="__codelineno-18-239" href="#__codelineno-18-239"></a> <span class="n">x_reconstructed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">image</span><span class="p">))</span>
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</span><span id="__span-18-240"><a id="__codelineno-18-240" name="__codelineno-18-240" href="#__codelineno-18-240"></a> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">x_reconstructed</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">image</span><span class="p">)</span>
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</span><span id="__span-18-241"><a id="__codelineno-18-241" name="__codelineno-18-241" href="#__codelineno-18-241"></a>
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</span><span id="__span-18-242"><a id="__codelineno-18-242" name="__codelineno-18-242" href="#__codelineno-18-242"></a> <span class="k">def</span> <span class="nf">compute_evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
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</span><span id="__span-18-243"><a id="__codelineno-18-243" name="__codelineno-18-243" href="#__codelineno-18-243"></a> <span class="n">generator</span> <span class="o">=</span> <span class="n">generate_mask</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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</span><span id="__span-18-244"><a id="__codelineno-18-244" name="__codelineno-18-244" href="#__codelineno-18-244"></a>
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</span><span id="__span-18-245"><a id="__codelineno-18-245" name="__codelineno-18-245" href="#__codelineno-18-245"></a> <span class="n">grid</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="n">Image</span><span class="o">.</span><span class="n">Image</span><span class="p">,</span> <span class="n">Image</span><span class="o">.</span><span class="n">Image</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-18-246"><a id="__codelineno-18-246" name="__codelineno-18-246" href="#__codelineno-18-246"></a> <span class="n">validation_losses</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
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</span><span id="__span-18-247"><a id="__codelineno-18-247" name="__codelineno-18-247" href="#__codelineno-18-247"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
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</span><span id="__span-18-248"><a id="__codelineno-18-248" name="__codelineno-18-248" href="#__codelineno-18-248"></a> <span class="n">mask</span> <span class="o">=</span> <span class="nb">next</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="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
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</span><span id="__span-18-249"><a id="__codelineno-18-249" name="__codelineno-18-249" href="#__codelineno-18-249"></a> <span class="n">x_reconstructed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">decoder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">autoencoder</span><span class="o">.</span><span class="n">encoder</span><span class="p">(</span><span class="n">mask</span><span class="p">))</span>
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</span><span id="__span-18-250"><a id="__codelineno-18-250" name="__codelineno-18-250" href="#__codelineno-18-250"></a> <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">x_reconstructed</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
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</span><span id="__span-18-251"><a id="__codelineno-18-251" name="__codelineno-18-251" href="#__codelineno-18-251"></a> <span class="n">validation_losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
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</span><span id="__span-18-252"><a id="__codelineno-18-252" name="__codelineno-18-252" href="#__codelineno-18-252"></a> <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">tensor_to_image</span><span class="p">(</span><span class="n">mask</span><span class="p">),</span> <span class="n">tensor_to_image</span><span class="p">((</span><span class="n">x_reconstructed</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">())))</span>
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</span><span id="__span-18-253"><a id="__codelineno-18-253" name="__codelineno-18-253" href="#__codelineno-18-253"></a>
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</span><span id="__span-18-254"><a id="__codelineno-18-254" name="__codelineno-18-254" href="#__codelineno-18-254"></a> <span class="n">mean_loss</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">validation_losses</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">validation_losses</span><span class="p">)</span>
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</span><span id="__span-18-255"><a id="__codelineno-18-255" name="__codelineno-18-255" href="#__codelineno-18-255"></a> <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean validation loss: </span><span class="si">{</span><span class="n">mean_loss</span><span class="si">}</span><span class="s2">, epoch: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">epoch</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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</span><span id="__span-18-256"><a id="__codelineno-18-256" name="__codelineno-18-256" href="#__codelineno-18-256"></a>
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</span><span id="__span-18-257"><a id="__codelineno-18-257" name="__codelineno-18-257" href="#__codelineno-18-257"></a> <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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</span><span id="__span-18-258"><a id="__codelineno-18-258" name="__codelineno-18-258" href="#__codelineno-18-258"></a>
|
|
</span><span id="__span-18-259"><a id="__codelineno-18-259" name="__codelineno-18-259" href="#__codelineno-18-259"></a> <span class="n">_</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</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="n">figsize</span><span class="o">=</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="c1"># type: ignore</span>
|
|
</span><span id="__span-18-260"><a id="__codelineno-18-260" name="__codelineno-18-260" href="#__codelineno-18-260"></a>
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</span><span id="__span-18-261"><a id="__codelineno-18-261" name="__codelineno-18-261" href="#__codelineno-18-261"></a> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">reconstructed</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">grid</span><span class="p">):</span>
|
|
</span><span id="__span-18-262"><a id="__codelineno-18-262" name="__codelineno-18-262" href="#__codelineno-18-262"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"gray"</span><span class="p">)</span>
|
|
</span><span id="__span-18-263"><a id="__codelineno-18-263" name="__codelineno-18-263" href="#__codelineno-18-263"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
|
|
</span><span id="__span-18-264"><a id="__codelineno-18-264" name="__codelineno-18-264" href="#__codelineno-18-264"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Mask"</span><span class="p">)</span>
|
|
</span><span id="__span-18-265"><a id="__codelineno-18-265" name="__codelineno-18-265" href="#__codelineno-18-265"></a>
|
|
</span><span id="__span-18-266"><a id="__codelineno-18-266" name="__codelineno-18-266" href="#__codelineno-18-266"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">reconstructed</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"gray"</span><span class="p">)</span>
|
|
</span><span id="__span-18-267"><a id="__codelineno-18-267" name="__codelineno-18-267" href="#__codelineno-18-267"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s2">"off"</span><span class="p">)</span>
|
|
</span><span id="__span-18-268"><a id="__codelineno-18-268" name="__codelineno-18-268" href="#__codelineno-18-268"></a> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Reconstructed"</span><span class="p">)</span>
|
|
</span><span id="__span-18-269"><a id="__codelineno-18-269" name="__codelineno-18-269" href="#__codelineno-18-269"></a>
|
|
</span><span id="__span-18-270"><a id="__codelineno-18-270" name="__codelineno-18-270" href="#__codelineno-18-270"></a> <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span> <span class="c1"># type: ignore</span>
|
|
</span><span id="__span-18-271"><a id="__codelineno-18-271" name="__codelineno-18-271" href="#__codelineno-18-271"></a> <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="sa">f</span><span class="s2">"result_</span><span class="si">{</span><span class="n">trainer</span><span class="o">.</span><span class="n">clock</span><span class="o">.</span><span class="n">epoch</span><span class="si">}</span><span class="s2">.png"</span><span class="p">)</span> <span class="c1"># type: ignore</span>
|
|
</span><span id="__span-18-272"><a id="__codelineno-18-272" name="__codelineno-18-272" href="#__codelineno-18-272"></a> <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> <span class="c1"># type: ignore</span>
|
|
</span><span id="__span-18-273"><a id="__codelineno-18-273" name="__codelineno-18-273" href="#__codelineno-18-273"></a>
|
|
</span><span id="__span-18-274"><a id="__codelineno-18-274" name="__codelineno-18-274" href="#__codelineno-18-274"></a> <span class="nd">@register_callback</span><span class="p">()</span>
|
|
</span><span id="__span-18-275"><a id="__codelineno-18-275" name="__codelineno-18-275" href="#__codelineno-18-275"></a> <span class="k">def</span> <span class="nf">evaluation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">EvaluationConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">EvaluationCallback</span><span class="p">:</span>
|
|
</span><span id="__span-18-276"><a id="__codelineno-18-276" name="__codelineno-18-276" href="#__codelineno-18-276"></a> <span class="k">return</span> <span class="n">EvaluationCallback</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
|
|
</span><span id="__span-18-277"><a id="__codelineno-18-277" name="__codelineno-18-277" href="#__codelineno-18-277"></a>
|
|
</span><span id="__span-18-278"><a id="__codelineno-18-278" name="__codelineno-18-278" href="#__codelineno-18-278"></a> <span class="nd">@register_callback</span><span class="p">()</span>
|
|
</span><span id="__span-18-279"><a id="__codelineno-18-279" name="__codelineno-18-279" href="#__codelineno-18-279"></a> <span class="k">def</span> <span class="nf">logging</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">CallbackConfig</span><span class="p">)</span> <span class="o">-></span> <span class="n">LoggingCallback</span><span class="p">:</span>
|
|
</span><span id="__span-18-280"><a id="__codelineno-18-280" name="__codelineno-18-280" href="#__codelineno-18-280"></a> <span class="k">return</span> <span class="n">LoggingCallback</span><span class="p">()</span>
|
|
</span><span id="__span-18-281"><a id="__codelineno-18-281" name="__codelineno-18-281" href="#__codelineno-18-281"></a>
|
|
</span><span id="__span-18-282"><a id="__codelineno-18-282" name="__codelineno-18-282" href="#__codelineno-18-282"></a>
|
|
</span><span id="__span-18-283"><a id="__codelineno-18-283" name="__codelineno-18-283" href="#__codelineno-18-283"></a><span class="n">trainer</span> <span class="o">=</span> <span class="n">AutoencoderTrainer</span><span class="p">(</span><span class="n">config</span><span class="p">)</span>
|
|
</span><span id="__span-18-284"><a id="__codelineno-18-284" name="__codelineno-18-284" href="#__codelineno-18-284"></a>
|
|
</span><span id="__span-18-285"><a id="__codelineno-18-285" name="__codelineno-18-285" href="#__codelineno-18-285"></a><span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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</span></code></pre></div>
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</details>
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