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<div class="highlight"><pre><span class="lineno">3</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">4</span><span class="kn">import</span> <span class="nn">torchvision</span>
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<span class="lineno">5</span><span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
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<span class="lineno">6</span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">random_split</span>
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<span class="lineno">7</span><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 class="lineno">8</span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span></pre></div>
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<a href='#section-1'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">10</span><span class="k">def</span> <span class="nf">LoadCifar10DatasetTrain</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
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<span class="lineno">11</span> <span class="n">trainset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="n">save</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
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<span class="lineno">12</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
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<span class="lineno">13</span> <span class="k">return</span> <span class="n">trainset</span></pre></div>
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<a href='#section-2'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">15</span><span class="k">def</span> <span class="nf">LoadCifar10DatasetTest</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">transform</span><span class="p">):</span>
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<span class="lineno">16</span> <span class="k">return</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="n">save</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
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<span class="lineno">17</span> <span class="n">download</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span></pre></div>
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<div class='section' id='section-3'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-3'>#</a>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">19</span><span class="k">def</span> <span class="nf">GetCustTransform</span><span class="p">():</span>
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<span class="lineno">20</span> <span class="n">transform_train</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
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<span class="lineno">21</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomRotation</span><span class="p">(</span><span class="mi">20</span><span class="p">),</span>
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<span class="lineno">22</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomCrop</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">pad_if_needed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding_mode</span><span class="o">=</span><span class="s1">'constant'</span><span class="p">),</span>
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<span class="lineno">23</span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
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<span class="lineno">24</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))])</span>
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<span class="lineno">25</span> <span class="k">return</span> <span class="n">transform_train</span></pre></div>
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</div>
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<div class='section' id='section-4'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-4'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">27</span><span class="k">def</span> <span class="nf">Dataloader_train_valid</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-5'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-5'>#</a>
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</div>
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<p>See utils/dataloader.py for data augmentations</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">30</span> <span class="n">transform_train_valid</span> <span class="o">=</span> <span class="n">GetCustTransform</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-6'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-6'>#</a>
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</div>
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<p>Get Cifar 10 Datasets</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">33</span> <span class="n">trainset</span> <span class="o">=</span> <span class="n">LoadCifar10DatasetTrain</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">transform_train_valid</span><span class="p">)</span>
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<span class="lineno">34</span> <span class="n">train_val_abs</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">)</span>
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<span class="lineno">35</span> <span class="n">train_subset</span><span class="p">,</span> <span class="n">val_subset</span> <span class="o">=</span> <span class="n">random_split</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="p">[</span><span class="n">train_val_abs</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainset</span><span class="p">)</span> <span class="o">-</span> <span class="n">train_val_abs</span><span class="p">])</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-7'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-7'>#</a>
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</div>
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<p>Get Cifar 10 Dataloaders</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">38</span> <span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_subset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
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<span class="lineno">39</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<span class="lineno">40</span>
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<span class="lineno">41</span> <span class="n">valloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">val_subset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
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<span class="lineno">42</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<span class="lineno">43</span> <span class="k">return</span> <span class="n">trainloader</span><span class="p">,</span> <span class="n">valloader</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-8'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-8'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">45</span><span class="k">def</span> <span class="nf">Dataloader_train</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-9'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-9'>#</a>
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</div>
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<p>See utils/dataloader.py for data augmentations</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">48</span> <span class="n">transform_train</span> <span class="o">=</span> <span class="n">GetCustTransform</span><span class="p">()</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-10'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-10'>#</a>
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</div>
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<p>Get Cifar 10 Datasets</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">51</span> <span class="n">trainset</span> <span class="o">=</span> <span class="n">LoadCifar10DatasetTrain</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">transform_train</span><span class="p">)</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-11'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-11'>#</a>
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</div>
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<p>Get Cifar 10 Dataloaders</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">53</span> <span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
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<span class="lineno">54</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<span class="lineno">55</span>
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<span class="lineno">56</span> <span class="k">return</span> <span class="n">trainloader</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-12'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-12'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">58</span><span class="k">def</span> <span class="nf">Dataloader_test</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-13'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-13'>#</a>
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</div>
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<p>transformation test set</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">61</span> <span class="n">transform_test</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
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<span class="lineno">62</span> <span class="p">[</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
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<span class="lineno">63</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))])</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-14'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-14'>#</a>
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</div>
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<p>initialize test dataset and dataloader</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">66</span> <span class="n">testset</span> <span class="o">=</span> <span class="n">LoadCifar10DatasetTest</span><span class="p">(</span><span class="n">save</span><span class="p">,</span> <span class="n">transform_test</span><span class="p">)</span>
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<span class="lineno">67</span> <span class="n">testloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">testset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
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<span class="lineno">68</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<span class="lineno">69</span>
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<span class="lineno">70</span> <span class="k">return</span> <span class="n">testloader</span></pre></div>
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<div class='section' id='section-15'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-15'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">72</span><span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">im</span><span class="p">):</span>
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<span class="lineno">73</span> <span class="n">image</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
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<span class="lineno">74</span> <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
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<span class="lineno">75</span> <span class="n">image</span> <span class="o">=</span> <span class="n">image</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span> <span class="c1"># unnormalize</span>
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<span class="lineno">76</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
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<span class="lineno">77</span> <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></pre></div>
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<div class='section' id='section-16'>
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<div class='docs'>
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<div class='section-link'>
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<a href='#section-16'>#</a>
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</div>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">79</span><span class="k">def</span> <span class="nf">imretrun</span><span class="p">(</span><span class="n">im</span><span class="p">):</span>
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<span class="lineno">80</span> <span class="n">image</span> <span class="o">=</span> <span class="n">im</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
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<span class="lineno">81</span> <span class="n">image</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
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<span class="lineno">82</span> <span class="n">image</span> <span class="o">=</span> <span class="n">image</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span> <span class="c1"># unnormalize</span>
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<span class="lineno">83</span> <span class="k">return</span> <span class="n">image</span></pre></div>
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