mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-08-14 09:31:42 +08:00
vit
This commit is contained in:
Varuna Jayasiri
@ -95,6 +95,7 @@ implementations.</p>
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<li><a href="transformers/mlm/index.html">Masked Language Model</a></li>
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<li><a href="transformers/mlp_mixer/index.html">MLP-Mixer: An all-MLP Architecture for Vision</a></li>
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<li><a href="transformers/gmlp/index.html">Pay Attention to MLPs (gMLP)</a></li>
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<li><a href="transformers/vit/index.html">Vision Transformer (ViT)</a></li>
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</ul>
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<h4>✨ <a href="recurrent_highway_networks/index.html">Recurrent Highway Networks</a></h4>
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<h4>✨ <a href="lstm/index.html">LSTM</a></h4>
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@ -117,12 +117,15 @@ It does single GPU training but we implement the concept of switching as describ
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<h2><a href="gmlp/index.html">Pay Attention to MLPs (gMLP)</a></h2>
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<p>This is an implementation of the paper
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<a href="https://papers.labml.ai/paper/2105.08050">Pay Attention to MLPs</a>.</p>
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<h2><a href="vit/index.html">Vision Transformer (ViT)</a></h2>
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<p>This is an implementation of the paper
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<a href="https://arxiv.org/abs/2010.11929">An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale</a>.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">87</span><span></span><span class="kn">from</span> <span class="nn">.configs</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span>
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<span class="lineno">88</span><span class="kn">from</span> <span class="nn">.models</span> <span class="kn">import</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">Decoder</span><span class="p">,</span> <span class="n">Generator</span><span class="p">,</span> <span class="n">EncoderDecoder</span>
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<span class="lineno">89</span><span class="kn">from</span> <span class="nn">.mha</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</span>
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<span class="lineno">90</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.xl.relative_mha</span> <span class="kn">import</span> <span class="n">RelativeMultiHeadAttention</span></pre></div>
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<div class="highlight"><pre><span class="lineno">92</span><span></span><span class="kn">from</span> <span class="nn">.configs</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span>
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<span class="lineno">93</span><span class="kn">from</span> <span class="nn">.models</span> <span class="kn">import</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">Encoder</span><span class="p">,</span> <span class="n">Decoder</span><span class="p">,</span> <span class="n">Generator</span><span class="p">,</span> <span class="n">EncoderDecoder</span>
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<span class="lineno">94</span><span class="kn">from</span> <span class="nn">.mha</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</span>
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<span class="lineno">95</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.xl.relative_mha</span> <span class="kn">import</span> <span class="n">RelativeMultiHeadAttention</span></pre></div>
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</div>
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</div>
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</div>
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@ -3,24 +3,24 @@
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<head>
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<meta http-equiv="content-type" content="text/html;charset=utf-8"/>
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<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
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<meta name="description" content="Train a ViT on CIFAR 10"/>
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<meta name="description" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<meta name="twitter:card" content="summary"/>
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<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
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<meta name="twitter:title" content="Train a ViT on CIFAR 10"/>
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<meta name="twitter:description" content="Train a ViT on CIFAR 10"/>
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<meta name="twitter:title" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<meta name="twitter:description" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<meta name="twitter:site" content="@labmlai"/>
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<meta name="twitter:creator" content="@labmlai"/>
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<meta property="og:url" content="https://nn.labml.ai/transformers/vit/experiment.html"/>
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<meta property="og:title" content="Train a ViT on CIFAR 10"/>
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<meta property="og:title" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
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<meta property="og:site_name" content="LabML Neural Networks"/>
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<meta property="og:type" content="object"/>
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<meta property="og:title" content="Train a ViT on CIFAR 10"/>
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<meta property="og:description" content="Train a ViT on CIFAR 10"/>
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<meta property="og:title" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<meta property="og:description" content="Train a Vision Transformer (ViT) on CIFAR 10"/>
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<title>Train a ViT on CIFAR 10</title>
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<title>Train a Vision Transformer (ViT) on CIFAR 10</title>
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<link rel="shortcut icon" href="/icon.png"/>
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<link rel="stylesheet" href="../../pylit.css">
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<link rel="canonical" href="https://nn.labml.ai/transformers/vit/experiment.html"/>
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@ -67,13 +67,14 @@
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<div class='section-link'>
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<a href='#section-0'>#</a>
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</div>
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<h1>Train a ViT on CIFAR 10</h1>
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<h1>Train a <a href="index.html">Vision Transformer (ViT)</a> on CIFAR 10</h1>
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<p><a href="https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">11</span><span></span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
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<span class="lineno">12</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
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<span class="lineno">13</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span>
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<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span></pre></div>
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<div class="highlight"><pre><span class="lineno">13</span><span></span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
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<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
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<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span>
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<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerConfigs</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-1'>
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@ -82,11 +83,11 @@
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<a href='#section-1'>#</a>
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</div>
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<h2>Configurations</h2>
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<p>We use <a href="../experiments/cifar10.html"><code>CIFAR10Configs</code></a> which defines all the
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<p>We use <a href="../../experiments/cifar10.html"><code>CIFAR10Configs</code></a> which defines all the
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dataset related configurations, optimizer, and a training loop.</p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">17</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">19</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-2'>
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@ -94,31 +95,22 @@ dataset related configurations, optimizer, and a training loop.</p>
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<div class='section-link'>
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<a href='#section-2'>#</a>
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</div>
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<p><a href="../configs.html#TransformerConfigs">Transformer configurations</a>
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to get <a href="../models.html#TransformerLayer">transformer layer</a></p>
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</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">25</span> <span class="n">transformer</span><span class="p">:</span> <span class="n">TransformerConfigs</span>
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<span class="lineno">26</span>
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<span class="lineno">27</span> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span>
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<span class="lineno">28</span> <span class="n">n_hidden</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span>
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<span class="lineno">29</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span></pre></div>
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<div class="highlight"><pre><span class="lineno">29</span> <span class="n">transformer</span><span class="p">:</span> <span class="n">TransformerConfigs</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-3'>
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<div class='docs doc-strings'>
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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>
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<h3>Create model</h3>
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<p>Size of a patch</p>
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||||
</div>
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<div class='code'>
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<div class="highlight"><pre><span class="lineno">32</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">transformer</span><span class="p">)</span>
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<span class="lineno">33</span><span class="k">def</span> <span class="nf">_transformer</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span>
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<span class="lineno">34</span> <span class="k">return</span> <span class="n">TransformerConfigs</span><span class="p">()</span>
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<span class="lineno">35</span>
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<span class="lineno">36</span>
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<span class="lineno">37</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
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<span class="lineno">38</span><span class="k">def</span> <span class="nf">_vit</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">32</span> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span></pre></div>
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</div>
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</div>
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<div class='section' id='section-4'>
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@ -126,17 +118,10 @@ dataset related configurations, optimizer, and a training loop.</p>
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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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<p>Size of the hidden layer in classification head</p>
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</div>
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<div class='code'>
|
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<div class="highlight"><pre><span class="lineno">42</span> <span class="kn">from</span> <span class="nn">labml_nn.transformers.vit</span> <span class="kn">import</span> <span class="n">VisionTransformer</span><span class="p">,</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">,</span> <span class="n">ClassificationHead</span><span class="p">,</span> \
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<span class="lineno">43</span> <span class="n">PatchEmbeddings</span>
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<span class="lineno">44</span>
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<span class="lineno">45</span> <span class="n">d_model</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">d_model</span>
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<span class="lineno">46</span> <span class="k">return</span> <span class="n">VisionTransformer</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">encoder_layer</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">n_layers</span><span class="p">,</span>
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<span class="lineno">47</span> <span class="n">PatchEmbeddings</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">patch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
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||||
<span class="lineno">48</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">(</span><span class="n">d_model</span><span class="p">),</span>
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<span class="lineno">49</span> <span class="n">ClassificationHead</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_hidden</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_classes</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">34</span> <span class="n">n_hidden_classification</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span></pre></div>
|
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</div>
|
||||
</div>
|
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<div class='section' id='section-5'>
|
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@ -144,21 +129,22 @@ dataset related configurations, optimizer, and a training loop.</p>
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<div class='section-link'>
|
||||
<a href='#section-5'>#</a>
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||||
</div>
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<p>Number of classes in the task</p>
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</div>
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||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">52</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
|
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<div class="highlight"><pre><span class="lineno">36</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span></pre></div>
|
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</div>
|
||||
</div>
|
||||
<div class='section' id='section-6'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-6'>#</a>
|
||||
</div>
|
||||
<p>Create experiment</p>
|
||||
<p>Create transformer configs</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">54</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'ViT'</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'cifar10'</span><span class="p">)</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">39</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">transformer</span><span class="p">)</span>
|
||||
<span class="lineno">40</span><span class="k">def</span> <span class="nf">_transformer</span><span class="p">():</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-7'>
|
||||
@ -166,22 +152,22 @@ dataset related configurations, optimizer, and a training loop.</p>
|
||||
<div class='section-link'>
|
||||
<a href='#section-7'>#</a>
|
||||
</div>
|
||||
<p>Create configurations</p>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">56</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">44</span> <span class="k">return</span> <span class="n">TransformerConfigs</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-8'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-8'>#</a>
|
||||
</div>
|
||||
<p>Load configurations</p>
|
||||
<h3>Create model</h3>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">58</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
|
||||
<span class="lineno">59</span> <span class="s1">'device.cuda_device'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">47</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="lineno">48</span><span class="k">def</span> <span class="nf">_vit</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-9'>
|
||||
@ -189,22 +175,11 @@ dataset related configurations, optimizer, and a training loop.</p>
|
||||
<div class='section-link'>
|
||||
<a href='#section-9'>#</a>
|
||||
</div>
|
||||
<p>‘optimizer.optimizer’: ‘Noam’,
|
||||
‘optimizer.learning_rate’: 1.,</p>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">63</span> <span class="s1">'optimizer.optimizer'</span><span class="p">:</span> <span class="s1">'Adam'</span><span class="p">,</span>
|
||||
<span class="lineno">64</span> <span class="s1">'optimizer.learning_rate'</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
|
||||
<span class="lineno">65</span> <span class="s1">'optimizer.d_model'</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span>
|
||||
<span class="lineno">66</span>
|
||||
<span class="lineno">67</span> <span class="s1">'transformer.d_model'</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span>
|
||||
<span class="lineno">68</span>
|
||||
<span class="lineno">69</span> <span class="s1">'epochs'</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
|
||||
<span class="lineno">70</span> <span class="s1">'train_batch_size'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
|
||||
<span class="lineno">71</span>
|
||||
<span class="lineno">72</span> <span class="s1">'train_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_train_augmented'</span><span class="p">,</span>
|
||||
<span class="lineno">73</span> <span class="s1">'valid_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_valid_no_augment'</span><span class="p">,</span>
|
||||
<span class="lineno">74</span> <span class="p">})</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">52</span> <span class="kn">from</span> <span class="nn">labml_nn.transformers.vit</span> <span class="kn">import</span> <span class="n">VisionTransformer</span><span class="p">,</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">,</span> <span class="n">ClassificationHead</span><span class="p">,</span> \
|
||||
<span class="lineno">53</span> <span class="n">PatchEmbeddings</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-10'>
|
||||
@ -212,10 +187,10 @@ dataset related configurations, optimizer, and a training loop.</p>
|
||||
<div class='section-link'>
|
||||
<a href='#section-10'>#</a>
|
||||
</div>
|
||||
<p>Set model for saving/loading</p>
|
||||
<p>Transformer size from <a href="../configs.html#TransformerConfigs">Transformer configurations</a></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">76</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">56</span> <span class="n">d_model</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">d_model</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-11'>
|
||||
@ -223,11 +198,13 @@ dataset related configurations, optimizer, and a training loop.</p>
|
||||
<div class='section-link'>
|
||||
<a href='#section-11'>#</a>
|
||||
</div>
|
||||
<p>Start the experiment and run the training loop</p>
|
||||
<p>Create a vision transformer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">78</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
|
||||
<span class="lineno">79</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">58</span> <span class="k">return</span> <span class="n">VisionTransformer</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">encoder_layer</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">transformer</span><span class="o">.</span><span class="n">n_layers</span><span class="p">,</span>
|
||||
<span class="lineno">59</span> <span class="n">PatchEmbeddings</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">patch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
|
||||
<span class="lineno">60</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">(</span><span class="n">d_model</span><span class="p">),</span>
|
||||
<span class="lineno">61</span> <span class="n">ClassificationHead</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_hidden_classification</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_classes</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-12'>
|
||||
@ -238,8 +215,133 @@ dataset related configurations, optimizer, and a training loop.</p>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">83</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="lineno">84</span> <span class="n">main</span><span class="p">()</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">64</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-13'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-13'>#</a>
|
||||
</div>
|
||||
<p>Create experiment</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">66</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'ViT'</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'cifar10'</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-14'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-14'>#</a>
|
||||
</div>
|
||||
<p>Create configurations</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">68</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-15'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-15'>#</a>
|
||||
</div>
|
||||
<p>Load configurations</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">70</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-16'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-16'>#</a>
|
||||
</div>
|
||||
<p>Optimizer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">72</span> <span class="s1">'optimizer.optimizer'</span><span class="p">:</span> <span class="s1">'Adam'</span><span class="p">,</span>
|
||||
<span class="lineno">73</span> <span class="s1">'optimizer.learning_rate'</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-17'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-17'>#</a>
|
||||
</div>
|
||||
<p>Transformer embedding size</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">76</span> <span class="s1">'transformer.d_model'</span><span class="p">:</span> <span class="mi">512</span><span class="p">,</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-18'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-18'>#</a>
|
||||
</div>
|
||||
<p>Training epochs and batch size</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">79</span> <span class="s1">'epochs'</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
|
||||
<span class="lineno">80</span> <span class="s1">'train_batch_size'</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-19'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-19'>#</a>
|
||||
</div>
|
||||
<p>Augment CIFAR 10 images for training</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">83</span> <span class="s1">'train_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_train_augmented'</span><span class="p">,</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-20'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-20'>#</a>
|
||||
</div>
|
||||
<p>Do not augment CIFAR 10 images for validation</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">85</span> <span class="s1">'valid_dataset'</span><span class="p">:</span> <span class="s1">'cifar10_valid_no_augment'</span><span class="p">,</span>
|
||||
<span class="lineno">86</span> <span class="p">})</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-21'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-21'>#</a>
|
||||
</div>
|
||||
<p>Set model for saving/loading</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">88</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">'model'</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-22'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-22'>#</a>
|
||||
</div>
|
||||
<p>Start the experiment and run the training loop</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">90</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
|
||||
<span class="lineno">91</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-23'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-23'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">95</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="lineno">96</span> <span class="n">main</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -3,24 +3,24 @@
|
||||
<head>
|
||||
<meta http-equiv="content-type" content="text/html;charset=utf-8"/>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
|
||||
<meta name="description" content=""/>
|
||||
<meta name="description" content="A PyTorch implementation/tutorial of the paper "An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale""/>
|
||||
|
||||
<meta name="twitter:card" content="summary"/>
|
||||
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
|
||||
<meta name="twitter:title" content="__init__.py"/>
|
||||
<meta name="twitter:description" content=""/>
|
||||
<meta name="twitter:title" content="Vision Transformer (ViT)"/>
|
||||
<meta name="twitter:description" content="A PyTorch implementation/tutorial of the paper "An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale""/>
|
||||
<meta name="twitter:site" content="@labmlai"/>
|
||||
<meta name="twitter:creator" content="@labmlai"/>
|
||||
|
||||
<meta property="og:url" content="https://nn.labml.ai/transformers/vit/index.html"/>
|
||||
<meta property="og:title" content="__init__.py"/>
|
||||
<meta property="og:title" content="Vision Transformer (ViT)"/>
|
||||
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
|
||||
<meta property="og:site_name" content="LabML Neural Networks"/>
|
||||
<meta property="og:type" content="object"/>
|
||||
<meta property="og:title" content="__init__.py"/>
|
||||
<meta property="og:description" content=""/>
|
||||
<meta property="og:title" content="Vision Transformer (ViT)"/>
|
||||
<meta property="og:description" content="A PyTorch implementation/tutorial of the paper "An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale""/>
|
||||
|
||||
<title>__init__.py</title>
|
||||
<title>Vision Transformer (ViT)</title>
|
||||
<link rel="shortcut icon" href="/icon.png"/>
|
||||
<link rel="stylesheet" href="../../pylit.css">
|
||||
<link rel="canonical" href="https://nn.labml.ai/transformers/vit/index.html"/>
|
||||
@ -63,19 +63,46 @@
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-0'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-0'>#</a>
|
||||
</div>
|
||||
|
||||
<h1>Vision Transformer (ViT)</h1>
|
||||
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
|
||||
<a href="https://arxiv.org/abs/2010.11929">An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale</a>.</p>
|
||||
<p>Vision transformer applies a pure transformer to images
|
||||
without any convolution layers.
|
||||
They split the image into patches and apply a transformer on patch embeddings.
|
||||
<a href="#PathEmbeddings">Patch embeddings</a> are generated by applying a simple linear transformation
|
||||
to the flattened pixel values of the patch.
|
||||
Then a standard transformer encoder is fed with the patch embeddings, along with a
|
||||
classification token <code>[CLS]</code>.
|
||||
The encoding on the <code>[CLS]</code> token is used to classify the image with an MLP.</p>
|
||||
<p>When feeding the transformer with the patches, learned positional embeddings are
|
||||
added to the patch embeddings, because the patch embeddings do not have any information
|
||||
about where that patch is from.
|
||||
The positional embeddings are a set of vectors for each patch location that get trained
|
||||
with gradient descent along with other parameters.</p>
|
||||
<p>ViTs perform well when they are pre-trained on large datasets.
|
||||
The paper suggests pre-training them with an MLP classification head and
|
||||
then using a single linear layer when fine-tuning.
|
||||
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
|
||||
They also use higher resolution images during inference while keeping the
|
||||
patch size the same.
|
||||
The positional embeddings for new patch locations are calculated by interpolating
|
||||
learning positional embeddings.</p>
|
||||
<p>Here’s <a href="experiment.html">an experiment</a> that trains ViT on CIFAR-10.
|
||||
This doesn’t do very well because it’s trained on a small dataset.
|
||||
It’s a simple experiment that anyone can run and play with ViTs.</p>
|
||||
<p><a href="https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
|
||||
<span class="lineno">2</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
|
||||
<span class="lineno">3</span>
|
||||
<span class="lineno">4</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
|
||||
<span class="lineno">5</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerLayer</span>
|
||||
<span class="lineno">6</span><span class="kn">from</span> <span class="nn">labml_nn.utils</span> <span class="kn">import</span> <span class="n">clone_module_list</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">45</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
|
||||
<span class="lineno">46</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
|
||||
<span class="lineno">47</span>
|
||||
<span class="lineno">48</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
|
||||
<span class="lineno">49</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">TransformerLayer</span>
|
||||
<span class="lineno">50</span><span class="kn">from</span> <span class="nn">labml_nn.utils</span> <span class="kn">import</span> <span class="n">clone_module_list</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-1'>
|
||||
@ -84,36 +111,40 @@
|
||||
<a href='#section-1'>#</a>
|
||||
</div>
|
||||
<p><a id="PatchEmbeddings"></p>
|
||||
<h2>Embed patches</h2>
|
||||
<h2>Get patch embeddings</h2>
|
||||
<p></a></p>
|
||||
<p>The paper splits the image into patches of equal size and do a linear transformation
|
||||
on the flattened pixels for each patch.</p>
|
||||
<p>We implement the same thing through a convolution layer, because it’s simpler to implement.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">9</span><span class="k">class</span> <span class="nc">PatchEmbeddings</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">53</span><span class="k">class</span> <span class="nc">PatchEmbeddings</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-2'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-2'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>d_model</code> is the transformer embeddings size</li>
|
||||
<li><code>patch_size</code> is the size of the patch</li>
|
||||
<li><code>in_channels</code> is the number of channels in the input image (3 for rgb)</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">65</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-3'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-3'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">16</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</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="lineno">17</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="lineno">18</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_size</span> <span class="o">=</span> <span class="n">patch_size</span>
|
||||
<span class="lineno">19</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">patch_size</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-3'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-3'>#</a>
|
||||
</div>
|
||||
<p>x has shape <code>[batch_size, channels, height, width]</code></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">21</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">71</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-4'>
|
||||
@ -121,15 +152,12 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-4'>#</a>
|
||||
</div>
|
||||
|
||||
<p>We create a convolution layer with a kernel size and and stride length equal to patch size.
|
||||
This is equivalent to splitting the image into patches and doing a linear
|
||||
transformation on each patch.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">25</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">26</span> <span class="n">bs</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="lineno">27</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="lineno">28</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">w</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
|
||||
<span class="lineno">29</span>
|
||||
<span class="lineno">30</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">76</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">patch_size</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-5'>
|
||||
@ -137,12 +165,12 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-5'>#</a>
|
||||
</div>
|
||||
<p><a id="LearnedPositionalEmbeddings"></p>
|
||||
<h2>Add parameterized positional encodings</h2>
|
||||
<p></a></p>
|
||||
<ul>
|
||||
<li><code>x</code> is the input image of shape <code>[batch_size, channels, height, width]</code></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">33</span><span class="k">class</span> <span class="nc">LearnedPositionalEmbeddings</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">78</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-6'>
|
||||
@ -150,12 +178,10 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-6'>#</a>
|
||||
</div>
|
||||
|
||||
<p>Apply convolution layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">40</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">max_len</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5_000</span><span class="p">):</span>
|
||||
<span class="lineno">41</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="lineno">42</span> <span class="bp">self</span><span class="o">.</span><span class="n">positional_encodings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">max_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">d_model</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">83</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-7'>
|
||||
@ -163,12 +189,10 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-7'>#</a>
|
||||
</div>
|
||||
|
||||
<p>Get the shape.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">44</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
|
||||
<span class="lineno">45</span> <span class="n">pe</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">positional_encodings</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
|
||||
<span class="lineno">46</span> <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">pe</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">85</span> <span class="n">bs</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-8'>
|
||||
@ -176,10 +200,11 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-8'>#</a>
|
||||
</div>
|
||||
|
||||
<p>Rearrange to shape <code>[patches, batch_size, d_model]</code></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">49</span><span class="k">class</span> <span class="nc">ClassificationHead</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">87</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="lineno">88</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">w</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-9'>
|
||||
@ -187,42 +212,38 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-9'>#</a>
|
||||
</div>
|
||||
|
||||
<p>Return the patch embeddings</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">50</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="lineno">51</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="lineno">52</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">([</span><span class="n">d_model</span><span class="p">])</span>
|
||||
<span class="lineno">53</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">)</span>
|
||||
<span class="lineno">54</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
|
||||
<span class="lineno">55</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_hidden</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">91</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-10'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-10'>#</a>
|
||||
</div>
|
||||
|
||||
<p><a id="LearnedPositionalEmbeddings"></p>
|
||||
<h2>Add parameterized positional encodings</h2>
|
||||
<p></a></p>
|
||||
<p>This adds learned positional embeddings to patch embeddings.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">57</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
|
||||
<span class="lineno">58</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">59</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
|
||||
<span class="lineno">60</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">61</span>
|
||||
<span class="lineno">62</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">94</span><span class="k">class</span> <span class="nc">LearnedPositionalEmbeddings</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-11'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-11'>#</a>
|
||||
</div>
|
||||
|
||||
<ul>
|
||||
<li><code>d_model</code> is the transformer embeddings size</li>
|
||||
<li><code>max_len</code> is the maximum number of patches</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">65</span><span class="k">class</span> <span class="nc">VisionTransformer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">103</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">max_len</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5_000</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-12'>
|
||||
@ -233,10 +254,7 @@
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">66</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformer_layer</span><span class="p">:</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
|
||||
<span class="lineno">67</span> <span class="n">patch_emb</span><span class="p">:</span> <span class="n">PatchEmbeddings</span><span class="p">,</span> <span class="n">pos_emb</span><span class="p">:</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">,</span>
|
||||
<span class="lineno">68</span> <span class="n">classification</span><span class="p">:</span> <span class="n">ClassificationHead</span><span class="p">):</span>
|
||||
<span class="lineno">69</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">108</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-13'>
|
||||
@ -244,38 +262,368 @@
|
||||
<div class='section-link'>
|
||||
<a href='#section-13'>#</a>
|
||||
</div>
|
||||
<p>Make copies of the transformer layer</p>
|
||||
<p>Positional embeddings for each location</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">71</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="n">classification</span>
|
||||
<span class="lineno">72</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_emb</span> <span class="o">=</span> <span class="n">pos_emb</span>
|
||||
<span class="lineno">73</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span> <span class="o">=</span> <span class="n">patch_emb</span>
|
||||
<span class="lineno">74</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer_layers</span> <span class="o">=</span> <span class="n">clone_module_list</span><span class="p">(</span><span class="n">transformer_layer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">)</span>
|
||||
<span class="lineno">75</span>
|
||||
<span class="lineno">76</span> <span class="bp">self</span><span class="o">.</span><span class="n">cls_token_emb</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">transformer_layer</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">110</span> <span class="bp">self</span><span class="o">.</span><span class="n">positional_encodings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">max_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">d_model</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-14'>
|
||||
<div class='docs'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-14'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>x</code> is the patch embeddings of shape <code>[patches, batch_size, d_model]</code></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">112</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-15'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-15'>#</a>
|
||||
</div>
|
||||
<p>Get the positional embeddings for the given patches</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">117</span> <span class="n">pe</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">positional_encodings</span><span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-16'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-16'>#</a>
|
||||
</div>
|
||||
<p>Add to patch embeddings and return</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">119</span> <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">pe</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-17'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-17'>#</a>
|
||||
</div>
|
||||
<p><a id="ClassificationHead"></p>
|
||||
<h2>MLP Classification Head</h2>
|
||||
<p></a></p>
|
||||
<p>This is the two layer MLP head to classify the image based on <code>[CLS]</code> token embedding.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">122</span><span class="k">class</span> <span class="nc">ClassificationHead</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-18'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-18'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>d_model</code> is the transformer embedding size</li>
|
||||
<li><code>n_hidden</code> is the size of the hidden layer</li>
|
||||
<li><code>n_classes</code> is the number of classes in the classification task</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">130</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-19'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-19'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">78</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="lineno">79</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">80</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">81</span> <span class="n">cls_token_emb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cls_token_emb</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="lineno">82</span> <span class="n">x</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">cls_token_emb</span><span class="p">,</span> <span class="n">x</span><span class="p">])</span>
|
||||
<span class="lineno">83</span> <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer_layers</span><span class="p">:</span>
|
||||
<span class="lineno">84</span> <span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="lineno">85</span>
|
||||
<span class="lineno">86</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="lineno">87</span>
|
||||
<span class="lineno">88</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="lineno">89</span>
|
||||
<span class="lineno">90</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
<div class="highlight"><pre><span class="lineno">136</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-20'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-20'>#</a>
|
||||
</div>
|
||||
<p>First layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">138</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">n_hidden</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-21'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-21'>#</a>
|
||||
</div>
|
||||
<p>Activation</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">140</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-22'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-22'>#</a>
|
||||
</div>
|
||||
<p>Second layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">142</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">n_hidden</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-23'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-23'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>x</code> is the transformer encoding for <code>[CLS]</code> token</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">144</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-24'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-24'>#</a>
|
||||
</div>
|
||||
<p>First layer and activation</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">149</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">act</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linear1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-25'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-25'>#</a>
|
||||
</div>
|
||||
<p>Second layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">151</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-26'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-26'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">154</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-27'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-27'>#</a>
|
||||
</div>
|
||||
<h2>Vision Transformer</h2>
|
||||
<p>This combines the <a href="#PatchEmbeddings">patch embeddings</a>,
|
||||
<a href="#LearnedPositionalEmbeddings">positional embeddings</a>,
|
||||
transformer and the <a href="#ClassificationHead">classification head</a>.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">157</span><span class="k">class</span> <span class="nc">VisionTransformer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-28'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-28'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>transformer_layer</code> is a copy of a single <a href="../models.html#TransformerLayer">transformer layer</a>.
|
||||
We make copies of it to make the transformer with <code>n_layers</code>.</li>
|
||||
<li><code>n_layers</code> is the number of [transformer layers]((../models.html#TransformerLayer).</li>
|
||||
<li><code>patch_emb</code> is the <a href="#PatchEmbeddings">patch embeddings layer</a>.</li>
|
||||
<li><code>pos_emb</code> is the <a href="#LearnedPositionalEmbeddings">positional embeddings layer</a>.</li>
|
||||
<li><code>classification</code> is the <a href="#ClassificationHead">classification head</a>.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">165</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformer_layer</span><span class="p">:</span> <span class="n">TransformerLayer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
|
||||
<span class="lineno">166</span> <span class="n">patch_emb</span><span class="p">:</span> <span class="n">PatchEmbeddings</span><span class="p">,</span> <span class="n">pos_emb</span><span class="p">:</span> <span class="n">LearnedPositionalEmbeddings</span><span class="p">,</span>
|
||||
<span class="lineno">167</span> <span class="n">classification</span><span class="p">:</span> <span class="n">ClassificationHead</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-29'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-29'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">176</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-30'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-30'>#</a>
|
||||
</div>
|
||||
<p>Patch embeddings</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">178</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span> <span class="o">=</span> <span class="n">patch_emb</span>
|
||||
<span class="lineno">179</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_emb</span> <span class="o">=</span> <span class="n">pos_emb</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-31'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-31'>#</a>
|
||||
</div>
|
||||
<p>Classification head</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">181</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span> <span class="o">=</span> <span class="n">classification</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-32'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-32'>#</a>
|
||||
</div>
|
||||
<p>Make copies of the transformer layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">183</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer_layers</span> <span class="o">=</span> <span class="n">clone_module_list</span><span class="p">(</span><span class="n">transformer_layer</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-33'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-33'>#</a>
|
||||
</div>
|
||||
<p><code>[CLS]</code> token embedding</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">186</span> <span class="bp">self</span><span class="o">.</span><span class="n">cls_token_emb</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">transformer_layer</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-34'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-34'>#</a>
|
||||
</div>
|
||||
<p>Final normalization layer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">188</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">([</span><span class="n">transformer_layer</span><span class="o">.</span><span class="n">size</span><span class="p">])</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-35'>
|
||||
<div class='docs doc-strings'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-35'>#</a>
|
||||
</div>
|
||||
<ul>
|
||||
<li><code>x</code> is the input image of shape <code>[batch_size, channels, height, width]</code></li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">190</span> <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-36'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-36'>#</a>
|
||||
</div>
|
||||
<p>Get patch embeddings. This gives a tensor of shape <code>[patches, batch_size, d_model]</code></p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">195</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patch_emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-37'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-37'>#</a>
|
||||
</div>
|
||||
<p>Add positional embeddings</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">197</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-38'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-38'>#</a>
|
||||
</div>
|
||||
<p>Concatenate the <code>[CLS]</code> token embeddings before feeding the transformer</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">199</span> <span class="n">cls_token_emb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cls_token_emb</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="lineno">200</span> <span class="n">x</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">cls_token_emb</span><span class="p">,</span> <span class="n">x</span><span class="p">])</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-39'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-39'>#</a>
|
||||
</div>
|
||||
<p>Pass through transformer layers with no attention masking</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">203</span> <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer_layers</span><span class="p">:</span>
|
||||
<span class="lineno">204</span> <span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-40'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-40'>#</a>
|
||||
</div>
|
||||
<p>Get the transformer output of the <code>[CLS]</code> token (which is the first in the sequence).</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">207</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-41'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-41'>#</a>
|
||||
</div>
|
||||
<p>Layer normalization</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">210</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ln</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-42'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-42'>#</a>
|
||||
</div>
|
||||
<p>Classification head, to get logits</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">213</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classification</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-43'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-43'>#</a>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class='code'>
|
||||
<div class="highlight"><pre><span class="lineno">216</span> <span class="k">return</span> <span class="n">x</span></pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
162
docs/transformers/vit/readme.html
Normal file
162
docs/transformers/vit/readme.html
Normal file
@ -0,0 +1,162 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta http-equiv="content-type" content="text/html;charset=utf-8"/>
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0"/>
|
||||
<meta name="description" content=""/>
|
||||
|
||||
<meta name="twitter:card" content="summary"/>
|
||||
<meta name="twitter:image:src" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
|
||||
<meta name="twitter:title" content=" Vision Transformer (ViT)"/>
|
||||
<meta name="twitter:description" content=""/>
|
||||
<meta name="twitter:site" content="@labmlai"/>
|
||||
<meta name="twitter:creator" content="@labmlai"/>
|
||||
|
||||
<meta property="og:url" content="https://nn.labml.ai/transformers/vit/readme.html"/>
|
||||
<meta property="og:title" content=" Vision Transformer (ViT)"/>
|
||||
<meta property="og:image" content="https://avatars1.githubusercontent.com/u/64068543?s=400&v=4"/>
|
||||
<meta property="og:site_name" content="LabML Neural Networks"/>
|
||||
<meta property="og:type" content="object"/>
|
||||
<meta property="og:title" content=" Vision Transformer (ViT)"/>
|
||||
<meta property="og:description" content=""/>
|
||||
|
||||
<title> Vision Transformer (ViT)</title>
|
||||
<link rel="shortcut icon" href="/icon.png"/>
|
||||
<link rel="stylesheet" href="../../pylit.css">
|
||||
<link rel="canonical" href="https://nn.labml.ai/transformers/vit/readme.html"/>
|
||||
<!-- Global site tag (gtag.js) - Google Analytics -->
|
||||
<script async src="https://www.googletagmanager.com/gtag/js?id=G-4V3HC8HBLH"></script>
|
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<script>
|
||||
window.dataLayer = window.dataLayer || [];
|
||||
|
||||
function gtag() {
|
||||
dataLayer.push(arguments);
|
||||
}
|
||||
|
||||
gtag('js', new Date());
|
||||
|
||||
gtag('config', 'G-4V3HC8HBLH');
|
||||
</script>
|
||||
</head>
|
||||
<body>
|
||||
<div id='container'>
|
||||
<div id="background"></div>
|
||||
<div class='section'>
|
||||
<div class='docs'>
|
||||
<p>
|
||||
<a class="parent" href="/">home</a>
|
||||
<a class="parent" href="../index.html">transformers</a>
|
||||
<a class="parent" href="index.html">vit</a>
|
||||
</p>
|
||||
<p>
|
||||
|
||||
<a href="https://github.com/lab-ml/labml_nn/tree/master/labml_nn/transformers/vit/readme.md">
|
||||
<img alt="Github"
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src="https://img.shields.io/github/stars/lab-ml/nn?style=social"
|
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<a href="https://twitter.com/labmlai"
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rel="nofollow">
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src="https://img.shields.io/twitter/follow/labmlai?style=social"
|
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style="max-width:100%;"/></a>
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class='section' id='section-0'>
|
||||
<div class='docs'>
|
||||
<div class='section-link'>
|
||||
<a href='#section-0'>#</a>
|
||||
</div>
|
||||
<h1><a href="https://nn.labml.ai/transformer/vit/index.html">Vision Transformer (ViT)</a></h1>
|
||||
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of the paper
|
||||
<a href="https://arxiv.org/abs/2010.11929">An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale</a>.</p>
|
||||
<p>Vision transformer applies a pure transformer to images
|
||||
without any convolution layers.
|
||||
They split the image into patches and apply a transformer on patch embeddings.
|
||||
<a href="https://nn.labml.ai/transformer/vit/index.html#PathEmbeddings">Patch embeddings</a> are generated by applying a simple linear transformation
|
||||
to the flattened pixel values of the patch.
|
||||
Then a standard transformer encoder is fed with the patch embeddings, along with a
|
||||
classification token <code>[CLS]</code>.
|
||||
The encoding on the <code>[CLS]</code> token is used to classify the image with an MLP.</p>
|
||||
<p>When feeding the transformer with the patches, learned positional embeddings are
|
||||
added to the patch embeddings, because the patch embeddings do not have any information
|
||||
about where that patch is from.
|
||||
The positional embeddings are a set of vectors for each patch location that get trained
|
||||
with gradient descent along with other parameters.</p>
|
||||
<p>ViTs perform well when they are pre-trained on large datasets.
|
||||
The paper suggests pre-training them with an MLP classification head and
|
||||
then using a single linear layer when fine-tuning.
|
||||
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
|
||||
They also use higher resolution images during inference while keeping the
|
||||
patch size the same.
|
||||
The positional embeddings for new patch locations are calculated by interpolating
|
||||
learning positional embeddings.</p>
|
||||
<p>Here’s <a href="https://nn.labml.ai/transformer/vit/experiment.html">an experiment</a> that trains ViT on CIFAR-10.
|
||||
This doesn’t do very well because it’s trained on a small dataset.
|
||||
It’s a simple experiment that anyone can run and play with ViTs.</p>
|
||||
</div>
|
||||
<div class='code'>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.4/MathJax.js?config=TeX-AMS_HTML">
|
||||
</script>
|
||||
<!-- MathJax configuration -->
|
||||
<script type="text/x-mathjax-config">
|
||||
MathJax.Hub.Config({
|
||||
tex2jax: {
|
||||
inlineMath: [ ['$','$'] ],
|
||||
displayMath: [ ['$$','$$'] ],
|
||||
processEscapes: true,
|
||||
processEnvironments: true
|
||||
},
|
||||
// Center justify equations in code and markdown cells. Elsewhere
|
||||
// we use CSS to left justify single line equations in code cells.
|
||||
displayAlign: 'center',
|
||||
"HTML-CSS": { fonts: ["TeX"] }
|
||||
});
|
||||
</script>
|
||||
<script>
|
||||
function handleImages() {
|
||||
var images = document.querySelectorAll('p>img')
|
||||
|
||||
console.log(images);
|
||||
for (var i = 0; i < images.length; ++i) {
|
||||
handleImage(images[i])
|
||||
}
|
||||
}
|
||||
|
||||
function handleImage(img) {
|
||||
img.parentElement.style.textAlign = 'center'
|
||||
|
||||
var modal = document.createElement('div')
|
||||
modal.id = 'modal'
|
||||
|
||||
var modalContent = document.createElement('div')
|
||||
modal.appendChild(modalContent)
|
||||
|
||||
var modalImage = document.createElement('img')
|
||||
modalContent.appendChild(modalImage)
|
||||
|
||||
var span = document.createElement('span')
|
||||
span.classList.add('close')
|
||||
span.textContent = 'x'
|
||||
modal.appendChild(span)
|
||||
|
||||
img.onclick = function () {
|
||||
console.log('clicked')
|
||||
document.body.appendChild(modal)
|
||||
modalImage.src = img.src
|
||||
}
|
||||
|
||||
span.onclick = function () {
|
||||
document.body.removeChild(modal)
|
||||
}
|
||||
}
|
||||
|
||||
handleImages()
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@ -31,6 +31,7 @@ implementations.
|
||||
* [Masked Language Model](transformers/mlm/index.html)
|
||||
* [MLP-Mixer: An all-MLP Architecture for Vision](transformers/mlp_mixer/index.html)
|
||||
* [Pay Attention to MLPs (gMLP)](transformers/gmlp/index.html)
|
||||
* [Vision Transformer (ViT)](transformers/vit/index.html)
|
||||
|
||||
#### ✨ [Recurrent Highway Networks](recurrent_highway_networks/index.html)
|
||||
|
||||
|
||||
@ -82,6 +82,11 @@ This is an implementation of the paper
|
||||
|
||||
This is an implementation of the paper
|
||||
[Pay Attention to MLPs](https://papers.labml.ai/paper/2105.08050).
|
||||
|
||||
## [Vision Transformer (ViT)](vit/index.html)
|
||||
|
||||
This is an implementation of the paper
|
||||
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
|
||||
"""
|
||||
|
||||
from .configs import TransformerConfigs
|
||||
|
||||
@ -1,3 +1,47 @@
|
||||
"""
|
||||
---
|
||||
title: Vision Transformer (ViT)
|
||||
summary: >
|
||||
A PyTorch implementation/tutorial of the paper
|
||||
"An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale"
|
||||
---
|
||||
|
||||
# Vision Transformer (ViT)
|
||||
|
||||
This is a [PyTorch](https://pytorch.org) implementation of the paper
|
||||
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
|
||||
|
||||
Vision transformer applies a pure transformer to images
|
||||
without any convolution layers.
|
||||
They split the image into patches and apply a transformer on patch embeddings.
|
||||
[Patch embeddings](#PathEmbeddings) are generated by applying a simple linear transformation
|
||||
to the flattened pixel values of the patch.
|
||||
Then a standard transformer encoder is fed with the patch embeddings, along with a
|
||||
classification token `[CLS]`.
|
||||
The encoding on the `[CLS]` token is used to classify the image with an MLP.
|
||||
|
||||
When feeding the transformer with the patches, learned positional embeddings are
|
||||
added to the patch embeddings, because the patch embeddings do not have any information
|
||||
about where that patch is from.
|
||||
The positional embeddings are a set of vectors for each patch location that get trained
|
||||
with gradient descent along with other parameters.
|
||||
|
||||
ViTs perform well when they are pre-trained on large datasets.
|
||||
The paper suggests pre-training them with an MLP classification head and
|
||||
then using a single linear layer when fine-tuning.
|
||||
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
|
||||
They also use higher resolution images during inference while keeping the
|
||||
patch size the same.
|
||||
The positional embeddings for new patch locations are calculated by interpolating
|
||||
learning positional embeddings.
|
||||
|
||||
Here's [an experiment](experiment.html) that trains ViT on CIFAR-10.
|
||||
This doesn't do very well because it's trained on a small dataset.
|
||||
It's a simple experiment that anyone can run and play with ViTs.
|
||||
|
||||
[](https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f)
|
||||
"""
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
@ -9,24 +53,41 @@ from labml_nn.utils import clone_module_list
|
||||
class PatchEmbeddings(Module):
|
||||
"""
|
||||
<a id="PatchEmbeddings">
|
||||
## Embed patches
|
||||
## Get patch embeddings
|
||||
</a>
|
||||
|
||||
The paper splits the image into patches of equal size and do a linear transformation
|
||||
on the flattened pixels for each patch.
|
||||
|
||||
We implement the same thing through a convolution layer, because it's simpler to implement.
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, patch_size: int, in_channels: int):
|
||||
"""
|
||||
* `d_model` is the transformer embeddings size
|
||||
* `patch_size` is the size of the patch
|
||||
* `in_channels` is the number of channels in the input image (3 for rgb)
|
||||
"""
|
||||
super().__init__()
|
||||
self.patch_size = patch_size
|
||||
|
||||
# We create a convolution layer with a kernel size and and stride length equal to patch size.
|
||||
# This is equivalent to splitting the image into patches and doing a linear
|
||||
# transformation on each patch.
|
||||
self.conv = nn.Conv2d(in_channels, d_model, patch_size, stride=patch_size)
|
||||
|
||||
def __call__(self, x: torch.Tensor):
|
||||
"""
|
||||
x has shape `[batch_size, channels, height, width]`
|
||||
* `x` is the input image of shape `[batch_size, channels, height, width]`
|
||||
"""
|
||||
# Apply convolution layer
|
||||
x = self.conv(x)
|
||||
# Get the shape.
|
||||
bs, c, h, w = x.shape
|
||||
# Rearrange to shape `[patches, batch_size, d_model]`
|
||||
x = x.permute(2, 3, 0, 1)
|
||||
x = x.view(h * w, bs, c)
|
||||
|
||||
# Return the patch embeddings
|
||||
return x
|
||||
|
||||
|
||||
@ -35,56 +96,121 @@ class LearnedPositionalEmbeddings(Module):
|
||||
<a id="LearnedPositionalEmbeddings">
|
||||
## Add parameterized positional encodings
|
||||
</a>
|
||||
|
||||
This adds learned positional embeddings to patch embeddings.
|
||||
"""
|
||||
|
||||
def __init__(self, d_model: int, max_len: int = 5_000):
|
||||
"""
|
||||
* `d_model` is the transformer embeddings size
|
||||
* `max_len` is the maximum number of patches
|
||||
"""
|
||||
super().__init__()
|
||||
# Positional embeddings for each location
|
||||
self.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)
|
||||
|
||||
def __call__(self, x: torch.Tensor):
|
||||
"""
|
||||
* `x` is the patch embeddings of shape `[patches, batch_size, d_model]`
|
||||
"""
|
||||
# Get the positional embeddings for the given patches
|
||||
pe = self.positional_encodings[x.shape[0]]
|
||||
# Add to patch embeddings and return
|
||||
return x + pe
|
||||
|
||||
|
||||
class ClassificationHead(Module):
|
||||
"""
|
||||
<a id="ClassificationHead">
|
||||
## MLP Classification Head
|
||||
</a>
|
||||
|
||||
This is the two layer MLP head to classify the image based on `[CLS]` token embedding.
|
||||
"""
|
||||
def __init__(self, d_model: int, n_hidden: int, n_classes: int):
|
||||
"""
|
||||
* `d_model` is the transformer embedding size
|
||||
* `n_hidden` is the size of the hidden layer
|
||||
* `n_classes` is the number of classes in the classification task
|
||||
"""
|
||||
super().__init__()
|
||||
self.ln = nn.LayerNorm([d_model])
|
||||
# First layer
|
||||
self.linear1 = nn.Linear(d_model, n_hidden)
|
||||
# Activation
|
||||
self.act = nn.ReLU()
|
||||
# Second layer
|
||||
self.linear2 = nn.Linear(n_hidden, n_classes)
|
||||
|
||||
def __call__(self, x: torch.Tensor):
|
||||
x = self.ln(x)
|
||||
"""
|
||||
* `x` is the transformer encoding for `[CLS]` token
|
||||
"""
|
||||
# First layer and activation
|
||||
x = self.act(self.linear1(x))
|
||||
# Second layer
|
||||
x = self.linear2(x)
|
||||
|
||||
#
|
||||
return x
|
||||
|
||||
|
||||
class VisionTransformer(Module):
|
||||
"""
|
||||
## Vision Transformer
|
||||
|
||||
This combines the [patch embeddings](#PatchEmbeddings),
|
||||
[positional embeddings](#LearnedPositionalEmbeddings),
|
||||
transformer and the [classification head](#ClassificationHead).
|
||||
"""
|
||||
def __init__(self, transformer_layer: TransformerLayer, n_layers: int,
|
||||
patch_emb: PatchEmbeddings, pos_emb: LearnedPositionalEmbeddings,
|
||||
classification: ClassificationHead):
|
||||
"""
|
||||
* `transformer_layer` is a copy of a single [transformer layer](../models.html#TransformerLayer).
|
||||
We make copies of it to make the transformer with `n_layers`.
|
||||
* `n_layers` is the number of [transformer layers]((../models.html#TransformerLayer).
|
||||
* `patch_emb` is the [patch embeddings layer](#PatchEmbeddings).
|
||||
* `pos_emb` is the [positional embeddings layer](#LearnedPositionalEmbeddings).
|
||||
* `classification` is the [classification head](#ClassificationHead).
|
||||
"""
|
||||
super().__init__()
|
||||
# Make copies of the transformer layer
|
||||
self.classification = classification
|
||||
self.pos_emb = pos_emb
|
||||
# Patch embeddings
|
||||
self.patch_emb = patch_emb
|
||||
self.pos_emb = pos_emb
|
||||
# Classification head
|
||||
self.classification = classification
|
||||
# Make copies of the transformer layer
|
||||
self.transformer_layers = clone_module_list(transformer_layer, n_layers)
|
||||
|
||||
# `[CLS]` token embedding
|
||||
self.cls_token_emb = nn.Parameter(torch.randn(1, 1, transformer_layer.size), requires_grad=True)
|
||||
# Final normalization layer
|
||||
self.ln = nn.LayerNorm([transformer_layer.size])
|
||||
|
||||
def __call__(self, x):
|
||||
def __call__(self, x: torch.Tensor):
|
||||
"""
|
||||
* `x` is the input image of shape `[batch_size, channels, height, width]`
|
||||
"""
|
||||
# Get patch embeddings. This gives a tensor of shape `[patches, batch_size, d_model]`
|
||||
x = self.patch_emb(x)
|
||||
# Add positional embeddings
|
||||
x = self.pos_emb(x)
|
||||
# Concatenate the `[CLS]` token embeddings before feeding the transformer
|
||||
cls_token_emb = self.cls_token_emb.expand(-1, x.shape[1], -1)
|
||||
x = torch.cat([cls_token_emb, x])
|
||||
|
||||
# Pass through transformer layers with no attention masking
|
||||
for layer in self.transformer_layers:
|
||||
x = layer(x=x, mask=None)
|
||||
|
||||
# Get the transformer output of the `[CLS]` token (which is the first in the sequence).
|
||||
x = x[0]
|
||||
|
||||
# Layer normalization
|
||||
x = self.ln(x)
|
||||
|
||||
# Classification head, to get logits
|
||||
x = self.classification(x)
|
||||
|
||||
#
|
||||
return x
|
||||
|
||||
@ -1,11 +1,13 @@
|
||||
"""
|
||||
---
|
||||
title: Train a ViT on CIFAR 10
|
||||
title: Train a Vision Transformer (ViT) on CIFAR 10
|
||||
summary: >
|
||||
Train a ViT on CIFAR 10
|
||||
Train a Vision Transformer (ViT) on CIFAR 10
|
||||
---
|
||||
|
||||
# Train a ViT on CIFAR 10
|
||||
# Train a [Vision Transformer (ViT)](index.html) on CIFAR 10
|
||||
|
||||
[](https://app.labml.ai/run/8b531d9ce3dc11eb84fc87df6756eb8f)
|
||||
"""
|
||||
|
||||
from labml import experiment
|
||||
@ -18,19 +20,27 @@ class Configs(CIFAR10Configs):
|
||||
"""
|
||||
## Configurations
|
||||
|
||||
We use [`CIFAR10Configs`](../experiments/cifar10.html) which defines all the
|
||||
We use [`CIFAR10Configs`](../../experiments/cifar10.html) which defines all the
|
||||
dataset related configurations, optimizer, and a training loop.
|
||||
"""
|
||||
|
||||
# [Transformer configurations](../configs.html#TransformerConfigs)
|
||||
# to get [transformer layer](../models.html#TransformerLayer)
|
||||
transformer: TransformerConfigs
|
||||
|
||||
# Size of a patch
|
||||
patch_size: int = 4
|
||||
n_hidden: int = 2048
|
||||
# Size of the hidden layer in classification head
|
||||
n_hidden_classification: int = 2048
|
||||
# Number of classes in the task
|
||||
n_classes: int = 10
|
||||
|
||||
|
||||
@option(Configs.transformer)
|
||||
def _transformer(c: Configs):
|
||||
def _transformer():
|
||||
"""
|
||||
Create transformer configs
|
||||
"""
|
||||
return TransformerConfigs()
|
||||
|
||||
|
||||
@ -42,11 +52,13 @@ def _vit(c: Configs):
|
||||
from labml_nn.transformers.vit import VisionTransformer, LearnedPositionalEmbeddings, ClassificationHead, \
|
||||
PatchEmbeddings
|
||||
|
||||
# Transformer size from [Transformer configurations](../configs.html#TransformerConfigs)
|
||||
d_model = c.transformer.d_model
|
||||
# Create a vision transformer
|
||||
return VisionTransformer(c.transformer.encoder_layer, c.transformer.n_layers,
|
||||
PatchEmbeddings(d_model, c.patch_size, 3),
|
||||
LearnedPositionalEmbeddings(d_model),
|
||||
ClassificationHead(d_model, c.n_hidden, c.n_classes)).to(c.device)
|
||||
ClassificationHead(d_model, c.n_hidden_classification, c.n_classes)).to(c.device)
|
||||
|
||||
|
||||
def main():
|
||||
@ -56,20 +68,20 @@ def main():
|
||||
conf = Configs()
|
||||
# Load configurations
|
||||
experiment.configs(conf, {
|
||||
'device.cuda_device': 0,
|
||||
|
||||
# 'optimizer.optimizer': 'Noam',
|
||||
# 'optimizer.learning_rate': 1.,
|
||||
# Optimizer
|
||||
'optimizer.optimizer': 'Adam',
|
||||
'optimizer.learning_rate': 2.5e-4,
|
||||
'optimizer.d_model': 512,
|
||||
|
||||
# Transformer embedding size
|
||||
'transformer.d_model': 512,
|
||||
|
||||
# Training epochs and batch size
|
||||
'epochs': 1000,
|
||||
'train_batch_size': 64,
|
||||
|
||||
# Augment CIFAR 10 images for training
|
||||
'train_dataset': 'cifar10_train_augmented',
|
||||
# Do not augment CIFAR 10 images for validation
|
||||
'valid_dataset': 'cifar10_valid_no_augment',
|
||||
})
|
||||
# Set model for saving/loading
|
||||
|
||||
32
labml_nn/transformers/vit/readme.md
Normal file
32
labml_nn/transformers/vit/readme.md
Normal file
@ -0,0 +1,32 @@
|
||||
# [Vision Transformer (ViT)](https://nn.labml.ai/transformer/vit/index.html)
|
||||
|
||||
This is a [PyTorch](https://pytorch.org) implementation of the paper
|
||||
[An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale](https://arxiv.org/abs/2010.11929).
|
||||
|
||||
Vision transformer applies a pure transformer to images
|
||||
without any convolution layers.
|
||||
They split the image into patches and apply a transformer on patch embeddings.
|
||||
[Patch embeddings](https://nn.labml.ai/transformer/vit/index.html#PathEmbeddings) are generated by applying a simple linear transformation
|
||||
to the flattened pixel values of the patch.
|
||||
Then a standard transformer encoder is fed with the patch embeddings, along with a
|
||||
classification token `[CLS]`.
|
||||
The encoding on the `[CLS]` token is used to classify the image with an MLP.
|
||||
|
||||
When feeding the transformer with the patches, learned positional embeddings are
|
||||
added to the patch embeddings, because the patch embeddings do not have any information
|
||||
about where that patch is from.
|
||||
The positional embeddings are a set of vectors for each patch location that get trained
|
||||
with gradient descent along with other parameters.
|
||||
|
||||
ViTs perform well when they are pre-trained on large datasets.
|
||||
The paper suggests pre-training them with an MLP classification head and
|
||||
then using a single linear layer when fine-tuning.
|
||||
The paper beats SOTA with a ViT pre-trained on a 300 million image dataset.
|
||||
They also use higher resolution images during inference while keeping the
|
||||
patch size the same.
|
||||
The positional embeddings for new patch locations are calculated by interpolating
|
||||
learning positional embeddings.
|
||||
|
||||
Here's [an experiment](https://nn.labml.ai/transformer/vit/experiment.html) that trains ViT on CIFAR-10.
|
||||
This doesn't do very well because it's trained on a small dataset.
|
||||
It's a simple experiment that anyone can run and play with ViTs.
|
||||
@ -37,6 +37,7 @@ implementations almost weekly.
|
||||
* [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
|
||||
* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
|
||||
* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
|
||||
* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
|
||||
|
||||
#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
|
||||
|
||||
|
||||
2
setup.py
2
setup.py
@ -5,7 +5,7 @@ with open("readme.md", "r") as f:
|
||||
|
||||
setuptools.setup(
|
||||
name='labml-nn',
|
||||
version='0.4.102',
|
||||
version='0.4.103',
|
||||
author="Varuna Jayasiri, Nipun Wijerathne",
|
||||
author_email="vpjayasiri@gmail.com, hnipun@gmail.com",
|
||||
description="A collection of PyTorch implementations of neural network architectures and layers.",
|
||||
|
||||
Reference in New Issue
Block a user