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<h1><code class="doc-symbol doc-symbol-nav doc-symbol-module"></code> CLIP</h1>
<div class="doc doc-object doc-module">
<div class="doc doc-contents first">
<div class="doc doc-children">
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPImageEncoder" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPImageEncoder</span>
<a href="#refiners.foundationals.clip.CLIPImageEncoder" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPImageEncoder</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">image_size</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">224</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">embedding_dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">768</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a> <span class="n">output_dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>
</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a> <span class="n">patch_size</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a> <span class="n">num_layers</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="n">num_attention_heads</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="n">feedforward_dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">3072</span><span class="p">,</span>
</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="n">layer_norm_eps</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></span> <span class="o">=</span> <span class="mf">1e-05</span><span class="p">,</span>
</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.fluxion.layers.Chain" href="../../fluxion/layers/#refiners.fluxion.layers.Chain">Chain</a></code></p>
<p>Contrastive Language-Image Pretraining (CLIP) image encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>image_size</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The size of the input image.</p>
</div>
</td>
<td>
<code>224</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>embedding_dim</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The dimension of the embedding.</p>
</div>
</td>
<td>
<code>768</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>output_dim</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The dimension of the output.</p>
</div>
</td>
<td>
<code>512</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>patch_size</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The size of the patches.</p>
</div>
</td>
<td>
<code>32</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>num_layers</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The number of layers.</p>
</div>
</td>
<td>
<code>12</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>num_attention_heads</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The number of attention heads.</p>
</div>
</td>
<td>
<code>12</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>feedforward_dim</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The dimension of the feedforward layer.</p>
</div>
</td>
<td>
<code>3072</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>layer_norm_eps</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The epsilon value for normalization.</p>
</div>
</td>
<td>
<code>1e-05</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/image_encoder.py</code></summary>
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<span class="normal"><a href="#__codelineno-0-149">149</a></span>
<span class="normal"><a href="#__codelineno-0-150">150</a></span>
<span class="normal"><a href="#__codelineno-0-151">151</a></span>
<span class="normal"><a href="#__codelineno-0-152">152</a></span>
<span class="normal"><a href="#__codelineno-0-153">153</a></span>
<span class="normal"><a href="#__codelineno-0-154">154</a></span>
<span class="normal"><a href="#__codelineno-0-155">155</a></span>
<span class="normal"><a href="#__codelineno-0-156">156</a></span>
<span class="normal"><a href="#__codelineno-0-157">157</a></span>
<span class="normal"><a href="#__codelineno-0-158">158</a></span>
<span class="normal"><a href="#__codelineno-0-159">159</a></span>
<span class="normal"><a href="#__codelineno-0-160">160</a></span>
<span class="normal"><a href="#__codelineno-0-161">161</a></span>
<span class="normal"><a href="#__codelineno-0-162">162</a></span>
<span class="normal"><a href="#__codelineno-0-163">163</a></span>
<span class="normal"><a href="#__codelineno-0-164">164</a></span>
<span class="normal"><a href="#__codelineno-0-165">165</a></span>
<span class="normal"><a href="#__codelineno-0-166">166</a></span>
<span class="normal"><a href="#__codelineno-0-167">167</a></span>
<span class="normal"><a href="#__codelineno-0-168">168</a></span>
<span class="normal"><a href="#__codelineno-0-169">169</a></span>
<span class="normal"><a href="#__codelineno-0-170">170</a></span>
<span class="normal"><a href="#__codelineno-0-171">171</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a> <span class="bp">self</span><span class="p">,</span>
</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a> <span class="n">image_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">224</span><span class="p">,</span>
</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a> <span class="n">embedding_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">768</span><span class="p">,</span>
</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a> <span class="n">output_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>
</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a> <span class="n">patch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a> <span class="n">num_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a> <span class="n">num_attention_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a> <span class="n">feedforward_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3072</span><span class="p">,</span>
</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a> <span class="n">layer_norm_eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">,</span>
</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize a CLIP image encoder.</span>
</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>
</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a><span class="sd"> Args:</span>
</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="sd"> image_size: The size of the input image.</span>
</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="sd"> embedding_dim: The dimension of the embedding.</span>
</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a><span class="sd"> output_dim: The dimension of the output.</span>
</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="sd"> patch_size: The size of the patches.</span>
</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a><span class="sd"> num_layers: The number of layers.</span>
</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a><span class="sd"> num_attention_heads: The number of attention heads.</span>
</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a><span class="sd"> feedforward_dim: The dimension of the feedforward layer.</span>
</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a><span class="sd"> layer_norm_eps: The epsilon value for normalization.</span>
</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a> <span class="bp">self</span><span class="o">.</span><span class="n">image_size</span> <span class="o">=</span> <span class="n">image_size</span>
</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a> <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a> <span class="bp">self</span><span class="o">.</span><span class="n">output_dim</span> <span class="o">=</span> <span class="n">output_dim</span>
</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a> <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><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a> <span class="bp">self</span><span class="o">.</span><span class="n">num_attention_heads</span> <span class="o">=</span> <span class="n">num_attention_heads</span>
</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a> <span class="bp">self</span><span class="o">.</span><span class="n">feedforward_dim</span> <span class="o">=</span> <span class="n">feedforward_dim</span>
</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a> <span class="n">cls_token_pooling</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a> <span class="n">ViTEmbeddings</span><span class="p">(</span>
</span><span id="__span-0-154"><a id="__codelineno-0-154" name="__codelineno-0-154"></a> <span class="n">image_size</span><span class="o">=</span><span class="n">image_size</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">patch_size</span><span class="o">=</span><span class="n">patch_size</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span>
</span><span id="__span-0-155"><a id="__codelineno-0-155" name="__codelineno-0-155"></a> <span class="p">),</span>
</span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">layer_norm_eps</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
</span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Chain</span><span class="p">(</span>
</span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a> <span class="n">TransformerLayer</span><span class="p">(</span>
</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span>
</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="n">feedforward_dim</span><span class="p">,</span>
</span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="n">num_attention_heads</span><span class="p">,</span>
</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a> <span class="n">layer_norm_eps</span><span class="o">=</span><span class="n">layer_norm_eps</span><span class="p">,</span>
</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a> <span class="p">)</span>
</span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">)</span>
</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a> <span class="p">),</span>
</span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Lambda</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="n">cls_token_pooling</span><span class="p">),</span>
</span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">layer_norm_eps</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
</span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="n">output_dim</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPImageEncoderG" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPImageEncoderG</span>
<a href="#refiners.foundationals.clip.CLIPImageEncoderG" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPImageEncoderG</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.foundationals.clip.image_encoder.CLIPImageEncoder" href="#refiners.foundationals.clip.CLIPImageEncoder">CLIPImageEncoder</a></code></p>
<p>CLIP giant image encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Attributes:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.embedding_dim">embedding_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1664</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.output_dim">output_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1280</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.patch_size">patch_size</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>14</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.num_layers">num_layers</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>48</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.num_attention_heads">num_attention_heads</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>16</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderG.feedforward_dim">feedforward_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>8192</p>
</div>
</td>
</tr>
</tbody>
</table>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/image_encoder.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-223">223</a></span>
<span class="normal"><a href="#__codelineno-0-224">224</a></span>
<span class="normal"><a href="#__codelineno-0-225">225</a></span>
<span class="normal"><a href="#__codelineno-0-226">226</a></span>
<span class="normal"><a href="#__codelineno-0-227">227</a></span>
<span class="normal"><a href="#__codelineno-0-228">228</a></span>
<span class="normal"><a href="#__codelineno-0-229">229</a></span>
<span class="normal"><a href="#__codelineno-0-230">230</a></span>
<span class="normal"><a href="#__codelineno-0-231">231</a></span>
<span class="normal"><a href="#__codelineno-0-232">232</a></span>
<span class="normal"><a href="#__codelineno-0-233">233</a></span>
<span class="normal"><a href="#__codelineno-0-234">234</a></span>
<span class="normal"><a href="#__codelineno-0-235">235</a></span>
<span class="normal"><a href="#__codelineno-0-236">236</a></span>
<span class="normal"><a href="#__codelineno-0-237">237</a></span>
<span class="normal"><a href="#__codelineno-0-238">238</a></span>
<span class="normal"><a href="#__codelineno-0-239">239</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-223"><a id="__codelineno-0-223" name="__codelineno-0-223"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-224"><a id="__codelineno-0-224" name="__codelineno-0-224"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP giant image encoder.</span>
</span><span id="__span-0-225"><a id="__codelineno-0-225" name="__codelineno-0-225"></a>
</span><span id="__span-0-226"><a id="__codelineno-0-226" name="__codelineno-0-226"></a><span class="sd"> Args:</span>
</span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-228"><a id="__codelineno-0-228" name="__codelineno-0-228"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-229"><a id="__codelineno-0-229" name="__codelineno-0-229"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-230"><a id="__codelineno-0-230" name="__codelineno-0-230"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-231"><a id="__codelineno-0-231" name="__codelineno-0-231"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="mi">1664</span><span class="p">,</span>
</span><span id="__span-0-232"><a id="__codelineno-0-232" name="__codelineno-0-232"></a> <span class="n">output_dim</span><span class="o">=</span><span class="mi">1280</span><span class="p">,</span>
</span><span id="__span-0-233"><a id="__codelineno-0-233" name="__codelineno-0-233"></a> <span class="n">patch_size</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
</span><span id="__span-0-234"><a id="__codelineno-0-234" name="__codelineno-0-234"></a> <span class="n">num_layers</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span>
</span><span id="__span-0-235"><a id="__codelineno-0-235" name="__codelineno-0-235"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="mi">8192</span><span class="p">,</span>
</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-238"><a id="__codelineno-0-238" name="__codelineno-0-238"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-239"><a id="__codelineno-0-239" name="__codelineno-0-239"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPImageEncoderH" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPImageEncoderH</span>
<a href="#refiners.foundationals.clip.CLIPImageEncoderH" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPImageEncoderH</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.foundationals.clip.image_encoder.CLIPImageEncoder" href="#refiners.foundationals.clip.CLIPImageEncoder">CLIPImageEncoder</a></code></p>
<p>CLIP huge image encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Attributes:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.embedding_dim">embedding_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1280</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.output_dim">output_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1024</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.patch_size">patch_size</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>14</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.num_layers">num_layers</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>32</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.num_attention_heads">num_attention_heads</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>16</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPImageEncoderH.feedforward_dim">feedforward_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>5120</p>
</div>
</td>
</tr>
</tbody>
</table>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/image_encoder.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-189">189</a></span>
<span class="normal"><a href="#__codelineno-0-190">190</a></span>
<span class="normal"><a href="#__codelineno-0-191">191</a></span>
<span class="normal"><a href="#__codelineno-0-192">192</a></span>
<span class="normal"><a href="#__codelineno-0-193">193</a></span>
<span class="normal"><a href="#__codelineno-0-194">194</a></span>
<span class="normal"><a href="#__codelineno-0-195">195</a></span>
<span class="normal"><a href="#__codelineno-0-196">196</a></span>
<span class="normal"><a href="#__codelineno-0-197">197</a></span>
<span class="normal"><a href="#__codelineno-0-198">198</a></span>
<span class="normal"><a href="#__codelineno-0-199">199</a></span>
<span class="normal"><a href="#__codelineno-0-200">200</a></span>
<span class="normal"><a href="#__codelineno-0-201">201</a></span>
<span class="normal"><a href="#__codelineno-0-202">202</a></span>
<span class="normal"><a href="#__codelineno-0-203">203</a></span>
<span class="normal"><a href="#__codelineno-0-204">204</a></span>
<span class="normal"><a href="#__codelineno-0-205">205</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-190"><a id="__codelineno-0-190" name="__codelineno-0-190"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP huge image encoder.</span>
</span><span id="__span-0-191"><a id="__codelineno-0-191" name="__codelineno-0-191"></a>
</span><span id="__span-0-192"><a id="__codelineno-0-192" name="__codelineno-0-192"></a><span class="sd"> Args:</span>
</span><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-194"><a id="__codelineno-0-194" name="__codelineno-0-194"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-195"><a id="__codelineno-0-195" name="__codelineno-0-195"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-196"><a id="__codelineno-0-196" name="__codelineno-0-196"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-197"><a id="__codelineno-0-197" name="__codelineno-0-197"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="mi">1280</span><span class="p">,</span>
</span><span id="__span-0-198"><a id="__codelineno-0-198" name="__codelineno-0-198"></a> <span class="n">output_dim</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
</span><span id="__span-0-199"><a id="__codelineno-0-199" name="__codelineno-0-199"></a> <span class="n">patch_size</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
</span><span id="__span-0-200"><a id="__codelineno-0-200" name="__codelineno-0-200"></a> <span class="n">num_layers</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
</span><span id="__span-0-201"><a id="__codelineno-0-201" name="__codelineno-0-201"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
</span><span id="__span-0-202"><a id="__codelineno-0-202" name="__codelineno-0-202"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="mi">5120</span><span class="p">,</span>
</span><span id="__span-0-203"><a id="__codelineno-0-203" name="__codelineno-0-203"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPTextEncoder" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPTextEncoder</span>
<a href="#refiners.foundationals.clip.CLIPTextEncoder" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPTextEncoder</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">embedding_dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">768</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">max_sequence_length</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">77</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a> <span class="n">vocabulary_size</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">49408</span><span class="p">,</span>
</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a> <span class="n">num_layers</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a> <span class="n">num_attention_heads</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="n">feedforward_dim</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></span> <span class="o">=</span> <span class="mi">3072</span><span class="p">,</span>
</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="n">layer_norm_eps</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></span> <span class="o">=</span> <span class="mf">1e-05</span><span class="p">,</span>
</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="n">use_quick_gelu</span><span class="p">:</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#bool">bool</a></span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a> <span class="n">tokenizer</span><span class="p">:</span> <span class="n"><span title="refiners.foundationals.clip.tokenizer.CLIPTokenizer">CLIPTokenizer</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.fluxion.layers.Chain" href="../../fluxion/layers/#refiners.fluxion.layers.Chain">Chain</a></code></p>
<p>Contrastive Language-Image Pretraining (CLIP) text encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>embedding_dim</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The embedding dimension.</p>
</div>
</td>
<td>
<code>768</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>max_sequence_length</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The maximum sequence length.</p>
</div>
</td>
<td>
<code>77</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>vocabulary_size</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The vocabulary size.</p>
</div>
</td>
<td>
<code>49408</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>num_layers</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The number of layers.</p>
</div>
</td>
<td>
<code>12</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>num_attention_heads</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The number of attention heads.</p>
</div>
</td>
<td>
<code>12</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>feedforward_dim</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The feedforward dimension.</p>
</div>
</td>
<td>
<code>3072</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>layer_norm_eps</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#float">float</a></code>
</td>
<td>
<div class="doc-md-description">
<p>The epsilon value for layer normalization.</p>
</div>
</td>
<td>
<code>1e-05</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>use_quick_gelu</code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#bool">bool</a></code>
</td>
<td>
<div class="doc-md-description">
<p>Whether to use the quick GeLU activation function.</p>
</div>
</td>
<td>
<code>False</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>tokenizer</code></td>
<td>
<code><span title="refiners.foundationals.clip.tokenizer.CLIPTokenizer">CLIPTokenizer</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The tokenizer.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/text_encoder.py</code></summary>
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<span class="normal"><a href="#__codelineno-0-152">152</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a> <span class="bp">self</span><span class="p">,</span>
</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a> <span class="n">embedding_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">768</span><span class="p">,</span>
</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a> <span class="n">max_sequence_length</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">77</span><span class="p">,</span>
</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a> <span class="n">vocabulary_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">49408</span><span class="p">,</span>
</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a> <span class="n">num_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a> <span class="n">num_attention_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a> <span class="n">feedforward_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3072</span><span class="p">,</span>
</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a> <span class="n">layer_norm_eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">,</span>
</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a> <span class="n">use_quick_gelu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a> <span class="n">tokenizer</span><span class="p">:</span> <span class="n">CLIPTokenizer</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP text encoder.</span>
</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="sd"> Args:</span>
</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="sd"> embedding_dim: The embedding dimension.</span>
</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd"> max_sequence_length: The maximum sequence length.</span>
</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd"> vocabulary_size: The vocabulary size.</span>
</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="sd"> num_layers: The number of layers.</span>
</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a><span class="sd"> num_attention_heads: The number of attention heads.</span>
</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="sd"> feedforward_dim: The feedforward dimension.</span>
</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a><span class="sd"> layer_norm_eps: The epsilon value for layer normalization.</span>
</span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a><span class="sd"> use_quick_gelu: Whether to use the quick GeLU activation function.</span>
</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a><span class="sd"> tokenizer: The tokenizer.</span>
</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a> <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a> <span class="bp">self</span><span class="o">.</span><span class="n">max_sequence_length</span> <span class="o">=</span> <span class="n">max_sequence_length</span>
</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size</span> <span class="o">=</span> <span class="n">vocabulary_size</span>
</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a> <span class="bp">self</span><span class="o">.</span><span class="n">num_layers</span> <span class="o">=</span> <span class="n">num_layers</span>
</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a> <span class="bp">self</span><span class="o">.</span><span class="n">num_attention_heads</span> <span class="o">=</span> <span class="n">num_attention_heads</span>
</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a> <span class="bp">self</span><span class="o">.</span><span class="n">feedforward_dim</span> <span class="o">=</span> <span class="n">feedforward_dim</span>
</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a> <span class="bp">self</span><span class="o">.</span><span class="n">layer_norm_eps</span> <span class="o">=</span> <span class="n">layer_norm_eps</span>
</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a> <span class="bp">self</span><span class="o">.</span><span class="n">use_quick_gelu</span> <span class="o">=</span> <span class="n">use_quick_gelu</span>
</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a> <span class="n">tokenizer</span> <span class="ow">or</span> <span class="n">CLIPTokenizer</span><span class="p">(</span><span class="n">sequence_length</span><span class="o">=</span><span class="n">max_sequence_length</span><span class="p">),</span>
</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Converter</span><span class="p">(</span><span class="n">set_dtype</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a> <span class="n">fl</span><span class="o">.</span><span class="n">Sum</span><span class="p">(</span>
</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a> <span class="n">TokenEncoder</span><span class="p">(</span>
</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a> <span class="n">vocabulary_size</span><span class="o">=</span><span class="n">vocabulary_size</span><span class="p">,</span>
</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span>
</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a> <span class="p">),</span>
</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a> <span class="n">PositionalEncoder</span><span class="p">(</span>
</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a> <span class="n">max_sequence_length</span><span class="o">=</span><span class="n">max_sequence_length</span><span class="p">,</span>
</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span>
</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a> <span class="p">),</span>
</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a> <span class="p">),</span>
</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a> <span class="o">*</span><span class="p">(</span>
</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a> <span class="n">TransformerLayer</span><span class="p">(</span>
</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span>
</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="n">num_attention_heads</span><span class="p">,</span>
</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="n">feedforward_dim</span><span class="p">,</span>
</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a> <span class="n">layer_norm_eps</span><span class="o">=</span><span class="n">layer_norm_eps</span><span class="p">,</span>
</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a> <span class="p">)</span>
</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">)</span>
</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a> <span class="p">),</span>
</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a> <span class="n">fl</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">normalized_shape</span><span class="o">=</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">layer_norm_eps</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">),</span>
</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a> <span class="p">)</span>
</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a> <span class="k">if</span> <span class="n">use_quick_gelu</span><span class="p">:</span>
</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a> <span class="k">for</span> <span class="n">gelu</span><span class="p">,</span> <span class="n">parent</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">predicate</span><span class="o">=</span><span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">_</span><span class="p">:</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">fl</span><span class="o">.</span><span class="n">GeLU</span><span class="p">)):</span>
</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a> <span class="n">parent</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a> <span class="n">old_module</span><span class="o">=</span><span class="n">gelu</span><span class="p">,</span>
</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a> <span class="n">new_module</span><span class="o">=</span><span class="n">fl</span><span class="o">.</span><span class="n">GeLU</span><span class="p">(</span><span class="n">approximation</span><span class="o">=</span><span class="n">fl</span><span class="o">.</span><span class="n">GeLUApproximation</span><span class="o">.</span><span class="n">SIGMOID</span><span class="p">),</span>
</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPTextEncoderG" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPTextEncoderG</span>
<a href="#refiners.foundationals.clip.CLIPTextEncoderG" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPTextEncoderG</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.foundationals.clip.text_encoder.CLIPTextEncoder" href="#refiners.foundationals.clip.CLIPTextEncoder">CLIPTextEncoder</a></code></p>
<p>CLIP giant text encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Attributes:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderG.embedding_dim">embedding_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1280</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderG.num_layers">num_layers</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>32</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderG.num_attention_heads">num_attention_heads</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>20</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderG.feedforward_dim">feedforward_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>5120</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderG.tokenizer">tokenizer</span></code></td>
<td>
<code><span title="refiners.foundationals.clip.tokenizer.CLIPTokenizer">CLIPTokenizer</span></code>
</td>
<td>
<div class="doc-md-description">
<p>CLIPTokenizer(pad_token_id=0)</p>
</div>
</td>
</tr>
</tbody>
</table>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/text_encoder.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-235">235</a></span>
<span class="normal"><a href="#__codelineno-0-236">236</a></span>
<span class="normal"><a href="#__codelineno-0-237">237</a></span>
<span class="normal"><a href="#__codelineno-0-238">238</a></span>
<span class="normal"><a href="#__codelineno-0-239">239</a></span>
<span class="normal"><a href="#__codelineno-0-240">240</a></span>
<span class="normal"><a href="#__codelineno-0-241">241</a></span>
<span class="normal"><a href="#__codelineno-0-242">242</a></span>
<span class="normal"><a href="#__codelineno-0-243">243</a></span>
<span class="normal"><a href="#__codelineno-0-244">244</a></span>
<span class="normal"><a href="#__codelineno-0-245">245</a></span>
<span class="normal"><a href="#__codelineno-0-246">246</a></span>
<span class="normal"><a href="#__codelineno-0-247">247</a></span>
<span class="normal"><a href="#__codelineno-0-248">248</a></span>
<span class="normal"><a href="#__codelineno-0-249">249</a></span>
<span class="normal"><a href="#__codelineno-0-250">250</a></span>
<span class="normal"><a href="#__codelineno-0-251">251</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-235"><a id="__codelineno-0-235" name="__codelineno-0-235"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP giant text encoder.</span>
</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a>
</span><span id="__span-0-238"><a id="__codelineno-0-238" name="__codelineno-0-238"></a><span class="sd"> Args:</span>
</span><span id="__span-0-239"><a id="__codelineno-0-239" name="__codelineno-0-239"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-240"><a id="__codelineno-0-240" name="__codelineno-0-240"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-241"><a id="__codelineno-0-241" name="__codelineno-0-241"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-242"><a id="__codelineno-0-242" name="__codelineno-0-242"></a> <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">CLIPTokenizer</span><span class="p">(</span><span class="n">pad_token_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</span><span id="__span-0-243"><a id="__codelineno-0-243" name="__codelineno-0-243"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-244"><a id="__codelineno-0-244" name="__codelineno-0-244"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="mi">1280</span><span class="p">,</span>
</span><span id="__span-0-245"><a id="__codelineno-0-245" name="__codelineno-0-245"></a> <span class="n">num_layers</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
</span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
</span><span id="__span-0-247"><a id="__codelineno-0-247" name="__codelineno-0-247"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="mi">5120</span><span class="p">,</span>
</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a> <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span>
</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPTextEncoderH" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPTextEncoderH</span>
<a href="#refiners.foundationals.clip.CLIPTextEncoderH" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPTextEncoderH</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.foundationals.clip.text_encoder.CLIPTextEncoder" href="#refiners.foundationals.clip.CLIPTextEncoder">CLIPTextEncoder</a></code></p>
<p>CLIP huge text encoder.</p>
<p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Attributes:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderH.embedding_dim">embedding_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>1024</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderH.num_layers">num_layers</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>23</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderH.num_attention_heads">num_attention_heads</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>16</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderH.feedforward_dim">feedforward_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>4096</p>
</div>
</td>
</tr>
</tbody>
</table>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/text_encoder.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-204">204</a></span>
<span class="normal"><a href="#__codelineno-0-205">205</a></span>
<span class="normal"><a href="#__codelineno-0-206">206</a></span>
<span class="normal"><a href="#__codelineno-0-207">207</a></span>
<span class="normal"><a href="#__codelineno-0-208">208</a></span>
<span class="normal"><a href="#__codelineno-0-209">209</a></span>
<span class="normal"><a href="#__codelineno-0-210">210</a></span>
<span class="normal"><a href="#__codelineno-0-211">211</a></span>
<span class="normal"><a href="#__codelineno-0-212">212</a></span>
<span class="normal"><a href="#__codelineno-0-213">213</a></span>
<span class="normal"><a href="#__codelineno-0-214">214</a></span>
<span class="normal"><a href="#__codelineno-0-215">215</a></span>
<span class="normal"><a href="#__codelineno-0-216">216</a></span>
<span class="normal"><a href="#__codelineno-0-217">217</a></span>
<span class="normal"><a href="#__codelineno-0-218">218</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP huge text encoder.</span>
</span><span id="__span-0-206"><a id="__codelineno-0-206" name="__codelineno-0-206"></a>
</span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a><span class="sd"> Args:</span>
</span><span id="__span-0-208"><a id="__codelineno-0-208" name="__codelineno-0-208"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-209"><a id="__codelineno-0-209" name="__codelineno-0-209"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-211"><a id="__codelineno-0-211" name="__codelineno-0-211"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span>
</span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a> <span class="n">num_layers</span><span class="o">=</span><span class="mi">23</span><span class="p">,</span>
</span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
</span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="mi">4096</span><span class="p">,</span>
</span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
</div>
</div>
<div class="doc doc-object doc-class">
<h2 id="refiners.foundationals.clip.CLIPTextEncoderL" class="doc doc-heading">
<code class="doc-symbol doc-symbol-heading doc-symbol-class"></code> <span class="doc doc-object-name doc-class-name">CLIPTextEncoderL</span>
<a href="#refiners.foundationals.clip.CLIPTextEncoderL" class="headerlink" title="Permanent link">&para;</a></h2>
<div class="language-python doc-signature highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nf">CLIPTextEncoderL</span><span class="p">(</span>
</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a> <span class="n">device</span><span class="p">:</span> <span class="n"><span title="torch.device">device</span></span> <span class="o">|</span> <span class="n"><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a> <span class="n">dtype</span><span class="p">:</span> <span class="n"><span title="torch.dtype">dtype</span></span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="p">)</span>
</span></code></pre></div>
<div class="doc doc-contents ">
<p class="doc doc-class-bases">
Bases: <code><a class="autorefs autorefs-internal" title="refiners.foundationals.clip.text_encoder.CLIPTextEncoder" href="#refiners.foundationals.clip.CLIPTextEncoder">CLIPTextEncoder</a></code></p>
<p>CLIP large text encoder.</p>
<details class="note" open>
<summary>Note</summary>
<p>We replace the GeLU activation function with an approximate GeLU to comply with the original CLIP implementation
of OpenAI (https://github.com/openai/CLIP/blob/a1d0717/clip/model.py#L166)</p>
</details> <p>See <a href="https://arxiv.org/abs/2103.00020">[arXiv:2103.00020] Learning Transferable Visual Models From Natural Language Supervision</a>
for more details.</p>
<p><span class="doc-section-title">Attributes:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderL.embedding_dim">embedding_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>768</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderL.num_layers">num_layers</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>12</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderL.num_attention_heads">num_attention_heads</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>12</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderL.feedforward_dim">feedforward_dim</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#int">int</a></code>
</td>
<td>
<div class="doc-md-description">
<p>3072</p>
</div>
</td>
</tr>
<tr class="doc-section-item">
<td><code><span title="refiners.foundationals.clip.CLIPTextEncoderL.use_quick_gelu">use_quick_gelu</span></code></td>
<td>
<code><a class="autorefs autorefs-external" href="https://docs.python.org/3/library/functions.html#bool">bool</a></code>
</td>
<td>
<div class="doc-md-description">
<p>True</p>
</div>
</td>
</tr>
</tbody>
</table>
<p><span class="doc-section-title">Parameters:</span></p>
<table>
<thead>
<tr>
<th>Name</th>
<th>Type</th>
<th>Description</th>
<th>Default</th>
</tr>
</thead>
<tbody>
<tr class="doc-section-item">
<td><code>device</code></td>
<td>
<code><span title="torch.device">device</span> | <a class="autorefs autorefs-external" href="https://docs.python.org/3/library/stdtypes.html#str">str</a> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch device to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
<tr class="doc-section-item">
<td><code>dtype</code></td>
<td>
<code><span title="torch.dtype">dtype</span> | None</code>
</td>
<td>
<div class="doc-md-description">
<p>The PyTorch data type to use.</p>
</div>
</td>
<td>
<code>None</code>
</td>
</tr>
</tbody>
</table>
<details class="quote">
<summary>Source code in <code>src/refiners/foundationals/clip/text_encoder.py</code></summary>
<div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-173">173</a></span>
<span class="normal"><a href="#__codelineno-0-174">174</a></span>
<span class="normal"><a href="#__codelineno-0-175">175</a></span>
<span class="normal"><a href="#__codelineno-0-176">176</a></span>
<span class="normal"><a href="#__codelineno-0-177">177</a></span>
<span class="normal"><a href="#__codelineno-0-178">178</a></span>
<span class="normal"><a href="#__codelineno-0-179">179</a></span>
<span class="normal"><a href="#__codelineno-0-180">180</a></span>
<span class="normal"><a href="#__codelineno-0-181">181</a></span>
<span class="normal"><a href="#__codelineno-0-182">182</a></span>
<span class="normal"><a href="#__codelineno-0-183">183</a></span>
<span class="normal"><a href="#__codelineno-0-184">184</a></span>
<span class="normal"><a href="#__codelineno-0-185">185</a></span>
<span class="normal"><a href="#__codelineno-0-186">186</a></span>
<span class="normal"><a href="#__codelineno-0-187">187</a></span>
<span class="normal"><a href="#__codelineno-0-188">188</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">|</span> <span class="nb">str</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="p">:</span> <span class="n">DType</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
</span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a><span class="w"> </span><span class="sd">&quot;&quot;&quot;Initialize CLIP large text encoder.</span>
</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>
</span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a><span class="sd"> Args:</span>
</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a><span class="sd"> device: The PyTorch device to use.</span>
</span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a><span class="sd"> dtype: The PyTorch data type to use.</span>
</span><span id="__span-0-179"><a id="__codelineno-0-179" name="__codelineno-0-179"></a><span class="sd"> &quot;&quot;&quot;</span>
</span><span id="__span-0-180"><a id="__codelineno-0-180" name="__codelineno-0-180"></a> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
</span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a> <span class="n">embedding_dim</span><span class="o">=</span><span class="mi">768</span><span class="p">,</span>
</span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a> <span class="n">num_layers</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a> <span class="n">num_attention_heads</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
</span><span id="__span-0-184"><a id="__codelineno-0-184" name="__codelineno-0-184"></a> <span class="n">feedforward_dim</span><span class="o">=</span><span class="mi">3072</span><span class="p">,</span>
</span><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a> <span class="n">use_quick_gelu</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
</span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
</span><span id="__span-0-187"><a id="__codelineno-0-187" name="__codelineno-0-187"></a> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span>
</span><span id="__span-0-188"><a id="__codelineno-0-188" name="__codelineno-0-188"></a> <span class="p">)</span>
</span></code></pre></div></td></tr></table></div>
</details>
<div class="doc doc-children">
</div>
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</div>
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