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<div class="section" id="torch-hub">
<h1>torch.hub<a class="headerlink" href="#torch-hub" title="Permalink to this headline">¶</a></h1>
<p>Pytorch Hub is a pre-trained model repository designed to facilitate research reproducibility.</p>
<div class="section" id="publishing-models">
<h2>Publishing models<a class="headerlink" href="#publishing-models" title="Permalink to this headline">¶</a></h2>
<p>Pytorch Hub supports publishing pre-trained models(model definitions and pre-trained weights)
to a github repository by adding a simple <code class="docutils literal"><span class="pre">hubconf.py</span></code> file;</p>
<p><code class="docutils literal"><span class="pre">hubconf.py</span></code> can have multiple entrypoints. Each entrypoint is defined as a python function with
the following signature.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">entrypoint_name</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="o">...</span>
</pre></div>
</div>
<div class="section" id="how-to-implement-an-entrypoint">
<h3>How to implement an entrypoint?<a class="headerlink" href="#how-to-implement-an-entrypoint" title="Permalink to this headline">¶</a></h3>
<p>Here is a code snipet from pytorch/vision repository, which specifies an entrypoint
for <code class="docutils literal"><span class="pre">resnet18</span></code> model. You can see a full script in
<a class="reference external" href="https://github.com/pytorch/vision/blob/master/hubconf.py">pytorch/vision repo</a></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">dependencies</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'torch'</span><span class="p">,</span> <span class="s1">'math'</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Resnet18 model</span>
<span class="sd"> pretrained (bool): a recommended kwargs for all entrypoints</span>
<span class="sd"> args & kwargs are arguments for the function</span>
<span class="sd"> """</span>
<span class="c1">######## Call the model in the repo ###############</span>
<span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="k">import</span> <span class="n">resnet18</span> <span class="k">as</span> <span class="n">_resnet18</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">_resnet18</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="c1">######## End of call ##############################</span>
<span class="c1"># The following logic is REQUIRED</span>
<span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span>
<span class="c1"># For weights saved in local repo</span>
<span class="c1"># model.load_state_dict(<path_to_saved_file>)</span>
<span class="c1"># For weights saved elsewhere</span>
<span class="n">checkpoint</span> <span class="o">=</span> <span class="s1">'https://download.pytorch.org/models/resnet18-5c106cde.pth'</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_zoo</span><span class="o">.</span><span class="n">load_url</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="k">return</span> <span class="n">model</span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">dependencies</span></code> variable is a <strong>list</strong> of package names required to to run the model.</li>
<li>Pretrained weights can either be stored local in the github repo, or loadable by
<code class="docutils literal"><span class="pre">model_zoo.load()</span></code>.</li>
<li><code class="docutils literal"><span class="pre">pretrained</span></code> controls whether to load the pre-trained weights provided by repo owners.</li>
<li><code class="docutils literal"><span class="pre">args</span></code> and <code class="docutils literal"><span class="pre">kwargs</span></code> are passed along to the real callable function.</li>
<li>Docstring of the function works as a help message, explaining what does the model do and what
are the allowed arguments.</li>
<li>Entrypoint function should <strong>ALWAYS</strong> return a model(nn.module).</li>
</ul>
</div>
<div class="section" id="important-notice">
<h3>Important Notice<a class="headerlink" href="#important-notice" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li>The published models should be at least in a branch/tag. It can’t be a random commit.</li>
</ul>
</div>
</div>
<div class="section" id="loading-models-from-hub">
<h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub" title="Permalink to this headline">¶</a></h2>
<p>Users can load the pre-trained models using <code class="docutils literal"><span class="pre">torch.hub.load()</span></code> API.</p>
<span class="target" id="module-torch.hub"></span><dl class="function">
<dt id="torch.hub.load">
<code class="descclassname">torch.hub.</code><code class="descname">load</code><span class="sig-paren">(</span><em>github</em>, <em>model</em>, <em>force_reload=False</em>, <em>*args</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.hub.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a model from a github repo, with pretrained weights.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>github</strong> – Required, a string with format “repo_owner/repo_name[:tag_name]” with an optional
tag/branch. The default branch is <cite>master</cite> if not specified.
Example: ‘pytorch/vision[:hub]’</li>
<li><strong>model</strong> – Required, a string of entrypoint name defined in repo’s hubconf.py</li>
<li><strong>force_reload</strong> – Optional, whether to discard the existing cache and force a fresh download.
Default is <cite>False</cite>.</li>
<li><strong>*args</strong> – Optional, the corresponding args for callable <cite>model</cite>.</li>
<li><strong>**kwargs</strong> – Optional, the corresponding kwargs for callable <cite>model</cite>.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a single model with corresponding pretrained weights.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<p>Here’s an example loading <code class="docutils literal"><span class="pre">resnet18</span></code> entrypoint from <code class="docutils literal"><span class="pre">pytorch/vision</span></code> repo.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">hub_model</span> <span class="o">=</span> <span class="n">hub</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
<span class="s1">'pytorch/vision:master'</span><span class="p">,</span> <span class="c1"># repo_owner/repo_name:branch</span>
<span class="s1">'resnet18'</span><span class="p">,</span> <span class="c1"># entrypoint</span>
<span class="mi">1234</span><span class="p">,</span> <span class="c1"># args for callable [not applicable to resnet]</span>
<span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># kwargs for callable</span>
</pre></div>
</div>
<div class="section" id="where-are-my-downloaded-model-weights-saved">
<h3>Where are my downloaded model & weights saved?<a class="headerlink" href="#where-are-my-downloaded-model-weights-saved" title="Permalink to this headline">¶</a></h3>
<p>The locations are used in the order of</p>
<ul class="simple">
<li>hub_dir: user specified path. It can be set in the following ways:
- Setting the environment variable <code class="docutils literal"><span class="pre">TORCH_HUB_DIR</span></code>
- Calling <code class="docutils literal"><span class="pre">hub.set_dir(<PATH_TO_HUB_DIR>)</span></code></li>
<li><code class="docutils literal"><span class="pre">~/.torch/hub</span></code></li>
</ul>
<dl class="function">
<dt id="torch.hub.set_dir">
<code class="descclassname">torch.hub.</code><code class="descname">set_dir</code><span class="sig-paren">(</span><em>d</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#set_dir"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.hub.set_dir" title="Permalink to this definition">¶</a></dt>
<dd><p>Optionally set hub_dir to a local dir to save downloaded models & weights.</p>
<p>If this argument is not set, env variable <cite>TORCH_HUB_DIR</cite> will be searched first,
<cite>~/.torch/hub</cite> will be created and used as fallback.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>d</strong> – path to a local folder to save downloaded models & weights.</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="caching-logic">
<h3>Caching logic<a class="headerlink" href="#caching-logic" title="Permalink to this headline">¶</a></h3>
<p>By default, we don’t clean up files after loading it. Hub uses the cache by default if it already exists in <code class="docutils literal"><span class="pre">hub_dir</span></code>.</p>
<p>Users can force a reload by calling <code class="docutils literal"><span class="pre">hub.load(...,</span> <span class="pre">force_reload=True)</span></code>. This will delete
the existing github folder and downloaded weights, reinitialize a fresh download. This is useful
when updates are published to the same branch, users can keep up with the latest release.</p>
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