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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 notranslate"><span class="pre">hubconf.py</span></code> file;</p>
<p><code class="docutils literal notranslate"><span class="pre">hubconf.py</span></code> can have multiple entrypoints. Each entrypoint is defined as a python function
(example: a pre-trained model you want to publish).</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">entrypoint_name</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"># args & kwargs are optional, for models which take positional/keyword arguments.</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 snippet specifies an entrypoint for <code class="docutils literal notranslate"><span class="pre">resnet18</span></code> model if we expand
the implementation in <code class="docutils literal notranslate"><span class="pre">pytorch/vision/hubconf.py</span></code>.
In most case importing the right function in <code class="docutils literal notranslate"><span class="pre">hubconf.py</span></code> is sufficient. Here we
just want to use the expanded version as an example to show how it works.
You can see the 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 notranslate"><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="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="c1"># resnet18 is the name of entrypoint</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">kwargs</span><span class="p">):</span>
<span class="sd">""" # This docstring shows up in hub.help()</span>
<span class="sd"> Resnet18 model</span>
<span class="sd"> pretrained (bool): kwargs, load pretrained weights into the model</span>
<span class="sd"> """</span>
<span class="c1"># Call the model, load pretrained weights</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">_resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
</pre></div>
</div>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">dependencies</span></code> variable is a <strong>list</strong> of package names required to <strong>load</strong> the model. Note this might
be slightly different from dependencies required for training a model.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> are passed along to the real callable function.</p></li>
<li><p>Docstring of the function works as a help message. It explains what does the model do and what
are the allowed positional/keyword arguments. It’s highly recommended to add a few examples here.</p></li>
<li><p>Entrypoint function can either return a model(nn.module), or auxiliary tools to make the user workflow smoother, e.g. tokenizers.</p></li>
<li><p>Callables prefixed with underscore are considered as helper functions which won’t show up in <code class="docutils literal notranslate"><span class="pre">torch.hub.list()</span></code>.</p></li>
<li><p>Pretrained weights can either be stored locally in the github repo, or loadable by
<code class="docutils literal notranslate"><span class="pre">torch.hub.load_state_dict_from_url()</span></code>. If less than 2GB, it’s recommended to attach it to a <a class="reference external" href="https://help.github.com/en/articles/distributing-large-binaries">project release</a>
and use the url from the release.
In the example above <code class="docutils literal notranslate"><span class="pre">torchvision.models.resnet.resnet18</span></code> handles <code class="docutils literal notranslate"><span class="pre">pretrained</span></code>, alternatively you can put the following logic in the entrypoint definition.</p></li>
</ul>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span>
<span class="c1"># For checkpoint saved in local github repo, e.g. <RELATIVE_PATH_TO_CHECKPOINT>=weights/save.pth</span>
<span class="n">dirname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span>
<span class="n">checkpoint</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dirname</span><span class="p">,</span> <span class="o"><</span><span class="n">RELATIVE_PATH_TO_CHECKPOINT</span><span class="o">></span><span class="p">)</span>
<span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>
<span class="c1"># For checkpoint 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">torch</span><span class="o">.</span><span class="n">hub</span><span class="o">.</span><span class="n">load_state_dict_from_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>
</pre></div>
</div>
</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><p>The published models should be at least in a branch/tag. It can’t be a random commit.</p></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>Pytorch Hub provides convenient APIs to explore all available models in hub through <code class="docutils literal notranslate"><span class="pre">torch.hub.list()</span></code>,
show docstring and examples through <code class="docutils literal notranslate"><span class="pre">torch.hub.help()</span></code> and load the pre-trained models using <code class="docutils literal notranslate"><span class="pre">torch.hub.load()</span></code></p>
<span class="target" id="module-torch.hub"></span><dl class="function">
<dt id="torch.hub.list">
<code class="sig-prename descclassname">torch.hub.</code><code class="sig-name descname">list</code><span class="sig-paren">(</span><em class="sig-param">github</em>, <em class="sig-param">force_reload=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#list"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.hub.list" title="Permalink to this definition">¶</a></dt>
<dd><p>List all entrypoints available in <cite>github</cite> hubconf.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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]’</p></li>
<li><p><strong>force_reload</strong> – Optional, whether to discard the existing cache and force a fresh download.
Default is <cite>False</cite>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list of available entrypoint names</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>entrypoints</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">entrypoints</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">hub</span><span class="o">.</span><span class="n">list</span><span class="p">(</span><span class="s1">'pytorch/vision'</span><span class="p">,</span> <span class="n">force_reload</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.hub.help">
<code class="sig-prename descclassname">torch.hub.</code><code class="sig-name descname">help</code><span class="sig-paren">(</span><em class="sig-param">github</em>, <em class="sig-param">model</em>, <em class="sig-param">force_reload=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#help"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.hub.help" title="Permalink to this definition">¶</a></dt>
<dd><p>Show the docstring of entrypoint <cite>model</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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]’</p></li>
<li><p><strong>model</strong> – Required, a string of entrypoint name defined in repo’s hubconf.py</p></li>
<li><p><strong>force_reload</strong> – Optional, whether to discard the existing cache and force a fresh download.
Default is <cite>False</cite>.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">hub</span><span class="o">.</span><span class="n">help</span><span class="p">(</span><span class="s1">'pytorch/vision'</span><span class="p">,</span> <span class="s1">'resnet18'</span><span class="p">,</span> <span class="n">force_reload</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
</pre></div>
</div>
</dd></dl>
<dl class="function">
<dt id="torch.hub.load">
<code class="sig-prename descclassname">torch.hub.</code><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">github</em>, <em class="sig-param">model</em>, <em class="sig-param">*args</em>, <em class="sig-param">**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>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><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]’</p></li>
<li><p><strong>model</strong> – Required, a string of entrypoint name defined in repo’s hubconf.py</p></li>
<li><p><strong>*args</strong> – Optional, the corresponding args for callable <cite>model</cite>.</p></li>
<li><p><strong>force_reload</strong> – Optional, whether to force a fresh download of github repo unconditionally.
Default is <cite>False</cite>.</p></li>
<li><p><strong>**kwargs</strong> – Optional, the corresponding kwargs for callable <cite>model</cite>.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a single model with corresponding pretrained weights.</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">torch</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'</span><span class="p">,</span> <span class="s1">'resnet50'</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="running-a-loaded-model">
<h3>Running a loaded model:<a class="headerlink" href="#running-a-loaded-model" title="Permalink to this headline">¶</a></h3>
<p>Note that <code class="docutils literal notranslate"><span class="pre">*args,</span> <span class="pre">**kwargs</span></code> in <code class="docutils literal notranslate"><span class="pre">torch.load()</span></code> are used to <strong>instantiate</strong> a model.
After you loaded a model, how can you find out what you can do with the model?
A suggested workflow is</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">dir(model)</span></code> to see all avaialble methods of the model.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">help(model.foo)</span></code> to check what arguments <code class="docutils literal notranslate"><span class="pre">model.foo</span></code> takes to run</p></li>
</ul>
<p>To help users explore without refering to documentation back and forth, we strongly
recommend repo owners make function help messages clear and succinct. It’s also helpful
to include a minimal working example.</p>
</div>
<div class="section" id="where-are-my-downloaded-models-saved">
<h3>Where are my downloaded models saved?<a class="headerlink" href="#where-are-my-downloaded-models-saved" title="Permalink to this headline">¶</a></h3>
<p>The locations are used in the order of</p>
<ul class="simple">
<li><p>Calling <code class="docutils literal notranslate"><span class="pre">hub.set_dir(<PATH_TO_HUB_DIR>)</span></code></p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">$TORCH_HOME/hub</span></code>, if environment variable <code class="docutils literal notranslate"><span class="pre">TORCH_HOME</span></code> is set.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">$XDG_CACHE_HOME/torch/hub</span></code>, if environment variable <code class="docutils literal notranslate"><span class="pre">XDG_CACHE_HOME</span></code> is set.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">~/.cache/torch/hub</span></code></p></li>
</ul>
<dl class="function">
<dt id="torch.hub.set_dir">
<code class="sig-prename descclassname">torch.hub.</code><code class="sig-name descname">set_dir</code><span class="sig-paren">(</span><em class="sig-param">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 <code class="docutils literal notranslate"><span class="pre">set_dir</span></code> is not called, default path is <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME/hub</span></code> where
environment variable <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME</span></code> defaults to <code class="docutils literal notranslate"><span class="pre">$XDG_CACHE_HOME/torch</span></code>.
<code class="docutils literal notranslate"><span class="pre">$XDG_CACHE_HOME</span></code> follows the X Design Group specification of the Linux
filesytem layout, with a default value <code class="docutils literal notranslate"><span class="pre">~/.cache</span></code> if the environment
variable is not set.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>d</strong> – path to a local folder to save downloaded models & weights.</p>
</dd>
</dl>
</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 notranslate"><span class="pre">hub_dir</span></code>.</p>
<p>Users can force a reload by calling <code class="docutils literal notranslate"><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>
</div>
<div class="section" id="known-limitations">
<h3>Known limitations:<a class="headerlink" href="#known-limitations" title="Permalink to this headline">¶</a></h3>
<p>Torch hub works by importing the package as if it was installed. There’re some side effects
introduced by importing in Python. For example, you can see new items in Python caches
<code class="docutils literal notranslate"><span class="pre">sys.modules</span></code> and <code class="docutils literal notranslate"><span class="pre">sys.path_importer_cache</span></code> which is normal Python behavior.</p>
<p>A known limitation that worth mentioning here is user <strong>CANNOT</strong> load two different branches of
the same repo in the <strong>same python process</strong>. It’s just like installing two packages with the
same name in Python, which is not good. Cache might join the party and give you surprises if you
actually try that. Of course it’s totally fine to load them in separate processes.</p>
</div>
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