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<div class="section" id="torch-load">
<h1>torch.load<a class="headerlink" href="#torch-load" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.load">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param">f</em>, <em class="sig-param">map_location=None</em>, <em class="sig-param">pickle_module=pickle</em>, <em class="sig-param">**pickle_load_args</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads an object saved with <a class="reference internal" href="torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p>
<p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses Python’s unpickling facilities but treats storages,
which underlie tensors, specially. They are first deserialized on the
CPU and are then moved to the device they were saved from. If this fails
(e.g. because the run time system doesn’t have certain devices), an exception
is raised. However, storages can be dynamically remapped to an alternative
set of devices using the <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> argument.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> is a callable, it will be called once for each serialized
storage with two arguments: storage and location. The storage argument
will be the initial deserialization of the storage, residing on the CPU.
Each serialized storage has a location tag associated with it which
identifies the device it was saved from, and this tag is the second
argument passed to <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code>. The builtin location tags are <code class="docutils literal notranslate"><span class="pre">'cpu'</span></code>
for CPU tensors and <code class="docutils literal notranslate"><span class="pre">'cuda:device_id'</span></code> (e.g. <code class="docutils literal notranslate"><span class="pre">'cuda:2'</span></code>) for CUDA tensors.
<code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> should return either <code class="docutils literal notranslate"><span class="pre">None</span></code> or a storage. If
<code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> returns a storage, it will be used as the final deserialized
object, already moved to the right device. Otherwise, <a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> will
fall back to the default behavior, as if <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> wasn’t specified.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> is a <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> object or a string containing
a device tag, it indicates the location where all tensors should be loaded.</p>
<p>Otherwise, if <code class="xref py py-attr docutils literal notranslate"><span class="pre">map_location</span></code> is a dict, it will be used to remap location tags
appearing in the file (keys), to ones that specify where to put the
storages (values).</p>
<p>User extensions can register their own location tags and tagging and
deserialization methods using <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.serialization.register_package()</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>f</strong> – a file-like object (has to implement <code class="xref py py-meth docutils literal notranslate"><span class="pre">read()</span></code>, :meth`readline`, :meth`tell`, and :meth`seek`),
or a string or os.PathLike object containing a file name</p></li>
<li><p><strong>map_location</strong> – a function, <a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, string or a dict specifying how to remap storage
locations</p></li>
<li><p><strong>pickle_module</strong> – module used for unpickling metadata and objects (has to
match the <code class="xref py py-attr docutils literal notranslate"><span class="pre">pickle_module</span></code> used to serialize file)</p></li>
<li><p><strong>pickle_load_args</strong> – (Python 3 only) optional keyword arguments passed over to
<code class="xref py py-func docutils literal notranslate"><span class="pre">pickle_module.load()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">pickle_module.Unpickler()</span></code>, e.g.,
<code class="xref py py-attr docutils literal notranslate"><span class="pre">errors=...</span></code>.</p></li>
</ul>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses <code class="docutils literal notranslate"><span class="pre">pickle</span></code> module implicitly, which is known to be insecure.
It is possible to construct malicious pickle data which will execute arbitrary code
during unpickling. Never load data that could have come from an untrusted
source, or that could have been tampered with. <strong>Only load data you trust</strong>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When you call <a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> on a file which contains GPU tensors, those tensors
will be loaded to GPU by default. You can call <code class="docutils literal notranslate"><span class="pre">torch.load(..,</span> <span class="pre">map_location='cpu')</span></code>
and then <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> to avoid GPU RAM surge when loading a model checkpoint.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>By default, we decode byte strings as <code class="docutils literal notranslate"><span class="pre">utf-8</span></code>. This is to avoid a common error
case <code class="docutils literal notranslate"><span class="pre">UnicodeDecodeError:</span> <span class="pre">'ascii'</span> <span class="pre">codec</span> <span class="pre">can't</span> <span class="pre">decode</span> <span class="pre">byte</span> <span class="pre">0x...</span></code>
when loading files saved by Python 2 in Python 3. If this default
is incorrect, you may use an extra <code class="xref py py-attr docutils literal notranslate"><span class="pre">encoding</span></code> keyword argument to specify how
these objects should be loaded, e.g., <code class="xref py py-attr docutils literal notranslate"><span class="pre">encoding='latin1'</span></code> decodes them
to strings using <code class="docutils literal notranslate"><span class="pre">latin1</span></code> encoding, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">encoding='bytes'</span></code> keeps them
as byte arrays which can be decoded later with <code class="docutils literal notranslate"><span class="pre">byte_array.decode(...)</span></code>.</p>
</div>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">)</span>
<span class="go"># Load all tensors onto the CPU</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cpu'</span><span class="p">))</span>
<span class="go"># Load all tensors onto the CPU, using a function</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="k">lambda</span> <span class="n">storage</span><span class="p">,</span> <span class="n">loc</span><span class="p">:</span> <span class="n">storage</span><span class="p">)</span>
<span class="go"># Load all tensors onto GPU 1</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="k">lambda</span> <span class="n">storage</span><span class="p">,</span> <span class="n">loc</span><span class="p">:</span> <span class="n">storage</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="go"># Map tensors from GPU 1 to GPU 0</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'tensors.pt'</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="p">{</span><span class="s1">'cuda:1'</span><span class="p">:</span><span class="s1">'cuda:0'</span><span class="p">})</span>
<span class="go"># Load tensor from io.BytesIO object</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'tensor.pt'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="go"> buffer = io.BytesIO(f.read())</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
<span class="go"># Load a module with 'ascii' encoding for unpickling</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'module.pt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'ascii'</span><span class="p">)</span>
</pre></div>
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