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<h1>torch.jit.trace_module<a class="headerlink" href="#torch-jit-trace-module" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.jit.trace_module">
<code class="sig-prename descclassname">torch.jit.</code><code class="sig-name descname">trace_module</code><span class="sig-paren">(</span><em class="sig-param">mod</em>, <em class="sig-param">inputs</em>, <em class="sig-param">optimize=None</em>, <em class="sig-param">check_trace=True</em>, <em class="sig-param">check_inputs=None</em>, <em class="sig-param">check_tolerance=1e-05</em>, <em class="sig-param">strict=True</em>, <em class="sig-param">_force_outplace=False</em>, <em class="sig-param">_module_class=None</em>, <em class="sig-param">_compilation_unit=<torch._C.CompilationUnit object></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace_module"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
<dd><p>Trace a module and return an executable <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> that will be optimized
using just-in-time compilation. When a module is passed to <a class="reference internal" href="torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a>, only
the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method is run and traced. With <code class="docutils literal notranslate"><span class="pre">trace_module</span></code>, you can specify a dictionary of
method names to example inputs to trace (see the <code class="docutils literal notranslate"><span class="pre">inputs</span></code>) argument below.</p>
<p>See <a class="reference internal" href="torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a> for more information on tracing.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mod</strong> (<a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – A <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> containing methods whose names are
specified in <code class="docutils literal notranslate"><span class="pre">inputs</span></code>. The given methods will be compiled
as a part of a single <cite>ScriptModule</cite>.</p></li>
<li><p><strong>inputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)"><em>dict</em></a>) – A dict containing sample inputs indexed by method names in <code class="docutils literal notranslate"><span class="pre">mod</span></code>.
The inputs will be passed to methods whose names correspond to inputs’
keys while tracing.
<code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">'forward'</span> <span class="pre">:</span> <span class="pre">example_forward_input,</span> <span class="pre">'method2':</span> <span class="pre">example_method2_input}</span></code></p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>check_trace</strong> (<code class="docutils literal notranslate"><span class="pre">bool</span></code>, optional) – Check if the same inputs run through
traced code produce the same outputs. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>. You might want
to disable this if, for example, your network contains non-
deterministic ops or if you are sure that the network is correct despite
a checker failure.</p></li>
<li><p><strong>check_inputs</strong> (<em>list of dicts</em><em>, </em><em>optional</em>) – A list of dicts of input arguments that should be used
to check the trace against what is expected. Each tuple
is equivalent to a set of input arguments that would
be specified in <code class="docutils literal notranslate"><span class="pre">inputs</span></code>. For best results, pass in a
set of checking inputs representative of the space of
shapes and types of inputs you expect the network to see.
If not specified, the original <code class="docutils literal notranslate"><span class="pre">inputs</span></code> are used for checking</p></li>
<li><p><strong>check_tolerance</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – Floating-point comparison tolerance to use in the checker procedure.
This can be used to relax the checker strictness in the event that
results diverge numerically for a known reason, such as operator fusion.</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> object with a single <code class="docutils literal notranslate"><span class="pre">forward</span></code> method containing the traced code.
When <code class="docutils literal notranslate"><span class="pre">func</span></code> is a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, the returned <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> will have the same set of
sub-modules and parameters as <code class="docutils literal notranslate"><span class="pre">func</span></code>.</p>
</dd>
</dl>
<p>Example (tracing a module with multiple methods):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">weighted_kernel_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span>
<span class="k">return</span> <span class="n">weight</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="o">.</span><span class="n">weight</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">Net</span><span class="p">()</span>
<span class="n">example_weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="n">example_forward_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="c1"># Trace a specific method and construct `ScriptModule` with</span>
<span class="c1"># a single `forward` method</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">forward</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
<span class="c1"># Trace a module (implicitly traces `forward`) and construct a</span>
<span class="c1"># `ScriptModule` with a single `forward` method</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">example_forward_input</span><span class="p">)</span>
<span class="c1"># Trace specific methods on a module (specified in `inputs`), constructs</span>
<span class="c1"># a `ScriptModule` with `forward` and `weighted_kernel_sum` methods</span>
<span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'forward'</span> <span class="p">:</span> <span class="n">example_forward_input</span><span class="p">,</span> <span class="s1">'weighted_kernel_sum'</span> <span class="p">:</span> <span class="n">example_weight</span><span class="p">}</span>
<span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace_module</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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