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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/methods.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
<h1>Methods</h1>
<p>Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.</p>
<hr />
<h3 id="freeze">freeze</h3>
<p>Freeze all params for inference</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
</pre></div>
</td></tr></table>
<hr />
<h3 id="load_from_metrics">load_from_metrics</h3>
<p>This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
such as the one saved by the <code>ModelCheckpoint</code> callback</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4
5
6
7
8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_checkpoint</span><span class="p">(</span>
<span class="n">checkpoint_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span>
<span class="p">)</span>
<span class="c1"># predict</span>
<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h3 id="load_from_metrics_1">load_from_metrics</h3>
<p>If you're using test tube, there is an alternate method which uses the meta_tags.csv
file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
test-tube experiment save_dir. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
2
3
4
5
6
7
8
9
10
11</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_metrics</span><span class="p">(</span>
<span class="n">weights_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span><span class="p">,</span>
<span class="n">tags_csv</span><span class="o">=</span><span class="s1">&#39;/path/to/test_tube/experiment/version/meta_tags.csv&#39;</span><span class="p">,</span>
<span class="n">on_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
<span class="n">map_location</span><span class="o">=</span><span class="bp">None</span>
<span class="p">)</span>
<span class="c1"># predict</span>
<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p><strong>Params</strong> </p>
<table>
<thead>
<tr>
<th>Param</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr>
<td>weights_path</td>
<td>Path to a PyTorch checkpoint</td>
</tr>
<tr>
<td>tags_csv</td>
<td>Path to meta_tags.csv file generated by the test-tube Experiment</td>
</tr>
<tr>
<td>on_gpu</td>
<td>if True, puts model on GPU. Make sure to use transforms option if model devices have changed</td>
</tr>
<tr>
<td>map_location</td>
<td>A dictionary mapping saved weight GPU devices to new GPU devices</td>
</tr>
</tbody>
</table>
<p><strong>Returns</strong> </p>
<p>LightningModule - The pretrained LightningModule</p>
<hr />
<h3 id="unfreeze">unfreeze</h3>
<p>Unfreeze all params for inference</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
</pre></div>
</td></tr></table>
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