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<li class="toctree-l3"><a href="#freeze">freeze</a></li>
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<p>Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.</p>
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<hr />
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<h3 id="freeze">freeze</h3>
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<p>Freeze all params for inference</p>
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<pre><code class="python">model = MyLightningModule(...)
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model.freeze()
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</code></pre>
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<hr />
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<h3 id="load_from_metrics">load_from_metrics</h3>
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<p>This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
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The meta_tags.csv file can be found in the test-tube experiment save_dir. </p>
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<pre><code class="python">pretrained_model = MyLightningModule.load_from_metrics(
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weights_path='/path/to/pytorch_checkpoint.ckpt',
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tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
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on_gpu=True,
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map_location=None
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)
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# predict
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pretrained_model.freeze()
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y_hat = pretrained_model(x)
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</code></pre>
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<p><strong>Params</strong> </p>
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<table>
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<thead>
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<tr>
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<th>Param</th>
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<th>description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>weights_path</td>
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<td>Path to a pytorch checkpoint</td>
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</tr>
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<tr>
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<td>tags_csv</td>
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<td>Path to meta_tags.csv file generated by the test-tube Experiment</td>
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</tr>
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<tr>
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<td>on_gpu</td>
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<td>if True, puts model on GPU. Make sure to use transforms option if model devices have changed</td>
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</tr>
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<tr>
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<td>map_location</td>
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<td>A dictionary mapping saved weight GPU devices to new GPU devices</td>
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</tr>
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</tbody>
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</table>
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<p><strong>Returns</strong> </p>
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<p>LightningModule - The pretrained LightningModule</p>
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<hr />
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<h3 id="unfreeze">unfreeze</h3>
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<p>Unfreeze all params for inference</p>
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<pre><code class="python">model = MyLightningModule(...)
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model.unfreeze()
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</code></pre>
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