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William Falcon
2019-08-13 15:21:24 -05:00
parent d32c548cf8
commit 9b8a06bfdb
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LightningModule/methods/index.html
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@@ -564,26 +564,39 @@
<hr />
<h3 id="freeze">freeze</h3>
<p>Freeze all params for inference</p>
<pre><code class="python">model = MyLightningModule(...)
model.freeze()
</code></pre>
<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 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>
<pre><code class="python">pretrained_model = MyLightningModule.load_from_metrics(
weights_path='/path/to/pytorch_checkpoint.ckpt',
tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
on_gpu=True,
map_location=None
)
<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>
# predict
pretrained_model.eval()
pretrained_model.freeze()
y_hat = pretrained_model(x)
</code></pre>
<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>
@@ -617,9 +630,11 @@ y_hat = pretrained_model(x)
<hr />
<h3 id="unfreeze">unfreeze</h3>
<p>Unfreeze all params for inference</p>
<pre><code class="python">model = MyLightningModule(...)
model.unfreeze()
</code></pre>
<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>