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<p><strong>Optional</strong>: </p>
<ul>
<li><a href="./#training_end">training_end</a> </li>
<li><a href="./#validation_step">validation_step</a> </li>
<li><a href="./#validation_end">validation_end</a> </li>
<li><a href="./#test_step">test_step</a> </li>
@@ -1244,6 +1259,153 @@
</pre></div>
</td></tr></table>
<p>If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># Truncated back-propagation through time </span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">,</span> <span class="n">hiddens</span><span class="p">):</span>
<span class="c1"># hiddens are the hiddens from the previous truncated backprop step</span>
</pre></div>
</td></tr></table>
<p>You can also return a -1 instead of a dict to stop the current loop. This is useful if you want to
break out of the current training epoch early.</p>
<hr />
<h3 id="training_end">training_end</h3>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">training_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_step_outputs</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>In certain cases (dp, ddp2), you might want to use all outputs of every process to do something.
For instance, if using negative samples, you could run a batch via dp and use ALL the outputs
for a single softmax across the full batch (ie: the denominator would use the full batch).</p>
<p>In this case you should define training_end to perform those calculations.</p>
<p><strong>Params</strong> </p>
<table>
<thead>
<tr>
<th>Param</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr>
<td>outputs</td>
<td>What you return in training_step.</td>
</tr>
</tbody>
</table>
<p><strong>Return</strong> </p>
<p>Dictionary or OrderedDict </p>
<table>
<thead>
<tr>
<th>key</th>
<th>value</th>
<th>is required</th>
</tr>
</thead>
<tbody>
<tr>
<td>loss</td>
<td>tensor scalar</td>
<td>Y</td>
</tr>
<tr>
<td>progress_bar</td>
<td>Dict for progress bar display. Must have only tensors</td>
<td>N</td>
</tr>
<tr>
<td>log</td>
<td>Dict of metrics to add to logger. Must have only tensors (no images, etc)</td>
<td>N</td>
</tr>
</tbody>
</table>
<p><strong>Example</strong></p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
2
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28</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># WITHOUT training_end</span>
<span class="c1"># if used in DP or DDP2, this batch is 1/nb_gpus large</span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
<span class="c1"># batch is 1/nb_gpus big</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">batch</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">nce_loss</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">&#39;loss&#39;</span><span class="p">:</span> <span class="n">loss</span><span class="p">}</span>
<span class="c1"># --------------</span>
<span class="c1"># with training_end to do softmax over the full batch</span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
<span class="c1"># batch is 1/nb_gpus big</span>
<span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">batch</span>
<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">&#39;out&#39;</span><span class="p">:</span> <span class="n">out</span><span class="p">}</span>
<span class="k">def</span> <span class="nf">training_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outputs</span><span class="p">):</span>
<span class="c1"># this out is now the full size of the batch</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="s1">&#39;out&#39;</span><span class="p">]</span>
<span class="c1"># this softmax now uses the full batch size</span>
<span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">nce_loss</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">return</span> <span class="p">{</span><span class="s1">&#39;loss&#39;</span><span class="p">:</span> <span class="n">loss</span><span class="p">}</span>
</pre></div>
</td></tr></table>
<p>If you define multiple optimizers, this step will also be called with an additional <code>optimizer_idx</code> param. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
4
5
6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># Multiple optimizers (ie: GANs) </span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">,</span> <span class="n">optimizer_idx</span><span class="p">):</span>
<span class="k">if</span> <span class="n">optimizer_idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># do training_step with encoder</span>
<span class="k">if</span> <span class="n">optimizer_idx</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="c1"># do training_step with decoder </span>
</pre></div>
</td></tr></table>
<p>If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># Truncated back-propagation through time </span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">,</span> <span class="n">hiddens</span><span class="p">):</span>
<span class="c1"># hiddens are the hiddens from the previous truncated backprop step</span>
</pre></div>
</td></tr></table>
<p>You can also return a -1 instead of a dict to stop the current loop. This is useful if you want to
break out of the current training epoch early.</p>
<hr />
@@ -1255,7 +1417,7 @@ break out of the current training epoch early.</p>
</td></tr></table>
<p>Called by lightning during training loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed. <br />
If you want to change the data during every epoch DON'T use the data_loader decorator. </p>
If you want to change the data during every epoch DON'T use the data_loader decorator.</p>
<h5 id="return">Return</h5>
<p>PyTorch DataLoader</p>
<p><strong>Example</strong></p>
@@ -2124,7 +2286,7 @@ The <a href="https://williamfalcon.github.io/test-tube/hyperparameter_optimizati
</div>
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<script src="../../assets/javascripts/application.245445c6.js"></script>
<script>app.initialize({version:"1.0.4",url:{base:"../.."}})</script>
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<a href="#freeze" class="md-nav__link">
<a href="#freeze" title="freeze" class="md-nav__link">
freeze
</a>
</li>
<li class="md-nav__item">
<a href="#load_from_metrics" class="md-nav__link">
<a href="#load_from_metrics" title="load_from_metrics" class="md-nav__link">
load_from_metrics
</a>
</li>
<li class="md-nav__item">
<a href="#unfreeze" class="md-nav__link">
<a href="#load_from_metrics_1" title="load_from_metrics" class="md-nav__link">
load_from_metrics
</a>
</li>
<li class="md-nav__item">
<a href="#unfreeze" title="unfreeze" class="md-nav__link">
unfreeze
</a>
@@ -531,21 +538,28 @@
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#freeze" class="md-nav__link">
<a href="#freeze" title="freeze" class="md-nav__link">
freeze
</a>
</li>
<li class="md-nav__item">
<a href="#load_from_metrics" class="md-nav__link">
<a href="#load_from_metrics" title="load_from_metrics" class="md-nav__link">
load_from_metrics
</a>
</li>
<li class="md-nav__item">
<a href="#unfreeze" class="md-nav__link">
<a href="#load_from_metrics_1" title="load_from_metrics" class="md-nav__link">
load_from_metrics
</a>
</li>
<li class="md-nav__item">
<a href="#unfreeze" title="unfreeze" class="md-nav__link">
unfreeze
</a>
@@ -584,8 +598,31 @@
<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>
<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
@@ -716,7 +753,7 @@ The meta_tags.csv file can be found in the test-tube experiment save_dir.
</div>
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<script src="../../assets/javascripts/application.245445c6.js"></script>
<script>app.initialize({version:"1.0.4",url:{base:"../.."}})</script>
LightningModule/properties/index.html
+22 -22
View File
@@ -34,7 +34,7 @@
<meta name="lang:search.tokenizer" content="[\s\-]+">
<link rel="shortcut icon" href="../../assets/images/favicon.png">
<meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.3">
<meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.0">
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<link rel="stylesheet" href="../../assets/stylesheets/application.30686662.css">
<link rel="stylesheet" href="../../assets/stylesheets/application.0284f74d.css">
@@ -171,7 +171,7 @@
<main class="md-main" role="main">
<main class="md-main">
<div class="md-main__inner md-grid" data-md-component="container">
@@ -296,56 +296,56 @@
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#current_epoch" class="md-nav__link">
<a href="#current_epoch" title="current_epoch" class="md-nav__link">
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</a>
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dtype
</a>
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<a href="#logger" title="logger" class="md-nav__link">
logger
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<a href="#global_step" class="md-nav__link">
<a href="#global_step" title="global_step" class="md-nav__link">
global_step
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<a href="#gradient_clip_val" class="md-nav__link">
<a href="#gradient_clip_val" title="gradient_clip_val" class="md-nav__link">
gradient_clip_val
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<a href="#on_gpu" class="md-nav__link">
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@@ -353,7 +353,7 @@
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<a href="#example_input_array" title="example_input_array" class="md-nav__link">
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@@ -579,56 +579,56 @@
<ul class="md-nav__list" data-md-scrollfix>
<li class="md-nav__item">
<a href="#current_epoch" class="md-nav__link">
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dtype
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<a href="#logger" title="logger" class="md-nav__link">
logger
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<a href="#global_step" class="md-nav__link">
<a href="#global_step" title="global_step" class="md-nav__link">
global_step
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<a href="#gradient_clip_val" class="md-nav__link">
<a href="#gradient_clip_val" title="gradient_clip_val" class="md-nav__link">
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<a href="#on_gpu" title="on_gpu" class="md-nav__link">
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trainer
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<a href="#debugging" title="Debugging" class="md-nav__link">
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@@ -636,7 +636,7 @@
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<a href="#example_input_array" class="md-nav__link">
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</a>
@@ -798,7 +798,7 @@ to get a print out of sizes coming into and out of every layer. </p>
</div>
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<script src="../../assets/javascripts/application.245445c6.js"></script>
<script>app.initialize({version:"1.0.4",url:{base:"../.."}})</script>