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LightningModule/RequiredTrainerInterface/index.html

@@ -949,9 +949,9 @@ from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 import torchvision.transforms as transforms
 
-import pytorch_lightning as ptl
+import pytorch_lightning as pl
 
-class CoolModel(ptl.LightningModule):
+class CoolModel(pl.LightningModule):
 
     def __init__(self):
         super(CoolModel, self).__init__()
@@ -981,15 +981,15 @@ class CoolModel(ptl.LightningModule):
     def configure_optimizers(self):
         return [torch.optim.Adam(self.parameters(), lr=0.02)]
 
-    @ptl.data_loader
+    @pl.data_loader
     def tng_dataloader(self):
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
-    @ptl.data_loader
+    @pl.data_loader
     def val_dataloader(self):
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 
-    @ptl.data_loader
+    @pl.data_loader
     def test_dataloader(self):
         return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
 </code></pre>
@@ -1248,15 +1248,15 @@ If you saved something with <strong>on_save_checkpoint</strong> this is your cha
 
 <hr />
 <h3 id="tng_dataloader">tng_dataloader</h3>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def tng_dataloader(self)
 </code></pre>
 
-<p>Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
+<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.</p>
 <h5 id="return_3">Return</h5>
 <p>PyTorch DataLoader</p>
 <p><strong>Example</strong></p>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def tng_dataloader(self):
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
     dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
@@ -1270,15 +1270,15 @@ def tng_dataloader(self):
 
 <hr />
 <h3 id="val_dataloader">val_dataloader</h3>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def tng_dataloader(self)
 </code></pre>
 
-<p>Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
+<p>Called by lightning during validation loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
 <h5 id="return_4">Return</h5>
 <p>PyTorch DataLoader</p>
 <p><strong>Example</strong></p>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def val_dataloader(self):
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
     dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -1293,15 +1293,15 @@ def val_dataloader(self):
 
 <hr />
 <h3 id="test_dataloader">test_dataloader</h3>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def test_dataloader(self)
 </code></pre>
 
-<p>Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
+<p>Called by lightning during test loop. Make sure to use the @pl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
 <h5 id="return_5">Return</h5>
 <p>PyTorch DataLoader</p>
 <p><strong>Example</strong></p>
-<pre><code class="python">@ptl.data_loader
+<pre><code class="python">@pl.data_loader
 def test_dataloader(self):
     transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
     dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)

Trainer/hooks/index.html

@@ -439,8 +439,8 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#on_batch_end" title="on_batch_end" class="md-nav__link">
-    on_batch_end
+  <a href="#on_epoch_end" title="on_epoch_end" class="md-nav__link">
+    on_epoch_end
   </a>
   
 </li>
@@ -450,6 +450,13 @@
     on_batch_start
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#on_batch_end" title="on_batch_end" class="md-nav__link">
+    on_batch_end
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -567,8 +574,8 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#on_batch_end" title="on_batch_end" class="md-nav__link">
-    on_batch_end
+  <a href="#on_epoch_end" title="on_epoch_end" class="md-nav__link">
+    on_epoch_end
   </a>
   
 </li>
@@ -578,6 +585,13 @@
     on_batch_start
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#on_batch_end" title="on_batch_end" class="md-nav__link">
+    on_batch_end
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -650,7 +664,7 @@ To enable a hook, simply override the method in your LightningModule and the tra
 </code></pre>
 
 <hr />
-<h4 id="on_batch_end">on_batch_end</h4>
+<h4 id="on_epoch_end">on_epoch_end</h4>
 <p>Called in the training loop at the very end of the epoch.   </p>
 <pre><code class="python">def on_epoch_end(self):
     # do something when the epoch ends 
@@ -663,6 +677,13 @@ To enable a hook, simply override the method in your LightningModule and the tra
     # do something when the batch starts
 </code></pre>
 
+<hr />
+<h4 id="on_batch_end">on_batch_end</h4>
+<p>Called in the training loop after the batch.   </p>
+<pre><code class="python">def on_batch_end(self):
+    # do something when the batch ends 
+</code></pre>
+
 <hr />
 <h4 id="on_pre_performance_check">on_pre_performance_check</h4>
 <p>Called at the very beginning of the validation loop.   </p>

source/_static/coverage.svg

@@ -0,0 +1,21 @@
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+<svg xmlns="http://www.w3.org/2000/svg" width="99" height="20">
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+        <stop offset="1" stop-opacity=".1"/>
+    </linearGradient>
+    <mask id="a">
+        <rect width="99" height="20" rx="3" fill="#fff"/>
+    </mask>
+    <g mask="url(#a)">
+        <path fill="#555" d="M0 0h63v20H0z"/>
+        <path fill="#4c1" d="M63 0h36v20H63z"/>
+        <path fill="url(#b)" d="M0 0h99v20H0z"/>
+    </g>
+    <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
+        <text x="31.5" y="15" fill="#010101" fill-opacity=".3">coverage</text>
+        <text x="31.5" y="14">coverage</text>
+        <text x="80" y="15" fill="#010101" fill-opacity=".3">99%</text>
+        <text x="80" y="14">99%</text>
+    </g>
+</svg>