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William Falcon
2019-08-07 12:15:50 -05:00
parent 4e9d3fc53b
commit 6f0d078ce3
5 changed files with 62 additions and 20 deletions
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LightningModule/RequiredTrainerInterface/index.html
+14 -14
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@@ -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)