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Fix doctests

This commit is contained in:
Carlos Mocholí 2022-05-25 16:38:39 +02:00 committed by lexierule
parent 41b0ffe1f4
commit 8d0266f958
1 changed files with 6 additions and 26 deletions
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32
docs/source/starter/core_guide.rst
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@ -1,9 +1,3 @@
.. testsetup:: *
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.core.datamodule import LightningDataModule
from pytorch_lightning.trainer.trainer import Trainer
.. _introduction_guide: .. _introduction_guide:
######################### #########################
@ -72,7 +66,7 @@ Let's first start with the model. In this case, we'll design a 3-layer neural ne
import torch import torch
from torch.nn import functional as F from torch.nn import functional as F
from torch import nn from torch import nn
from pytorch_lightning.core.lightning import LightningModule from pytorch_lightning import LightningModule
class LitMNIST(LightningModule): class LitMNIST(LightningModule):
@ -187,14 +181,13 @@ Data
Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIST. Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIST.
.. testcode:: .. code-block:: python
:skipif: not _TORCHVISION_AVAILABLE
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
import os import os
from torchvision import datasets, transforms
from pytorch_lightning import Trainer from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
# transforms # transforms
# prepare transforms standard to MNIST # prepare transforms standard to MNIST
@ -204,19 +197,6 @@ Lightning operates on pure dataloaders. Here's the PyTorch code for loading MNIS
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform) mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
mnist_train = DataLoader(mnist_train, batch_size=64) mnist_train = DataLoader(mnist_train, batch_size=64)
.. testoutput::
:hide:
:skipif: os.path.isdir(os.path.join(os.getcwd(), 'MNIST')) or not _TORCHVISION_AVAILABLE
Downloading ...
Extracting ...
Downloading ...
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Processing...
Done!
You can use DataLoaders in three ways: You can use DataLoaders in three ways:
1. Pass DataLoaders to .fit() 1. Pass DataLoaders to .fit()