LightningDataModule¶
A datamodule is a shareable, reusable class that encapsulates all the steps needed to process data:
A datamodule encapsulates the five steps involved in data processing in PyTorch:
Download / tokenize / process.
Clean and (maybe) save to disk.
Load inside
Dataset
.Apply transforms (rotate, tokenize, etc…).
Wrap inside a
DataLoader
.
This class can then be shared and used anywhere:
from pl_bolts.datamodules import CIFAR10DataModule, ImagenetDataModule
model = LitClassifier()
trainer = Trainer()
imagenet = ImagenetDataModule()
trainer.fit(model, imagenet)
cifar10 = CIFAR10DataModule()
trainer.fit(model, cifar10)
Why do I need a DataModule?¶
In normal PyTorch code, the data cleaning/preparation is usually scattered across many files. This makes sharing and reusing the exact splits and transforms across projects impossible.
Datamodules are for you if you ever asked the questions:
what splits did you use?
what transforms did you use?
what normalization did you use?
how did you prepare/tokenize the data?
What is a DataModule¶
A DataModule is simply a collection of a train_dataloader, val_dataloader(s), test_dataloader(s) along with the matching transforms and data processing/downloads steps required.
Here’s a simple PyTorch example:
# regular PyTorch
test_data = MNIST(my_path, train=False, download=True)
train_data = MNIST(my_path, train=True, download=True)
train_data, val_data = random_split(train_data, [55000, 5000])
train_loader = DataLoader(train_data, batch_size=32)
val_loader = DataLoader(val_data, batch_size=32)
test_loader = DataLoader(test_data, batch_size=32)
The equivalent DataModule just organizes the same exact code, but makes it reusable across projects.
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, data_dir: str = "path/to/dir", batch_size: int = 32):
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
def setup(self, stage=None):
self.mnist_test = MNIST(self.data_dir, train=False)
mnist_full = MNIST(self.data_dir, train=True)
self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
def train_dataloader(self):
return DataLoader(self.mnist_train, batch_size=self.batch_size)
def val_dataloader(self):
return DataLoader(self.mnist_val, batch_size=self.batch_size)
def test_dataloader(self):
return DataLoader(self.mnist_test, batch_size=self.batch_size)
But now, as the complexity of your processing grows (transforms, multiple-GPU training), you can let Lightning handle those details for you while making this dataset reusable so you can share with colleagues or use in different projects.
mnist = MNISTDataModule(my_path)
model = LitClassifier()
trainer = Trainer()
trainer.fit(model, mnist)
Here’s a more realistic, complex DataModule that shows how much more reusable the datamodule is.
import pytorch_lightning as pl
from torch.utils.data import random_split, DataLoader
# Note - you must have torchvision installed for this example
from torchvision.datasets import MNIST
from torchvision import transforms
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, data_dir: str = './'):
super().__init__()
self.data_dir = data_dir
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# self.dims is returned when you call dm.size()
# Setting default dims here because we know them.
# Could optionally be assigned dynamically in dm.setup()
self.dims = (1, 28, 28)
def prepare_data(self):
# download
MNIST(self.data_dir, train=True, download=True)
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
if stage == 'fit' or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
# Optionally...
# self.dims = tuple(self.mnist_train[0][0].shape)
# Assign test dataset for use in dataloader(s)
if stage == 'test' or stage is None:
self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
# Optionally...
# self.dims = tuple(self.mnist_test[0][0].shape)
def train_dataloader(self):
return DataLoader(self.mnist_train, batch_size=32)
def val_dataloader(self):
return DataLoader(self.mnist_val, batch_size=32)
def test_dataloader(self):
return DataLoader(self.mnist_test, batch_size=32)
Note
setup
expects a string arg stage
. It is used to separate setup logic for trainer.fit
and trainer.test
.
LightningDataModule API¶
To define a DataModule define 5 methods:
prepare_data (how to download(), tokenize, etc…)
setup (how to split, etc…)
train_dataloader
val_dataloader(s)
test_dataloader(s)
prepare_data¶
Use this method to do things that might write to disk or that need to be done only from a single process in distributed settings.
download
tokenize
etc…
class MNISTDataModule(pl.LightningDataModule):
def prepare_data(self):
# download
MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor())
Warning
prepare_data
is called from a single process (e.g. GPU 0). Do not use it to assign state (self.x = y).
setup¶
There are also data operations you might want to perform on every GPU. Use setup to do things like:
count number of classes
build vocabulary
perform train/val/test splits
apply transforms (defined explicitly in your datamodule or assigned in init)
etc…
import pytorch_lightning as pl
class MNISTDataModule(pl.LightningDataModule):
def setup(self, stage: Optional[str] = None):
# Assign Train/val split(s) for use in Dataloaders
if stage == 'fit' or stage is None:
mnist_full = MNIST(
self.data_dir,
train=True,
download=True,
transform=self.transform
)
self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
self.dims = self.mnist_train[0][0].shape
# Assign Test split(s) for use in Dataloaders
if stage == 'test' or stage is None:
self.mnist_test = MNIST(
self.data_dir,
train=False,
download=True,
transform=self.transform
)
self.dims = getattr(self, 'dims', self.mnist_test[0][0].shape)
Warning
setup is called from every process. Setting state here is okay.
train_dataloader¶
Use this method to generate the train dataloader. Usually you just wrap the dataset you defined in setup
.
import pytorch_lightning as pl
class MNISTDataModule(pl.LightningDataModule):
def train_dataloader(self):
return DataLoader(self.mnist_train, batch_size=64)
val_dataloader¶
Use this method to generate the val dataloader. Usually you just wrap the dataset you defined in setup
.
import pytorch_lightning as pl
class MNISTDataModule(pl.LightningDataModule):
def val_dataloader(self):
return DataLoader(self.mnist_val, batch_size=64)
test_dataloader¶
Use this method to generate the test dataloader. Usually you just wrap the dataset you defined in setup
.
import pytorch_lightning as pl
class MNISTDataModule(pl.LightningDataModule):
def test_dataloader(self):
return DataLoader(self.mnist_test, batch_size=64)
transfer_batch_to_device¶
Override to define how you want to move an arbitrary batch to a device.
class MNISTDataModule(LightningDataModule):
def transfer_batch_to_device(self, batch, device):
x = batch['x']
x = CustomDataWrapper(x)
batch['x'] = x.to(device)
return batch
Note
This hook only runs on single GPU training and DDP (no data-parallel).
on_before_batch_transfer¶
Override to alter or apply augmentations to your batch before it is transferred to the device.
class MNISTDataModule(LightningDataModule):
def on_before_batch_transfer(self, batch, dataloader_idx):
batch['x'] = transforms(batch['x'])
return batch
Warning
Currently dataloader_idx always returns 0 and will be updated to support the true idx in the future.
Note
This hook only runs on single GPU training and DDP (no data-parallel).
on_after_batch_transfer¶
Override to alter or apply augmentations to your batch after it is transferred to the device.
class MNISTDataModule(LightningDataModule):
def on_after_batch_transfer(self, batch, dataloader_idx):
batch['x'] = gpu_transforms(batch['x'])
return batch
Warning
Currently dataloader_idx
always returns 0 and will be updated to support the true idx
in the future.
Note
This hook only runs on single GPU training and DDP (no data-parallel). This hook
will also be called when using CPU device, so adding augmentations here or in
on_before_batch_transfer
means the same thing.
Note
To decouple your data from transforms you can parametrize them via __init__
.
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, train_transforms, val_transforms, test_transforms):
super().__init__()
self.train_transforms = train_transforms
self.val_transforms = val_transforms
self.test_transforms = test_transforms
Using a DataModule¶
The recommended way to use a DataModule is simply:
dm = MNISTDataModule()
model = Model()
trainer.fit(model, dm)
trainer.test(datamodule=dm)
If you need information from the dataset to build your model, then run prepare_data and setup manually (Lightning still ensures the method runs on the correct devices)
dm = MNISTDataModule()
dm.prepare_data()
dm.setup('fit')
model = Model(num_classes=dm.num_classes, width=dm.width, vocab=dm.vocab)
trainer.fit(model, dm)
dm.setup('test')
trainer.test(datamodule=dm)
Datamodules without Lightning¶
You can of course use DataModules in plain PyTorch code as well.
# download, etc...
dm = MNISTDataModule()
dm.prepare_data()
# splits/transforms
dm.setup('fit')
# use data
for batch in dm.train_dataloader():
...
for batch in dm.val_dataloader():
...
# lazy load test data
dm.setup('test')
for batch in dm.test_dataloader():
...
But overall, DataModules encourage reproducibility by allowing all details of a dataset to be specified in a unified structure.