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mnist_datamodule.py
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mnist_datamodule.py
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from typing import Any, Dict, Optional, Tuple
import torch
from lightning import LightningDataModule
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
from torchvision.datasets import MNIST
from torchvision.transforms import transforms
class MNISTDataModule(LightningDataModule):
"""`LightningDataModule` for the MNIST dataset.
The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples.
It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a
fixed-size image. The original black and white images from NIST were size normalized to fit in a 20x20 pixel box
while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing
technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of
mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.
A `LightningDataModule` implements 7 key methods:
```python
def prepare_data(self):
# Things to do on 1 GPU/TPU (not on every GPU/TPU in DDP).
# Download data, pre-process, split, save to disk, etc...
def setup(self, stage):
# Things to do on every process in DDP.
# Load data, set variables, etc...
def train_dataloader(self):
# return train dataloader
def val_dataloader(self):
# return validation dataloader
def test_dataloader(self):
# return test dataloader
def predict_dataloader(self):
# return predict dataloader
def teardown(self, stage):
# Called on every process in DDP.
# Clean up after fit or test.
```
This allows you to share a full dataset without explaining how to download,
split, transform and process the data.
Read the docs:
https://lightning.ai/docs/pytorch/latest/data/datamodule.html
"""
def __init__(
self,
data_dir: str = "data/",
train_val_test_split: Tuple[int, int, int] = (55_000, 5_000, 10_000),
batch_size: int = 64,
num_workers: int = 0,
pin_memory: bool = False,
) -> None:
"""Initialize a `MNISTDataModule`.
:param data_dir: The data directory. Defaults to `"data/"`.
:param train_val_test_split: The train, validation and test split. Defaults to `(55_000, 5_000, 10_000)`.
:param batch_size: The batch size. Defaults to `64`.
:param num_workers: The number of workers. Defaults to `0`.
:param pin_memory: Whether to pin memory. Defaults to `False`.
"""
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
# data transformations
self.transforms = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
)
self.data_train: Optional[Dataset] = None
self.data_val: Optional[Dataset] = None
self.data_test: Optional[Dataset] = None
@property
def num_classes(self) -> int:
"""Get the number of classes.
:return: The number of MNIST classes (10).
"""
return 10
def prepare_data(self) -> None:
"""Download data if needed. Lightning ensures that `self.prepare_data()` is called only
within a single process on CPU, so you can safely add your downloading logic within. In
case of multi-node training, the execution of this hook depends upon
`self.prepare_data_per_node()`.
Do not use it to assign state (self.x = y).
"""
MNIST(self.hparams.data_dir, train=True, download=True)
MNIST(self.hparams.data_dir, train=False, download=True)
def setup(self, stage: Optional[str] = None) -> None:
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and
`trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after
`self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to
`self.setup()` once the data is prepared and available for use.
:param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``.
"""
# load and split datasets only if not loaded already
if not self.data_train and not self.data_val and not self.data_test:
trainset = MNIST(self.hparams.data_dir, train=True, transform=self.transforms)
testset = MNIST(self.hparams.data_dir, train=False, transform=self.transforms)
dataset = ConcatDataset(datasets=[trainset, testset])
self.data_train, self.data_val, self.data_test = random_split(
dataset=dataset,
lengths=self.hparams.train_val_test_split,
generator=torch.Generator().manual_seed(42),
)
def train_dataloader(self) -> DataLoader[Any]:
"""Create and return the train dataloader.
:return: The train dataloader.
"""
return DataLoader(
dataset=self.data_train,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=True,
)
def val_dataloader(self) -> DataLoader[Any]:
"""Create and return the validation dataloader.
:return: The validation dataloader.
"""
return DataLoader(
dataset=self.data_val,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
def test_dataloader(self) -> DataLoader[Any]:
"""Create and return the test dataloader.
:return: The test dataloader.
"""
return DataLoader(
dataset=self.data_test,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
def teardown(self, stage: Optional[str] = None) -> None:
"""Lightning hook for cleaning up after `trainer.fit()`, `trainer.validate()`,
`trainer.test()`, and `trainer.predict()`.
:param stage: The stage being torn down. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`.
Defaults to ``None``.
"""
pass
def state_dict(self) -> Dict[Any, Any]:
"""Called when saving a checkpoint. Implement to generate and save the datamodule state.
:return: A dictionary containing the datamodule state that you want to save.
"""
return {}
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
"""Called when loading a checkpoint. Implement to reload datamodule state given datamodule
`state_dict()`.
:param state_dict: The datamodule state returned by `self.state_dict()`.
"""
pass
if __name__ == "__main__":
_ = MNISTDataModule()