mnist_datamodule.py

import os
from typing import Any, Tuple

import torch
from pytorch_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):
    """Example of LightningDataModule for MNIST dataset.

    A DataModule implements 5 key methods:
        - prepare_data (things to do on 1 GPU/TPU, not on every GPU/TPU in distributed mode)
        - setup (things to do on every accelerator in distributed mode)
        - train_dataloader (the training dataloader)
        - val_dataloader (the validation dataloader(s))
        - test_dataloader (the test dataloader(s))
    This allows you to share a full dataset without explaining how to download,
    split, transform and process the data.

    Read the docs:
        https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.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,
        persistent_workers: bool = False,
    ) -> None:
        super().__init__()

        # this line allows to access init params with 'self.hparams' attribute
        self.save_hyperparameters()

        # data transformations
        self.transform = transforms.Compose(
            [
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,)),
            ]
        )

        self.data_train: Dataset | None = None
        self.data_val: Dataset | None = None
        self.data_test: Dataset | None = None

    @property
    def num_classes(self) -> int:
        return 10

    def prepare_data(self) -> None:
        """Download data if needed.

        This method is called only from a single GPU.
        Do not use it to assign state (self.x = y).
        """
        mnist_dir = os.path.join(self.hparams.data_dir, "MNIST")
        _download = not os.path.exists(mnist_dir)
        MNIST(self.hparams.data_dir, train=True, download=_download)
        MNIST(self.hparams.data_dir, train=False, download=_download)

    def setup(self, stage: Any = None) -> None:
        """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.

        This method is called by lightning when doing `trainer.fit()` and `trainer.test()`,
        so be careful not to execute the random split twice! The `stage` can be used to
        differentiate whether it's called before trainer.fit()` or `trainer.test()`.
        """
        if stage in ("fit", "test", None):
            trainset = MNIST(
                root=self.hparams.data_dir, train=True, transform=self.transform
            )
            testset = MNIST(
                root=self.hparams.data_dir, train=False, transform=self.transform
            )
            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:
        return DataLoader(
            dataset=self.data_train,
            batch_size=self.hparams.batch_size,
            shuffle=True,
            num_workers=self.hparams.num_workers,
            persistent_workers=self.hparams.persistent_workers,
            pin_memory=self.hparams.pin_memory,
        )

    def val_dataloader(self) -> DataLoader:
        return DataLoader(
            dataset=self.data_val,
            batch_size=self.hparams.batch_size,
            num_workers=self.hparams.num_workers,
            persistent_workers=self.hparams.persistent_workers,
            pin_memory=self.hparams.pin_memory,
        )

    def test_dataloader(self) -> DataLoader:
        return DataLoader(
            dataset=self.data_test,
            batch_size=self.hparams.batch_size,
            num_workers=self.hparams.num_workers,
            persistent_workers=self.hparams.persistent_workers,
            pin_memory=self.hparams.pin_memory,
        )