mnist_datamodule.py

from typing import Optional, Tuple
from sklearn.model_selection import KFold, StratifiedKFold

import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split, Subset
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,
        stratify: bool = False,
        n_splits: int = 5,
    ):
        super().__init__()

        # this line allows to access init params with 'self.hparams' attribute
        # it 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.dims is returned when you call datamodule.size()
        self.dims = (1, 28, 28)

        self.data_train: Optional[Dataset] = None
        self.data_val: Optional[Dataset] = None
        self.data_test: Optional[Dataset] = None

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

    def prepare_data(self):
        """Download data if needed. This method is called only from a single GPU.
        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):
        """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
        This method is called by lightning twice for `trainer.fit()` and `trainer.test()`, so be careful if you do a random split!
        The `stage` can be used to differentiate whether it's called before trainer.fit()` or `trainer.test()`."""

        # load datasets only if they're 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):
        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):
        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):
        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 get_splits(self):
        self.prepare_data()
        self.setup()

        if self.hparams.stratify:
            labels = self.get_data_labels()
            cv_ = StratifiedKFold(n_splits=self.hparams.n_splits)
        else:
            labels = None
            cv_ = KFold(n_splits=self.hparams.n_splits)

        dataset = self.get_dataset()
        n_samples = len(dataset)
        for train_idx, val_idx in cv_.split(X=range(n_samples), y=labels):
            _train = Subset(dataset, train_idx)
            train_loader = DataLoader(dataset=_train,
                                      batch_size=self.hparams.batch_size,
                                      shuffle=True,
                                      num_workers=self.hparams.num_workers,
                                      pin_memory=self.hparams.pin_memory)

            _val = Subset(dataset, val_idx)
            val_loader = DataLoader(dataset=_val,
                                    batch_size=self.hparams.batch_size,
                                    shuffle=False,
                                    num_workers=self.hparams.num_workers,
                                    pin_memory=self.hparams.pin_memory)

            yield train_loader, val_loader

    def get_dataset(self):
        """Creates and returns the complete dataset."""
        return ConcatDataset([self.data_train, self.data_val])

    def get_data_labels(self):
        dataset = self.get_dataset()
        return [int(sample[1]) for sample in dataset]