code/datamodules/image_classification.py

from .base import ImageDataModule
from torch.utils.data import random_split
from torchvision.datasets import MNIST, CIFAR10
from typing import Optional


class MNISTDataModule(ImageDataModule):
    """Datamodule for the MNIST dataset."""

    def prepare_data(self):
        # Download MNIST
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):
        # Set the training and validation data
        if stage == "fit" or stage is None:
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.train_data, self.val_data = random_split(mnist_full, [55000, 5000])

        # Set the test data
        if stage == "test" or stage is None:
            self.test_data = MNIST(self.data_dir, train=False, transform=self.transform)


class CIFAR10DataModule(ImageDataModule):
    """Datamodule for the CIFAR10 dataset."""

    def prepare_data(self):
        # Download CIFAR10
        CIFAR10(self.data_dir, train=True, download=True)
        CIFAR10(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):
        # Set the training and validation data
        if stage == "fit" or stage is None:
            cifar10_full = CIFAR10(self.data_dir, train=True, transform=self.transform)
            self.train_data, self.val_data = random_split(cifar10_full, [45000, 5000])

        # Set the test data
        if stage == "test" or stage is None:
            self.test_data = CIFAR10(
                self.data_dir, train=False, transform=self.transform
            )