lightning/tests/base/datamodules.py

import os
from typing import Any, Dict, Optional

from pytorch_lightning.core.datamodule import LightningDataModule
from tests.base.datasets import MNIST, TrialMNIST
from torch.utils.data import DataLoader, random_split
from torch.utils.data.distributed import DistributedSampler


class TrialMNISTDataModule(LightningDataModule):
    def __init__(self, data_dir: str = "./"):
        super().__init__()
        self.data_dir = data_dir
        self.non_picklable = None
        self.checkpoint_state: Optional[str] = None

    def prepare_data(self):
        TrialMNIST(self.data_dir, train=True, download=True)
        TrialMNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):

        if stage == "fit" or stage is None:
            mnist_full = TrialMNIST(
                root=self.data_dir, train=True, num_samples=64, download=True
            )
            self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
            self.dims = self.mnist_train[0][0].shape

        if stage == "test" or stage is None:
            self.mnist_test = TrialMNIST(
                root=self.data_dir, train=False, num_samples=64, download=True
            )
            self.dims = getattr(self, "dims", self.mnist_test[0][0].shape)

        self.non_picklable = lambda x: x ** 2

    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)

    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        checkpoint[self.__class__.__name__] = self.__class__.__name__

    def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        self.checkpoint_state = checkpoint.get(self.__class__.__name__)


class MNISTDataModule(LightningDataModule):
    def __init__(
        self, data_dir: str = "./", batch_size: int = 32, dist_sampler: bool = False
    ) -> None:
        super().__init__()

        self.dist_sampler = dist_sampler
        self.data_dir = data_dir
        self.batch_size = batch_size

        # 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 only
        MNIST(self.data_dir, train=True, download=True, normalize=(0.1307, 0.3081))
        MNIST(self.data_dir, train=False, download=True, normalize=(0.1307, 0.3081))

    def setup(self, stage: Optional[str] = None):

        # Assign train/val datasets for use in dataloaders
        # TODO: need to split using random_split once updated to torch >= 1.6
        if stage == "fit" or stage is None:
            self.mnist_train = MNIST(
                self.data_dir, train=True, normalize=(0.1307, 0.3081)
            )

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(
                self.data_dir, train=False, normalize=(0.1307, 0.3081)
            )

    def train_dataloader(self):
        dist_sampler = None
        if self.dist_sampler:
            dist_sampler = DistributedSampler(self.mnist_train, shuffle=False)

        return DataLoader(
            self.mnist_train,
            batch_size=self.batch_size,
            sampler=dist_sampler,
            shuffle=False,
        )

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=self.batch_size, shuffle=False)