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import pytorch_lightning as pl
import torchvision
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
class MNISTDataDictWrapper(Dataset):
def __init__(self, dset):
super().__init__()
self.dset = dset
def __getitem__(self, i):
x, y = self.dset[i]
return {"jpg": x, "cls": y}
def __len__(self):
return len(self.dset)
class MNISTLoader(pl.LightningDataModule):
def __init__(self, batch_size, num_workers=0, prefetch_factor=2, shuffle=True):
super().__init__()
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
)
self.batch_size = batch_size
self.num_workers = num_workers
self.prefetch_factor = prefetch_factor if num_workers > 0 else 0
self.shuffle = shuffle
self.train_dataset = MNISTDataDictWrapper(
torchvision.datasets.MNIST(
root=".data/", train=True, download=True, transform=transform
)
)
self.test_dataset = MNISTDataDictWrapper(
torchvision.datasets.MNIST(
root=".data/", train=False, download=True, transform=transform
)
)
def prepare_data(self):
pass
def train_dataloader(self):
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
prefetch_factor=self.prefetch_factor,
)
def test_dataloader(self):
return DataLoader(
self.test_dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
prefetch_factor=self.prefetch_factor,
)
def val_dataloader(self):
return DataLoader(
self.test_dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
prefetch_factor=self.prefetch_factor,
)
if __name__ == "__main__":
dset = MNISTDataDictWrapper(
torchvision.datasets.MNIST(
root=".data/",
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
),
)
)
ex = dset[0]
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