| import pytorch_lightning as pl |
| import torchvision |
| from torch.utils.data import DataLoader, Dataset |
| from torchvision import transforms |
|
|
|
|
| class CIFAR10DataDictWrapper(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 CIFAR10Loader(pl.LightningDataModule): |
| def __init__(self, batch_size, num_workers=0, 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.shuffle = shuffle |
| self.train_dataset = CIFAR10DataDictWrapper( |
| torchvision.datasets.CIFAR10( |
| root=".data/", train=True, download=True, transform=transform |
| ) |
| ) |
| self.test_dataset = CIFAR10DataDictWrapper( |
| torchvision.datasets.CIFAR10( |
| 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, |
| ) |
|
|
| def test_dataloader(self): |
| return DataLoader( |
| self.test_dataset, |
| batch_size=self.batch_size, |
| shuffle=self.shuffle, |
| num_workers=self.num_workers, |
| ) |
|
|
| def val_dataloader(self): |
| return DataLoader( |
| self.test_dataset, |
| batch_size=self.batch_size, |
| shuffle=self.shuffle, |
| num_workers=self.num_workers, |
| ) |
|
|
