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| 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, | |
| ) | |