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