39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from torch.utils.data import random_split, DataLoader
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from pytorch_lightning.core.datamodule import LightningDataModule
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from tests.base.datasets import TrialMNIST
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class TrialMNISTDataModule(LightningDataModule):
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def __init__(self, data_dir: str = './'):
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super().__init__()
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self.data_dir = data_dir
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self.non_picklable = None
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def prepare_data(self):
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TrialMNIST(self.data_dir, train=True, download=True)
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TrialMNIST(self.data_dir, train=False, download=True)
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def setup(self, stage: str = None):
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if stage == 'fit' or stage is None:
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mnist_full = TrialMNIST(root=self.data_dir, train=True, num_samples=64, download=True)
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self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
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self.dims = self.mnist_train[0][0].shape
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if stage == 'test' or stage is None:
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self.mnist_test = TrialMNIST(root=self.data_dir, train=False, num_samples=32, download=True)
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self.dims = getattr(self, 'dims', self.mnist_test[0][0].shape)
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self.non_picklable = lambda x: x**2
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def train_dataloader(self):
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return DataLoader(self.mnist_train, batch_size=32)
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def val_dataloader(self):
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return DataLoader(self.mnist_val, batch_size=32)
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def test_dataloader(self):
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return DataLoader(self.mnist_test, batch_size=32)
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