67 lines
1.7 KiB
Python
67 lines
1.7 KiB
Python
import os
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import torch
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from torch.utils.data import DataLoader, Dataset
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from pytorch_lightning import LightningModule, Trainer
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class RandomDataset(Dataset):
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def __init__(self, size, length):
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self.len = length
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self.data = torch.randn(length, size)
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def __getitem__(self, index):
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return self.data[index]
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def __len__(self):
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return self.len
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class BoringModel(LightningModule):
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def __init__(self):
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super().__init__()
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self.layer = torch.nn.Linear(32, 2)
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def forward(self, x):
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return self.layer(x)
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def training_step(self, batch, batch_idx):
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loss = self(batch).sum()
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self.log("train_loss", loss)
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return {"loss": loss}
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def validation_step(self, batch, batch_idx):
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loss = self(batch).sum()
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self.log("valid_loss", loss)
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def test_step(self, batch, batch_idx):
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loss = self(batch).sum()
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self.log("test_loss", loss)
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def configure_optimizers(self):
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return torch.optim.SGD(self.layer.parameters(), lr=0.1)
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def run():
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train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
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val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
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test_data = DataLoader(RandomDataset(32, 64), batch_size=2)
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=os.getcwd(),
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limit_train_batches=1,
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limit_val_batches=1,
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limit_test_batches=1,
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num_sanity_val_steps=0,
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max_epochs=1,
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enable_model_summary=False,
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)
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trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
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trainer.test(model, dataloaders=test_data)
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if __name__ == "__main__":
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run()
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