66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
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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: int, length: int):
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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 loss(self, batch, prediction):
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# An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
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return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))
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def training_step(self, batch, batch_idx):
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output = self(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def validation_step(self, batch, batch_idx):
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output = self(batch)
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loss = self.loss(batch, output)
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return {"x": loss}
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def test_step(self, batch, batch_idx):
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output = self(batch)
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loss = self.loss(batch, output)
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return {"y": loss}
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
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return [optimizer], [lr_scheduler]
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64))
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val_dataloader = train_dataloader
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test_dataloader = train_dataloader
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predict_dataloader = train_dataloader
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if __name__ == "__main__":
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model = BoringModel()
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trainer = Trainer(max_epochs=1, accelerator="cpu", devices=2, strategy="ddp")
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trainer.fit(model)
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trainer.validate(model)
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trainer.test(model)
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trainer.predict(model)
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