107 lines
2.9 KiB
Python
107 lines
2.9 KiB
Python
import torch
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from pytorch_lightning import LightningModule
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from torch.utils.data import Dataset
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class RandomDictDataset(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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a = self.data[index]
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b = a + 2
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return {'a': a, 'b': b}
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def __len__(self):
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return self.len
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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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"""
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Testing PL Module
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Use as follows:
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- subclass
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- modify the behavior for what you want
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class TestModel(BaseTestModel):
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def training_step(...):
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# do your own thing
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or:
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model = BaseTestModel()
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model.training_epoch_end = None
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"""
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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 step(self, x):
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x = self.layer(x)
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out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
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return out
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def training_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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return {"loss": loss}
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def training_step_end(self, training_step_outputs):
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return training_step_outputs
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def training_epoch_end(self, outputs) -> None:
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torch.stack([x["loss"] for x in outputs]).mean()
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def validation_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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return {"x": loss}
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def validation_epoch_end(self, outputs) -> None:
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torch.stack([x['x'] for x in outputs]).mean()
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def test_step(self, batch, batch_idx):
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output = self.layer(batch)
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loss = self.loss(batch, output)
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return {"y": loss}
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def test_epoch_end(self, outputs) -> None:
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torch.stack([x["y"] for x in outputs]).mean()
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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 torch.utils.data.DataLoader(RandomDataset(32, 64))
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def val_dataloader(self):
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return torch.utils.data.DataLoader(RandomDataset(32, 64))
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def test_dataloader(self):
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return torch.utils.data.DataLoader(RandomDataset(32, 64))
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