89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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from torch.utils.data import Dataset
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import pytorch_lightning as pl
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PATH_LEGACY = os.path.dirname(__file__)
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class RandomDataset(Dataset):
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def __init__(self, size, length: int = 100):
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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 DummyModel(pl.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 _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} # used for PL<1.0
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return loss # used for PL >= 1.0
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def training_step(self, batch, batch_idx):
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return self._step(batch, batch_idx)
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def validation_step(self, batch, batch_idx):
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self._step(batch, batch_idx)
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def test_step(self, batch, batch_idx):
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self._step(batch, batch_idx)
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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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def main_train(dir_path, max_epochs: int = 5):
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trainer = pl.Trainer(default_root_dir=dir_path, checkpoint_callback=True, max_epochs=max_epochs)
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model = DummyModel()
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trainer.fit(model)
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if __name__ == "__main__":
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path_dir = os.path.join(PATH_LEGACY, "checkpoints", str(pl.__version__))
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main_train(path_dir)
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