99 lines
3.6 KiB
Python
99 lines
3.6 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 torch
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from pytorch_lightning import LightningModule
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from torch.utils.data import Dataset
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from typing import Optional
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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 SimpleModule(LightningModule):
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def __init__(self, epoch_min_loss_override: Optional[int] = None):
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"""LightningModule for testing purposes
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Args:
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epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
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validation loss for testing purposes (zero based). If None this is ignored. Defaults to 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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self.epoch_min_loss_override = epoch_min_loss_override
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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.forward(batch)
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loss = self.loss(batch, output)
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return {"output": output, "loss": loss, "checkpoint_on": loss}
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def validation_step(self, batch, batch_idx):
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output = self.forward(batch)
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loss = self.loss(batch, output)
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return {"output": output, "loss": loss, "checkpoint_on": loss}
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def test_step(self, batch, batch_idx):
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output = self.forward(batch)
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loss = self.loss(batch, output)
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return {"output": output, "loss": loss}
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def training_epoch_end(self, outputs) -> None:
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avg_loss = torch.stack([x["loss"] for x in outputs]).mean()
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self.log("avg_loss", avg_loss)
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def validation_epoch_end(self, outputs) -> None:
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avg_val_loss = torch.stack(
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[torch.randn(1, requires_grad=True) for _ in outputs]
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).mean()
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# For testing purposes allow a nominated epoch to have a low loss
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if self.current_epoch == self.epoch_min_loss_override:
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avg_val_loss -= 1e10
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self.log("avg_val_loss", avg_val_loss)
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self.log("checkpoint_on", avg_val_loss)
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def test_epoch_end(self, outputs) -> None:
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avg_loss = torch.stack(
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[torch.randn(1, requires_grad=True) for _ in outputs]
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).mean()
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self.log("test_loss", avg_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 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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