make bug_report_model minimal (#7191)
* simple and boring script * simplify dataloader * replace bug report model
This commit is contained in:
Adrian Wälchli
GitHub
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commit
591b9cee70
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@ -1,38 +1,10 @@
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# 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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# --------------------------------------------
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# --------------------------------------------
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# --------------------------------------------
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# USE THIS MODEL TO REPRODUCE A BUG YOU REPORT
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# --------------------------------------------
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# --------------------------------------------
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# --------------------------------------------
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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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from pl_examples import cli_lightning_logo
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from torch.utils.data import Dataset, DataLoader
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from pytorch_lightning import LightningModule, Trainer
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class RandomDataset(Dataset):
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"""
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>>> RandomDataset(size=10, length=20) # doctest: +ELLIPSIS
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<...bug_report_model.RandomDataset object at ...>
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"""
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def __init__(self, size, length):
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self.len = length
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@ -46,112 +18,48 @@ class RandomDataset(Dataset):
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class BoringModel(LightningModule):
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"""
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>>> BoringModel() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
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BoringModel(
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(layer): Linear(...)
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)
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"""
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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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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 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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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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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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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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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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return torch.optim.SGD(self.layer.parameters(), lr=0.1)
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# NOTE: If you are using a cmd line to run your script,
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# provide the cmd line as below.
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# opt = "--max_epochs 1 --limit_train_batches 1".split(" ")
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# parser = ArgumentParser()
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# args = parser.parse_args(opt)
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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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class TestModel(BoringModel):
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def on_train_epoch_start(self) -> None:
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print('override any method to prove your bug')
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def test_run():
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# fake data
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train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
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val_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
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test_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
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# model
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model = TestModel()
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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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num_sanity_val_steps=0,
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max_epochs=1,
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weights_summary=None,
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)
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trainer.fit(model, train_data, val_data)
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trainer.test(test_dataloaders=test_data)
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trainer.fit(model, train_dataloader=train_data, val_dataloaders=val_data)
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trainer.test(model, test_dataloaders=test_data)
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if __name__ == '__main__':
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cli_lightning_logo()
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test_run()
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run()
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