60 lines
1.8 KiB
Python
60 lines
1.8 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 pytest
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import torch
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from pytorch_lightning import Trainer
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from tests.helpers.boring_model import BoringModel, RandomDataset
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def test_overfit_multiple_val_loaders(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def validation_step(self, batch, batch_idx, dataloader_idx):
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output = self.layer(batch[0])
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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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pass
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def val_dataloader(self):
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dl1 = torch.utils.data.DataLoader(RandomDataset(32, 64))
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dl2 = torch.utils.data.DataLoader(RandomDataset(32, 64))
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return [dl1, dl2]
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=2, overfit_batches=1, log_every_n_steps=1, weights_summary=None
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)
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trainer.fit(model)
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@pytest.mark.parametrize("overfit", [1, 2, 0.1, 0.25, 1.0])
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def test_overfit_basic(tmpdir, overfit):
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"""
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Tests that only training_step can be used
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"""
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit, weights_summary=None)
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trainer.fit(model)
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