82 lines
2.7 KiB
Python
82 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 pytest
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from torch.utils.data import DataLoader
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from pytorch_lightning.trainer.trainer import Trainer
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from tests.helpers import BoringModel, RandomDataset
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@pytest.mark.parametrize(
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"max_epochs,expected_val_loop_calls,expected_val_batches",
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[(1, 0, [0]), (4, 2, [0, 2, 0, 2]), (5, 2, [0, 2, 0, 2, 0])],
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)
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def test_check_val_every_n_epoch(tmpdir, max_epochs, expected_val_loop_calls, expected_val_batches):
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class TestModel(BoringModel):
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val_epoch_calls = 0
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val_batches = []
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def on_train_epoch_end(self, *args, **kwargs):
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self.val_batches.append(self.trainer.progress_bar_callback.total_val_batches)
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def on_validation_epoch_start(self) -> None:
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self.val_epoch_calls += 1
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=max_epochs,
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num_sanity_val_steps=0,
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limit_val_batches=2,
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check_val_every_n_epoch=2,
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logger=False,
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)
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trainer.fit(model)
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assert trainer.state.finished, f"Training failed with {trainer.state}"
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assert model.val_epoch_calls == expected_val_loop_calls
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assert model.val_batches == expected_val_batches
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def test_check_val_every_n_epoch_with_max_steps(tmpdir):
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data_samples_train = 2
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check_val_every_n_epoch = 3
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max_epochs = 4
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.validation_called_at_step = set()
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def validation_step(self, *args):
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self.validation_called_at_step.add(self.global_step)
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return super().validation_step(*args)
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, data_samples_train))
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_steps=data_samples_train * max_epochs,
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check_val_every_n_epoch=check_val_every_n_epoch,
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num_sanity_val_steps=0,
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)
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trainer.fit(model)
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assert trainer.current_epoch == max_epochs
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assert trainer.global_step == max_epochs * data_samples_train
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assert list(model.validation_called_at_step) == [data_samples_train * check_val_every_n_epoch]
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