98 lines
2.8 KiB
Python
98 lines
2.8 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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from tests.base import SimpleModule
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from pytorch_lightning.trainer import Trainer
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@pytest.mark.parametrize('max_epochs', [1, 2, 3])
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def test_val_check_interval_1(tmpdir, max_epochs):
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class TestModel(SimpleModule):
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def __init__(self):
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super().__init__()
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self.train_epoch_calls = 0
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self.val_epoch_calls = 0
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def on_train_epoch_start(self) -> None:
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self.train_epoch_calls += 1
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def on_validation_epoch_start(self) -> None:
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if not self.trainer.running_sanity_check:
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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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max_epochs=max_epochs,
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val_check_interval=1.0,
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logger=False,
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)
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trainer.fit(model)
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assert model.val_epoch_calls == max_epochs
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@pytest.mark.parametrize('max_epochs', [1, 2, 3])
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def test_val_check_interval_quarter(tmpdir, max_epochs):
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class TestModel(SimpleModule):
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def __init__(self):
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super().__init__()
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self.train_epoch_calls = 0
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self.val_epoch_calls = 0
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def on_train_epoch_start(self) -> None:
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self.train_epoch_calls += 1
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def on_validation_epoch_start(self) -> None:
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if not self.trainer.running_sanity_check:
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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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max_epochs=max_epochs,
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val_check_interval=0.25,
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logger=False,
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)
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trainer.fit(model)
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assert model.val_epoch_calls == max_epochs * 4
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@pytest.mark.parametrize('max_epochs', [1, 2, 3])
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def test_val_check_interval_third(tmpdir, max_epochs):
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class TestModel(SimpleModule):
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def __init__(self):
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super().__init__()
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self.train_epoch_calls = 0
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self.val_epoch_calls = 0
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def on_train_epoch_start(self) -> None:
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self.train_epoch_calls += 1
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def on_validation_epoch_start(self) -> None:
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if not self.trainer.running_sanity_check:
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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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max_epochs=max_epochs,
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val_check_interval=0.33,
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logger=False,
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)
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trainer.fit(model)
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assert model.val_epoch_calls == max_epochs * 3
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