95 lines
3.0 KiB
Python
Executable File
95 lines
3.0 KiB
Python
Executable File
# 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 tests.base.develop_utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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# TODO: add matching messages
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def test_wrong_train_setting(tmpdir):
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"""
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* Test that an error is thrown when no `train_dataloader()` is defined
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* Test that an error is thrown when no `training_step()` is defined
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"""
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tutils.reset_seed()
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hparams = EvalModelTemplate.get_default_hparams()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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with pytest.raises(MisconfigurationException):
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model = EvalModelTemplate(**hparams)
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model.train_dataloader = None
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trainer.fit(model)
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with pytest.raises(MisconfigurationException):
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model = EvalModelTemplate(**hparams)
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model.training_step = None
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trainer.fit(model)
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def test_wrong_configure_optimizers(tmpdir):
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""" Test that an error is thrown when no `configure_optimizers()` is defined """
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tutils.reset_seed()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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with pytest.raises(MisconfigurationException):
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model = EvalModelTemplate()
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model.configure_optimizers = None
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trainer.fit(model)
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def test_val_loop_config(tmpdir):
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""""
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When either val loop or val data are missing raise warning
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"""
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tutils.reset_seed()
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hparams = EvalModelTemplate.get_default_hparams()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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# no val data has val loop
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with pytest.warns(UserWarning):
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model = EvalModelTemplate(**hparams)
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model.validation_step = None
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trainer.fit(model)
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# has val loop but no val data
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with pytest.warns(UserWarning):
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model = EvalModelTemplate(**hparams)
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model.val_dataloader = None
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trainer.fit(model)
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def test_test_loop_config(tmpdir):
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""""
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When either test loop or test data are missing
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"""
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hparams = EvalModelTemplate.get_default_hparams()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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# has test loop but no test data
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with pytest.warns(UserWarning):
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model = EvalModelTemplate(**hparams)
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model.test_dataloader = None
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trainer.test(model)
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# has test data but no test loop
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with pytest.warns(UserWarning):
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model = EvalModelTemplate(**hparams)
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model.test_step = None
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trainer.test(model, test_dataloaders=model.dataloader(train=False))
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