74 lines
2.4 KiB
Python
74 lines
2.4 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 pytorch_lightning import Callback, Trainer
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from tests.helpers.boring_model import BoringModel
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@pytest.mark.parametrize("single_cb", [False, True])
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def test_train_step_no_return(tmpdir, single_cb):
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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 CB(Callback):
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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d = outputs[0][0]
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assert 'minimize' in d
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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assert 'x' in outputs
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def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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assert 'x' in outputs
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def on_train_epoch_end(self, trainer, pl_module, outputs):
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d = outputs[0]
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assert len(d) == trainer.num_training_batches
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class TestModel(BoringModel):
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def on_train_batch_end(self, outputs, batch, batch_idx: int, dataloader_idx: int) -> None:
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d = outputs[0][0]
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assert 'minimize' in d
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def on_validation_batch_end(self, outputs, batch, batch_idx: int, dataloader_idx: int) -> None:
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assert 'x' in outputs
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def on_test_batch_end(self, outputs, batch, batch_idx: int, dataloader_idx: int) -> None:
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assert 'x' in outputs
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def on_train_epoch_end(self, outputs) -> None:
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d = outputs[0]
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assert len(d) == self.trainer.num_training_batches
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model = TestModel()
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trainer = Trainer(
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callbacks=CB() if single_cb else [CB()],
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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assert any(isinstance(c, CB) for c in trainer.callbacks)
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results = trainer.fit(model)
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assert results
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