65 lines
2.6 KiB
Python
65 lines
2.6 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 Trainer
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from pytorch_lightning.callbacks import BasePredictionWriter
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers import BoringModel
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def test_prediction_writer(tmpdir):
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class CustomPredictionWriter(BasePredictionWriter):
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def __init__(self, writer_interval: str):
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super().__init__(writer_interval)
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self.write_on_batch_end_called = False
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self.write_on_epoch_end_called = False
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def write_on_batch_end(self, *args, **kwargs):
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self.write_on_batch_end_called = True
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def write_on_epoch_end(self, *args, **kwargs):
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self.write_on_epoch_end_called = True
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with pytest.raises(MisconfigurationException, match=r"`write_interval` should be one of \['batch"):
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CustomPredictionWriter("something")
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model = BoringModel()
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cb = CustomPredictionWriter("batch_and_epoch")
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trainer = Trainer(limit_predict_batches=4, callbacks=cb)
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results = trainer.predict(model, dataloaders=model.train_dataloader())
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assert len(results) == 4
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assert cb.write_on_batch_end_called
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assert cb.write_on_epoch_end_called
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cb = CustomPredictionWriter("batch_and_epoch")
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trainer = Trainer(limit_predict_batches=4, callbacks=cb)
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trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
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assert cb.write_on_batch_end_called
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assert cb.write_on_epoch_end_called
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cb = CustomPredictionWriter("batch")
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trainer = Trainer(limit_predict_batches=4, callbacks=cb)
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trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
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assert cb.write_on_batch_end_called
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assert not cb.write_on_epoch_end_called
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cb = CustomPredictionWriter("epoch")
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trainer = Trainer(limit_predict_batches=4, callbacks=cb)
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trainer.predict(model, dataloaders=model.train_dataloader(), return_predictions=False)
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assert not cb.write_on_batch_end_called
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assert cb.write_on_epoch_end_called
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