67 lines
2.0 KiB
Python
67 lines
2.0 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 inspect
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from functools import partial
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from pytorch_lightning import seed_everything, Trainer
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from pytorch_lightning.callbacks import Callback, LambdaCallback
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from tests.helpers.boring_model import BoringModel
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def test_lambda_call(tmpdir):
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seed_everything(42)
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class CustomModel(BoringModel):
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def on_train_epoch_start(self):
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if self.current_epoch > 1:
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raise KeyboardInterrupt
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checker = set()
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def call(hook, *_, **__):
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checker.add(hook)
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hooks = {m for m, _ in inspect.getmembers(Callback, predicate=inspect.isfunction)}
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hooks_args = {h: partial(call, h) for h in hooks}
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hooks_args["on_save_checkpoint"] = lambda *_: [checker.add("on_save_checkpoint")]
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model = CustomModel()
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# successful run
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=1,
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limit_train_batches=1,
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limit_val_batches=1,
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callbacks=[LambdaCallback(**hooks_args)],
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)
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trainer.fit(model)
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# raises KeyboardInterrupt and loads from checkpoint
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=3,
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limit_train_batches=1,
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limit_val_batches=1,
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limit_test_batches=1,
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limit_predict_batches=1,
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resume_from_checkpoint=trainer.checkpoint_callback.best_model_path,
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callbacks=[LambdaCallback(**hooks_args)],
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)
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trainer.fit(model)
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trainer.test(model)
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trainer.predict(model)
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assert checker == hooks
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