93 lines
3.1 KiB
Python
93 lines
3.1 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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from pytorch_lightning import Callback, Trainer
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from tests.helpers import BoringModel
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from tests.helpers.runif import RunIf
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class BatchHookObserverCallback(Callback):
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def on_train_batch_start(self, trainer, pl_module, batch, *args):
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assert batch.device == pl_module.device
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, *args):
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assert batch.device == pl_module.device
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def on_validation_batch_start(self, trainer, pl_module, batch, *args):
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assert batch.device == pl_module.device
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, *args):
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assert batch.device == pl_module.device
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def on_test_batch_start(self, trainer, pl_module, batch, *args):
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assert batch.device == pl_module.device
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def on_test_batch_end(self, trainer, pl_module, outputs, batch, *args):
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assert batch.device == pl_module.device
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def on_predict_batch_start(self, trainer, pl_module, batch, *args):
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assert batch.device == pl_module.device
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def on_predict_batch_end(self, trainer, pl_module, outputs, batch, *args):
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assert batch.device == pl_module.device
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class BatchHookObserverModel(BoringModel):
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def on_train_batch_start(self, batch, *args):
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assert batch.device == self.device
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def on_train_batch_end(self, outputs, batch, *args):
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assert batch.device == self.device
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def on_validation_batch_start(self, batch, *args):
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assert batch.device == self.device
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def on_validation_batch_end(self, outputs, batch, *args):
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assert batch.device == self.device
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def on_test_batch_start(self, batch, *args):
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assert batch.device == self.device
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def on_test_batch_end(self, outputs, batch, *args):
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assert batch.device == self.device
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def on_predict_batch_start(self, batch, *args):
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assert batch.device == self.device
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def on_predict_batch_end(self, outputs, batch, *args):
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assert batch.device == self.device
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@RunIf(min_gpus=1)
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def test_callback_batch_on_device(tmpdir):
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"""Test that the batch object sent to the on_*_batch_start/end hooks is on the right device."""
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batch_callback = BatchHookObserverCallback()
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model = BatchHookObserverModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_steps=1,
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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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accelerator="gpu",
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devices=1,
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callbacks=[batch_callback],
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)
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trainer.fit(model)
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trainer.validate(model)
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trainer.test(model)
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trainer.predict(model)
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