"""Test deprecated functionality which will be removed in vX.Y.Z""" from pytorch_lightning import Trainer import tests.base.utils as tutils from tests.base import TestModelBase, LightTrainDataloader, LightEmptyTestStep def test_tbd_remove_in_v0_8_0_module_imports(): from pytorch_lightning.logging.comet_logger import CometLogger # noqa: F811 from pytorch_lightning.logging.mlflow_logger import MLFlowLogger # noqa: F811 from pytorch_lightning.logging.test_tube_logger import TestTubeLogger # noqa: F811 from pytorch_lightning.pt_overrides.override_data_parallel import ( # noqa: F811 LightningDataParallel, LightningDistributedDataParallel) from pytorch_lightning.overrides.override_data_parallel import ( # noqa: F811 LightningDataParallel, LightningDistributedDataParallel) from pytorch_lightning.core.model_saving import ModelIO # noqa: F811 from pytorch_lightning.core.root_module import LightningModule # noqa: F811 from pytorch_lightning.root_module.decorators import data_loader # noqa: F811 from pytorch_lightning.root_module.grads import GradInformation # noqa: F811 from pytorch_lightning.root_module.hooks import ModelHooks # noqa: F811 from pytorch_lightning.root_module.memory import ModelSummary # noqa: F811 from pytorch_lightning.root_module.model_saving import ModelIO # noqa: F811 from pytorch_lightning.root_module.root_module import LightningModule # noqa: F811 def test_tbd_remove_in_v0_8_0_trainer(): mapping_old_new = { 'gradient_clip': 'gradient_clip_val', 'nb_gpu_nodes': 'num_nodes', 'max_nb_epochs': 'max_epochs', 'min_nb_epochs': 'min_epochs', 'nb_sanity_val_steps': 'num_sanity_val_steps', } # skip 0 since it may be interested as False kwargs = {k: (i + 1) for i, k in enumerate(mapping_old_new)} trainer = Trainer(**kwargs) for attr_old in mapping_old_new: attr_new = mapping_old_new[attr_old] assert kwargs[attr_old] == getattr(trainer, attr_old), \ 'Missing deprecated attribute "%s"' % attr_old assert kwargs[attr_old] == getattr(trainer, attr_new), \ 'Wrongly passed deprecated argument "%s" to attribute "%s"' % (attr_old, attr_new) def test_tbd_remove_in_v0_9_0_module_imports(): from pytorch_lightning.core.decorators import data_loader # noqa: F811 from pytorch_lightning.logging.comet import CometLogger # noqa: F402 from pytorch_lightning.logging.mlflow import MLFlowLogger # noqa: F402 from pytorch_lightning.logging.neptune import NeptuneLogger # noqa: F402 from pytorch_lightning.logging.test_tube import TestTubeLogger # noqa: F402 from pytorch_lightning.logging.wandb import WandbLogger # noqa: F402 class ModelVer0_6(LightTrainDataloader, LightEmptyTestStep, TestModelBase): # todo: this shall not be needed while evaluate asks for dataloader explicitly def val_dataloader(self): return self._dataloader(train=False) def validation_end(self, outputs): return {'val_loss': 0.6} def test_end(self, outputs): return {'test_loss': 0.6} class ModelVer0_7(LightTrainDataloader, LightEmptyTestStep, TestModelBase): # todo: this shall not be needed while evaluate asks for dataloader explicitly def val_dataloader(self): return self._dataloader(train=False) def validation_end(self, outputs): return {'val_loss': 0.7} def test_end(self, outputs): return {'test_loss': 0.7} def test_tbd_remove_in_v1_0_0_model_hooks(): hparams = tutils.get_default_hparams() model = ModelVer0_6(hparams) trainer = Trainer(logger=False) trainer.test(model) assert trainer.callback_metrics == {'test_loss': 0.6} trainer = Trainer(logger=False) # TODO: why `dataloder` is required if it is not used result = trainer.evaluate(model, dataloaders=[[None]], max_batches=1) assert result == {'val_loss': 0.6} model = ModelVer0_7(hparams) trainer = Trainer(logger=False) trainer.test(model) assert trainer.callback_metrics == {'test_loss': 0.7} trainer = Trainer(logger=False) # TODO: why `dataloder` is required if it is not used result = trainer.evaluate(model, dataloaders=[[None]], max_batches=1) assert result == {'val_loss': 0.7}