lightning/tests/test_deprecated.py

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"""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',
'default_save_path': 'default_root_dir',
}
# 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_trainer():
# test show_progress_bar set by progress_bar_refresh_rate
trainer = Trainer(progress_bar_refresh_rate=0, show_progress_bar=True)
assert not getattr(trainer, 'show_progress_bar')
trainer = Trainer(progress_bar_refresh_rate=50, show_progress_bar=False)
assert getattr(trainer, 'show_progress_bar')
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
from pytorch_lightning.profiler import SimpleProfiler, AdvancedProfiler # 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_step(self, batch, batch_idx, *args, **kwargs):
return {'val_loss': 0.6}
def validation_end(self, outputs):
return {'val_loss': 0.6}
def test_dataloader(self):
return self._dataloader(train=False)
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_step(self, batch, batch_idx, *args, **kwargs):
return {'val_loss': 0.7}
def validation_end(self, outputs):
return {'val_loss': 0.7}
def test_dataloader(self):
return self._dataloader(train=False)
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}