lightning/tests/test_deprecated.py

"""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}