193 lines
7.9 KiB
Python
193 lines
7.9 KiB
Python
"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import sys
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import pytest
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from pytorch_lightning import Trainer
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import tests.base.utils as tutils
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from tests.base import EvalModelTemplate
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def _soft_unimport_module(str_module):
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# once the module is imported e.g with parsing with pytest it lives in memory
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if str_module in sys.modules:
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del sys.modules[str_module]
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def test_tbd_remove_in_v0_8_0_module_imports():
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_soft_unimport_module("pytorch_lightning.logging.comet_logger")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.logging.comet_logger import CometLogger # noqa: F811
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_soft_unimport_module("pytorch_lightning.logging.mlflow_logger")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.logging.mlflow_logger import MLFlowLogger # noqa: F811
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_soft_unimport_module("pytorch_lightning.logging.test_tube_logger")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.logging.test_tube_logger import TestTubeLogger # noqa: F811
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_soft_unimport_module("pytorch_lightning.pt_overrides.override_data_parallel")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.pt_overrides.override_data_parallel import ( # noqa: F811
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LightningDataParallel, LightningDistributedDataParallel)
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_soft_unimport_module("pytorch_lightning.overrides.override_data_parallel")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.overrides.override_data_parallel import ( # noqa: F811
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LightningDataParallel, LightningDistributedDataParallel)
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_soft_unimport_module("pytorch_lightning.core.model_saving")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.core.model_saving import ModelIO # noqa: F811
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_soft_unimport_module("pytorch_lightning.core.root_module")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.core.root_module import LightningModule # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.decorators")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.decorators import data_loader # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.grads")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.grads import GradInformation # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.hooks")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.hooks import ModelHooks # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.memory")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.memory import ModelSummary # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.model_saving")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.model_saving import ModelIO # noqa: F811
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_soft_unimport_module("pytorch_lightning.root_module.root_module")
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with pytest.deprecated_call(match='v0.8.0'):
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from pytorch_lightning.root_module.root_module import LightningModule # noqa: F811
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def test_tbd_remove_in_v0_8_0_trainer():
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mapping_old_new = {
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'gradient_clip': 'gradient_clip_val',
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'nb_gpu_nodes': 'num_nodes',
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'max_nb_epochs': 'max_epochs',
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'min_nb_epochs': 'min_epochs',
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'nb_sanity_val_steps': 'num_sanity_val_steps',
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'default_save_path': 'default_root_dir',
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}
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# skip 0 since it may be interested as False
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kwargs = {k: (i + 1) for i, k in enumerate(mapping_old_new)}
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trainer = Trainer(**kwargs)
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for attr_old in mapping_old_new:
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attr_new = mapping_old_new[attr_old]
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with pytest.deprecated_call(match='v0.8.0'):
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_ = getattr(trainer, attr_old)
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assert kwargs[attr_old] == getattr(trainer, attr_old), \
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'Missing deprecated attribute "%s"' % attr_old
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assert kwargs[attr_old] == getattr(trainer, attr_new), \
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'Wrongly passed deprecated argument "%s" to attribute "%s"' % (attr_old, attr_new)
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def test_tbd_remove_in_v0_9_0_trainer():
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# test show_progress_bar set by progress_bar_refresh_rate
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with pytest.deprecated_call(match='v0.9.0'):
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trainer = Trainer(progress_bar_refresh_rate=0, show_progress_bar=True)
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assert not getattr(trainer, 'show_progress_bar')
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with pytest.deprecated_call(match='v0.9.0'):
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trainer = Trainer(progress_bar_refresh_rate=50, show_progress_bar=False)
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assert getattr(trainer, 'show_progress_bar')
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with pytest.deprecated_call(match='v0.9.0'):
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trainer = Trainer(num_tpu_cores=8)
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assert trainer.tpu_cores == 8
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def test_tbd_remove_in_v0_9_0_module_imports():
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_soft_unimport_module("pytorch_lightning.core.decorators")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.core.decorators import data_loader # noqa: F811
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data_loader(print)
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_soft_unimport_module("pytorch_lightning.logging.comet")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.logging.comet import CometLogger # noqa: F402
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_soft_unimport_module("pytorch_lightning.logging.mlflow")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.logging.mlflow import MLFlowLogger # noqa: F402
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_soft_unimport_module("pytorch_lightning.logging.neptune")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.logging.neptune import NeptuneLogger # noqa: F402
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_soft_unimport_module("pytorch_lightning.logging.test_tube")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.logging.test_tube import TestTubeLogger # noqa: F402
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_soft_unimport_module("pytorch_lightning.logging.wandb")
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with pytest.deprecated_call(match='v0.9.0'):
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from pytorch_lightning.logging.wandb import WandbLogger # noqa: F402
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class ModelVer0_6(EvalModelTemplate):
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# todo: this shall not be needed while evaluate asks for dataloader explicitly
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def val_dataloader(self):
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return self.dataloader(train=False)
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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return {'val_loss': 0.6}
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def validation_end(self, outputs):
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return {'val_loss': 0.6}
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def test_dataloader(self):
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return self.dataloader(train=False)
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def test_end(self, outputs):
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return {'test_loss': 0.6}
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class ModelVer0_7(EvalModelTemplate):
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# todo: this shall not be needed while evaluate asks for dataloader explicitly
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def val_dataloader(self):
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return self.dataloader(train=False)
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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return {'val_loss': 0.7}
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def validation_end(self, outputs):
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return {'val_loss': 0.7}
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def test_dataloader(self):
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return self.dataloader(train=False)
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def test_end(self, outputs):
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return {'test_loss': 0.7}
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def test_tbd_remove_in_v1_0_0_model_hooks():
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hparams = EvalModelTemplate.get_default_hparams()
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model = ModelVer0_6(hparams)
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with pytest.deprecated_call(match='v1.0'):
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trainer = Trainer(logger=False)
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trainer.test(model)
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assert trainer.callback_metrics == {'test_loss': 0.6}
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with pytest.deprecated_call(match='v1.0'):
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trainer = Trainer(logger=False)
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# TODO: why `dataloder` is required if it is not used
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result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
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assert result == {'val_loss': 0.6}
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model = ModelVer0_7(hparams)
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with pytest.deprecated_call(match='v1.0'):
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trainer = Trainer(logger=False)
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trainer.test(model)
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assert trainer.callback_metrics == {'test_loss': 0.7}
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with pytest.deprecated_call(match='v1.0'):
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trainer = Trainer(logger=False)
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# TODO: why `dataloder` is required if it is not used
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result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
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assert result == {'val_loss': 0.7}
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