158 lines
5.7 KiB
Python
158 lines
5.7 KiB
Python
"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import random
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import sys
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import pytest
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import torch
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from pytorch_lightning import Trainer
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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_10_0_trainer():
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rnd_val = random.random()
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with pytest.deprecated_call(match='v0.10.0'):
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trainer = Trainer(overfit_pct=rnd_val)
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assert trainer.overfit_batches == rnd_val
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with pytest.deprecated_call(match='v0.10.0'):
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assert trainer.overfit_pct == rnd_val
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rnd_val = random.random()
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with pytest.deprecated_call(match='v0.10.0'):
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trainer = Trainer(train_percent_check=rnd_val)
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assert trainer.limit_train_batches == rnd_val
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with pytest.deprecated_call(match='v0.10.0'):
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assert trainer.train_percent_check == rnd_val
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rnd_val = random.random()
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with pytest.deprecated_call(match='v0.10.0'):
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trainer = Trainer(val_percent_check=rnd_val)
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assert trainer.limit_val_batches == rnd_val
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with pytest.deprecated_call(match='v0.10.0'):
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assert trainer.val_percent_check == rnd_val
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rnd_val = random.random()
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with pytest.deprecated_call(match='v0.10.0'):
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trainer = Trainer(test_percent_check=rnd_val)
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assert trainer.limit_test_batches == rnd_val
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with pytest.deprecated_call(match='v0.10.0'):
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assert trainer.test_percent_check == rnd_val
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trainer = Trainer()
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with pytest.deprecated_call(match='v0.10.0'):
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trainer.proc_rank = 0
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with pytest.deprecated_call(match='v0.10.0'):
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assert trainer.proc_rank == trainer.global_rank
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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': torch.tensor(0.6)}
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def validation_end(self, outputs):
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return {'val_loss': torch.tensor(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': torch.tensor(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': torch.tensor(0.7)}
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def validation_end(self, outputs):
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return {'val_loss': torch.tensor(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': torch.tensor(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': torch.tensor(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': torch.tensor(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': torch.tensor(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': torch.tensor(0.7)}
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