def test_tbd_remove_in_v1_0_0_model_hooks():
hparams = EvalModelTemplate.get_default_hparams()
model = ModelVer0_6(hparams)
with pytest.deprecated_call(match='v1.0'):
trainer = Trainer(logger=False)
trainer.test(model)
assert trainer.callback_metrics == {'test_loss': 0.6}
with pytest.deprecated_call(match='v1.0'):
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)
with pytest.deprecated_call(match='v1.0'):
trainer = Trainer(logger=False)
trainer.test(model)
assert trainer.callback_metrics == {'test_loss': 0.7}
with pytest.deprecated_call(match='v1.0'):
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}
def test_tbd_remove_in_v1_0_0_model_hooks():
model = ModelVer0_6()
with pytest.deprecated_call(match='v1.0'):
trainer = Trainer(logger=False)
trainer.test(model)
assert trainer.callback_metrics == {'test_loss': torch.tensor(0.6)}
with pytest.deprecated_call(match='will be removed in v1.0'):
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': torch.tensor(0.6)}
model = ModelVer0_7()
with pytest.deprecated_call(match='will be removed in v1.0'):
trainer = Trainer(logger=False)
trainer.test(model)
assert trainer.callback_metrics == {'test_loss': torch.tensor(0.7)}
with pytest.deprecated_call(match='will be removed in v1.0'):
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': torch.tensor(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}
