def test_call_to_trainer_method(tmpdir, optimizer):
"""Test that directly calling the trainer method works"""
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
if optimizer == "adagrad":
model.configure_optimizers = model.configure_optimizers__adagrad
before_lr = hparams.get("learning_rate")
# logger file to get meta
trainer = Trainer(default_root_dir=tmpdir, max_epochs=2)
lrfinder = trainer.tuner.lr_find(model, mode="linear")
after_lr = lrfinder.suggestion()
model.learning_rate = after_lr
trainer.tune(model)
assert before_lr != after_lr, "Learning rate was not altered after running learning rate finder"
def test_call_to_trainer_method(tmpdir):
""" Test that directly calling the trainer method works """
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
before_lr = hparams.get('learning_rate')
# logger file to get meta
trainer = Trainer(
default_save_path=tmpdir,
max_epochs=2,
)
lrfinder = trainer.lr_find(model, mode='linear')
after_lr = lrfinder.suggestion()
model.learning_rate = after_lr
trainer.fit(model)
assert before_lr != after_lr, \
'Learning rate was not altered after running learning rate finder'
def test_datamodule_parameter(tmpdir):
""" Test that the datamodule parameter works """
# trial datamodule
dm = TrialMNISTDataModule(tmpdir)
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
before_lr = hparams.get('learning_rate')
# logger file to get meta
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
)
lrfinder = trainer.tuner.lr_find(model, datamodule=dm)
after_lr = lrfinder.suggestion()
model.learning_rate = after_lr
assert before_lr != after_lr, \
'Learning rate was not altered after running learning rate finder'
