def test_trainer_arg_str(tmpdir, use_hparams):
""" Test that setting trainer arg to string works """
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
model.my_fancy_lr = 1.0 # update with non-standard field
model.hparams['my_fancy_lr'] = 1.0
before_lr = model.my_fancy_lr
if use_hparams:
del model.my_fancy_lr
model.configure_optimizers = model.configure_optimizers__lr_from_hparams
# logger file to get meta
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
auto_lr_find='my_fancy_lr',
)
trainer.tune(model)
if use_hparams:
after_lr = model.hparams.my_fancy_lr
else:
after_lr = model.my_fancy_lr
assert before_lr != after_lr, \
'Learning rate was not altered after running learning rate finder'
def test_trainer_arg_str(tmpdir):
""" Test that setting trainer arg to string works """
model = EvalModelTemplate()
model.my_fancy_lr = 1.0 # update with non-standard field
before_lr = model.my_fancy_lr
# logger file to get meta
trainer = Trainer(default_save_path=tmpdir,
max_epochs=2,
auto_lr_find='my_fancy_lr')
trainer.fit(model)
after_lr = model.my_fancy_lr
assert before_lr != after_lr, \
'Learning rate was not altered after running learning rate finder'
