def test_hparams_save_load(tmpdir):
model = EvalModelTemplate(vars(tutils.get_default_hparams()))
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
)
# fit model
result = trainer.fit(model)
assert result == 1
# try to load the model now
pretrained_model = tutils.load_model_from_checkpoint(
trainer.checkpoint_callback.dirpath, module_class=EvalModelTemplate)
assert pretrained_model
def test_hparams_save_load(tmpdir):
model = DictHparamsModel({'in_features': 28 * 28, 'out_features': 10, 'failed_key': lambda x: x})
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
)
# fit model
result = trainer.fit(model)
assert result == 1
# try to load the model now
pretrained_model = tutils.load_model_from_checkpoint(
trainer.checkpoint_callback.dirpath,
module_class=DictHparamsModel
)
def test_hparams_save_load(tmpdir):
model = DictHparamsModel({'in_features': 28 * 28, 'out_features': 10})
# logger file to get meta
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1
# try to load the model now
pretrained_model = tutils.load_model_from_checkpoint(
trainer.checkpoint_callback.dirpath, module_class=DictHparamsModel)
