def test_model_checkpoint_to_yaml(tmpdir, save_top_k):
""" Test that None in checkpoint callback is valid and that chkp_path is set correctly """
tutils.reset_seed()
model = EvalModelTemplate()
checkpoint = ModelCheckpoint(filepath=tmpdir, monitor='early_stop_on', save_top_k=save_top_k)
trainer = Trainer(default_root_dir=tmpdir, checkpoint_callback=checkpoint, overfit_batches=0.20, max_epochs=2)
trainer.fit(model)
path_yaml = os.path.join(tmpdir, 'best_k_models.yaml')
checkpoint.to_yaml(path_yaml)
d = yaml.full_load(open(path_yaml, 'r'))
best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
assert d == best_k
def test_model_checkpoint_to_yaml(tmpdir, save_top_k: int):
"""Test that None in checkpoint callback is valid and that chkp_path is set correctly."""
tutils.reset_seed()
model = LogInTwoMethods()
checkpoint = ModelCheckpoint(dirpath=tmpdir, monitor="early_stop_on", save_top_k=save_top_k)
trainer = Trainer(default_root_dir=tmpdir, callbacks=[checkpoint], overfit_batches=0.20, max_epochs=2)
trainer.fit(model)
path_yaml = os.path.join(tmpdir, "best_k_models.yaml")
checkpoint.to_yaml(path_yaml)
d = yaml.full_load(open(path_yaml))
best_k = dict(checkpoint.best_k_models.items())
assert d == best_k