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