| @@ -232,3 +232,56 @@ def test_decode_layer_05(): | |||||
| # assert isinstance(rel_pred.edges_neg, TrainValTest) | # assert isinstance(rel_pred.edges_neg, TrainValTest) | ||||
| # assert isinstance(rel_pred.edges_back_pos, TrainValTest) | # assert isinstance(rel_pred.edges_back_pos, TrainValTest) | ||||
| # assert isinstance(rel_pred.edges_back_neg, TrainValTest) | # assert isinstance(rel_pred.edges_back_neg, TrainValTest) | ||||
| def test_decode_layer_parameter_count_01(): | |||||
| d = Data() | |||||
| d.add_node_type('Dummy', 100) | |||||
| fam = d.add_relation_family('Dummy-Dummy', 0, 0, False) | |||||
| fam.add_relation_type('Dummy Relation 1', | |||||
| torch.rand((100, 100), dtype=torch.float32).round().to_sparse()) | |||||
| prep_d = prepare_training(d, TrainValTest(.8, .1, .1)) | |||||
| dec = DecodeLayer(input_dim=[ 32 ], data=prep_d, keep_prob=1., | |||||
| activation=lambda x: x) | |||||
| assert len(list(dec.parameters())) == 2 | |||||
| def test_decode_layer_parameter_count_02(): | |||||
| d = Data() | |||||
| d.add_node_type('Dummy', 100) | |||||
| fam = d.add_relation_family('Dummy-Dummy', 0, 0, False) | |||||
| fam.add_relation_type('Dummy Relation 1', | |||||
| torch.rand((100, 100), dtype=torch.float32).round().to_sparse()) | |||||
| fam.add_relation_type('Dummy Relation 2', | |||||
| torch.rand((100, 100), dtype=torch.float32).round().to_sparse()) | |||||
| prep_d = prepare_training(d, TrainValTest(.8, .1, .1)) | |||||
| dec = DecodeLayer(input_dim=[ 32 ], data=prep_d, keep_prob=1., | |||||
| activation=lambda x: x) | |||||
| assert len(list(dec.parameters())) == 3 | |||||
| def test_decode_layer_parameter_count_03(): | |||||
| d = Data() | |||||
| d.add_node_type('Dummy', 100) | |||||
| for _ in range(2): | |||||
| fam = d.add_relation_family('Dummy-Dummy', 0, 0, False) | |||||
| fam.add_relation_type('Dummy Relation 1', | |||||
| torch.rand((100, 100), dtype=torch.float32).round().to_sparse()) | |||||
| fam.add_relation_type('Dummy Relation 2', | |||||
| torch.rand((100, 100), dtype=torch.float32).round().to_sparse()) | |||||
| prep_d = prepare_training(d, TrainValTest(.8, .1, .1)) | |||||
| dec = DecodeLayer(input_dim=[ 32 ], data=prep_d, keep_prob=1., | |||||
| activation=lambda x: x) | |||||
| assert len(list(dec.parameters())) == 6 | |||||