Add test_decagon_layer_05().

tests/decagon_pytorch/test_layer.py

@@ -189,3 +189,53 @@ def test_decagon_layer_04():
     assert len(out_d_layer) == 1
 
     assert torch.all(out_d_layer[0] == out_multi_dgca)
+
+
+def test_decagon_layer_05():
+    # check if it is equivalent to MultiDGCA, as it should be
+    # this time for two relations, same edge type
+
+    d = Data()
+    d.add_node_type('Dummy', 100)
+    d.add_relation_type('Dummy Relation 1', 0, 0,
+        torch.rand((100, 100), dtype=torch.float32).round().to_sparse())
+    d.add_relation_type('Dummy Relation 2', 0, 0,
+        torch.rand((100, 100), dtype=torch.float32).round().to_sparse())
+
+    in_layer = OneHotInputLayer(d)
+
+    multi_dgca = SparseMultiDGCA([100, 100], 32,
+        [r.adjacency_matrix for r in d.relation_types[0, 0]],
+        keep_prob=1., activation=lambda x: x)
+
+    d_layer = DecagonLayer(d, in_layer, output_dim=32,
+        keep_prob=1., rel_activation=lambda x: x,
+        layer_activation=lambda x: x)
+
+    assert all([
+        isinstance(dgca, DropoutGraphConvActivation) \
+            for dgca in d_layer.next_layer_repr[0][0][0]
+    ])
+
+    weight = [ dgca.graph_conv.weight \
+        for dgca in d_layer.next_layer_repr[0][0][0] ]
+    assert all([
+        isinstance(w, torch.Tensor) \
+            for w in weight
+    ])
+
+    assert len(multi_dgca.sparse_dgca) == 2
+    for i in range(2):
+        assert isinstance(multi_dgca.sparse_dgca[i], SparseDropoutGraphConvActivation)
+
+    for i in range(2):
+        multi_dgca.sparse_dgca[i].sparse_graph_conv.weight = weight[i]
+
+    out_d_layer = d_layer()
+    x = in_layer()
+    out_multi_dgca = multi_dgca([ x[0], x[0] ])
+
+    assert isinstance(out_d_layer, list)
+    assert len(out_d_layer) == 1
+
+    assert torch.all(out_d_layer[0] == out_multi_dgca)