Add test_parameter_count_01().

src/icosagon/convlayer.py

@@ -68,6 +68,33 @@ class DecagonLayer(torch.nn.Module):
         self.next_layer_repr[fam.node_type_row].append(
             Convolutions(fam.node_type_column, convolutions))
 
+    # def build_fam_two_node_types_sym(self, fam) -> None:
+    #     convolutions_row = torch.nn.ModuleList()
+    #     convolutions_column = torch.nn.ModuleList()
+    #
+    #     if self.input_dim[fam.node_type_column] != \
+    #         self.input_dim[fam.node_type_row]:
+    #         raise ValueError('input_dim for row and column must be equal for a symmetric family')
+    #
+    #     if self.output_dim[fam.node_type_column] != \
+    #         self.output_dim[fam.node_type_row]:
+    #         raise ValueError('output_dim for row and column must be equal for a symmetric family')
+    #
+    #     for r in fam.relation_types:
+    #         assert r.adjacency_matrix is not None and \
+    #             r.adjacency_matrix_backward is not None
+    #         conv = DropoutGraphConvActivation(self.input_dim[fam.node_type_column],
+    #             self.output_dim[fam.node_type_row], r.adjacency_matrix,
+    #             self.keep_prob, self.rel_activation)
+    #         convolutions_row.append(conv)
+    #         convolutions_column.append(conv.clone(r.adjacency_matrix_backward))
+    #
+    #     self.next_layer_repr[fam.node_type_row].append(
+    #         Convolutions(fam.node_type_column, convolutions_row))
+    #
+    #     self.next_layer_repr[fam.node_type_column].append(
+    #         Convolutions(fam.node_type_row, convolutions_column))
+
     def build_fam_two_node_types(self, fam) -> None:
         convolutions_row = torch.nn.ModuleList()
         convolutions_column = torch.nn.ModuleList()
@@ -91,6 +118,12 @@ class DecagonLayer(torch.nn.Module):
         self.next_layer_repr[fam.node_type_column].append(
             Convolutions(fam.node_type_row, convolutions_column))
 
+    # def build_fam_two_node_types(self, fam) -> None:
+    #     if fam.is_symmetric:
+    #         self.build_fam_two_node_types_sym(fam)
+    #     else:
+    #         self.build_fam_two_node_types_asym(fam)
+
     def build_family(self, fam) -> None:
         if fam.node_type_row == fam.node_type_column:
             self.build_fam_one_node_type(fam)

src/icosagon/convolve.py

@@ -48,3 +48,10 @@ class DropoutGraphConvActivation(torch.nn.Module):
         x = self.graph_conv(x)
         x = self.activation(x)
         return x
+
+    def clone(self, adjacency_matrix) -> 'DropoutGraphConvActivation':
+        res = DropoutGraphConvActivation(self.input_dim,
+            self.output_dim, adjacency_matrix, self.keep_prob,
+            self.activation)
+        res.graph_conv.weight = self.graph_conv.weight
+        return res

tests/icosagon/test_convlayer.py

@@ -280,3 +280,21 @@ def test_module_nesting_06():
     dummy_7 = Dummy7()
     dummy_7 = dummy_7.to(device)
     assert dummy_7.dummy_1[0][0].whatever.device == device
+
+
+def test_parameter_count_01():
+    d = Data()
+    d.add_node_type('Dummy', 100)
+    fam = d.add_relation_family('Dummy-Dummy', 0, 0, True)
+    fam.add_relation_type('Dummy Relation 1',
+        _symmetric_random(100, 100).to_sparse())
+    fam.add_relation_type('Dummy Relation 2',
+        _symmetric_random(100, 100).to_sparse())
+
+    in_layer = OneHotInputLayer(d)
+    assert len(list(in_layer.parameters())) == 1
+
+    d_layer = DecagonLayer(in_layer.output_dim, output_dim=32, data=d,
+        keep_prob=1., rel_activation=lambda x: x,
+        layer_activation=lambda x: x)
+    assert len(list(d_layer.parameters())) == 2