Fix aggregation of representations from different edge types.

src/triacontagon/model.py

@@ -133,9 +133,10 @@ class Model(torch.nn.Module):
         List[torch.Tensor]:
 
         cur_layer_repr = in_layer_repr
-        next_layer_repr = [ None ] * len(self.data.vertex_types)
-
+        
         for i in range(len(self.layer_dimensions) - 1):
+            next_layer_repr = [ [] for _ in range(len(self.data.vertex_types)) ]
+
             for _, et in self.data.edge_types.items():
                 vt_row, vt_col = et.vertex_type_row, et.vertex_type_column
                 adj_matrices = self.adj_matrices['%d-%d' % (vt_row, vt_col)]
@@ -158,13 +159,18 @@ class Model(torch.nn.Module):
                     self.data.vertex_types[vt_row].count,
                     self.layer_dimensions[i + 1])
 
-                print('b, Layer:', i, 'x.shape:', x.shape)
+                print('b, Layer:', i, 'vt_row:', vt_row, 'x.shape:', x.shape)
 
                 x = x.sum(dim=0)
                 x = torch.nn.functional.normalize(x, p=2, dim=1)
-                x = self.conv_activation(x)
+                # x = self.rel_activation(x)
+                print('c, Layer:', i, 'vt_row:', vt_row, 'x.shape:', x.shape)
+
+                next_layer_repr[vt_row].append(x)
+
+            next_layer_repr = [ self.conv_activation(sum(x)) \
+                for x in next_layer_repr ]
 
-                next_layer_repr[vt_row] = x
             cur_layer_repr = next_layer_repr
         return next_layer_repr