Use hints instead of is_autogenerated.

src/icosagon/data.py

@@ -5,11 +5,12 @@
 
 
 from collections import defaultdict
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 import torch
 from typing import List, \
     Dict, \
-    Tuple
+    Tuple, \
+    Any
 
 
 @dataclass
@@ -24,7 +25,7 @@ class RelationType(object):
     node_type_row: int
     node_type_column: int
     adjacency_matrix: torch.Tensor
-    is_autogenerated: bool = False
+    hints: Dict[str, Any] = field(default_factory=dict)
 
 
 class Data(object):
@@ -81,14 +82,16 @@ class Data(object):
 
         self.relation_types[node_type_row, node_type_column].append(
             RelationType(name, node_type_row, node_type_column,
-                adjacency_matrix, False))
+                adjacency_matrix))
 
         if node_type_row != node_type_column and two_way:
+            hints = { 'display': False }
             if adjacency_matrix_backward is None:
                 adjacency_matrix_backward = adjacency_matrix.transpose(0, 1)
+                hints['symmetric'] = True
             self.relation_types[node_type_column, node_type_row].append(
                 RelationType(name, node_type_column, node_type_row,
-                    adjacency_matrix_backward, True))
+                    adjacency_matrix_backward, hints))
 
     def __repr__(self):
         n = len(self.node_types)
@@ -114,7 +117,7 @@ class Data(object):
                     self.node_types[node_type_column].name + ':\n'
 
                 for r in self.relation_types[node_type_row, node_type_column]:
-                    if r.is_autogenerated:
+                    if not r.hints.get('display', True):
                         continue
                     s_1 += '    - ' + r.name + '\n'
                     count += 1

src/icosagon/declayer.py

@@ -71,6 +71,6 @@ class DecodeLayer(torch.nn.Module):
         for (node_type_row, node_type_column), dec in self.decoders.items():
             inputs_row = last_layer_repr[node_type_row]
             inputs_column = last_layer_repr[node_type_column]
-            pred_adj_matrices = dec(inputs_row, inputs_column)
+            pred_adj_matrices = [ dec(inputs_row, inputs_column, k) for k in range(dec.num_relation_types) ]
             res[node_type_row, node_type_column] = pred_adj_matrices
         return res