[BugFix] Fix bug in RGCN data processing and use index_select to impr…

…ove speed (dmlc#429) * use index_select instead of __getitem__ * fix bug in dataset processing * fix edge_type shape bug * comments
SunYa0 · Mar 4, 2019 · d3c24cc · d3c24cc
1 parent fb4246e
commit d3c24cc
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Showing 3 changed files with 8 additions and 6 deletions.
diff --git a/examples/pytorch/rgcn/layers.py b/examples/pytorch/rgcn/layers.py
@@ -89,10 +89,10 @@ def msg_func(edges):
                 # an embedding lookup using source node id
                 embed = weight.view(-1, self.out_feat)
                 index = edges.data['type'] * self.in_feat + edges.src['id']
-                return {'msg': embed[index] * edges.data['norm']}
+                return {'msg': embed.index_select(0, index) * edges.data['norm']}
         else:
             def msg_func(edges):
-                w = weight[edges.data['type']]
+                w = weight.index_select(0, edges.data['type'])
                 msg = torch.bmm(edges.src['h'].unsqueeze(1), w).squeeze()
                 msg = msg * edges.data['norm']
                 return {'msg': msg}
@@ -119,7 +119,7 @@ def __init__(self, in_feat, out_feat, num_rels, num_bases, bias=None,
         nn.init.xavier_uniform_(self.weight, gain=nn.init.calculate_gain('relu'))
 
     def msg_func(self, edges):
-        weight = self.weight[edges.data['type']].view(
+        weight = self.weight.index_select(0, edges.data['type']).view(
                     -1, self.submat_in, self.submat_out)
         node = edges.src['h'].view(-1, 1, self.submat_in)
         msg = torch.bmm(node, weight).view(-1, self.out_feat)

diff --git a/examples/pytorch/rgcn/link_predict.py b/examples/pytorch/rgcn/link_predict.py
@@ -149,7 +149,7 @@ def main(args):
 
         # set node/edge feature
         node_id = torch.from_numpy(node_id).view(-1, 1)
-        edge_type = torch.from_numpy(edge_type).view(-1, 1)
+        edge_type = torch.from_numpy(edge_type)
         node_norm = torch.from_numpy(node_norm).view(-1, 1)
         data, labels = torch.from_numpy(data), torch.from_numpy(labels)
         deg = g.in_degrees(range(g.number_of_nodes())).float().view(-1, 1)

diff --git a/python/dgl/contrib/data/knowledge_graph.py b/python/dgl/contrib/data/knowledge_graph.py
@@ -410,8 +410,10 @@ def _load_data(dataset_str='aifb', dataset_path=None):
                 dst = nodes_dict[o]
                 assert src < num_node and dst < num_node
                 rel = relations_dict[p]
-                edge_list.append((src, dst, 2 * rel))
-                edge_list.append((dst, src, 2 * rel + 1))
+                # relation id 0 is self-relation, so others should start with 1
+                edge_list.append((src, dst, 2 * rel + 1))
+                # reverse relation
+                edge_list.append((dst, src, 2 * rel + 2))
 
             # sort indices by destination
             edge_list = sorted(edge_list, key=lambda x: (x[1], x[0], x[2]))