PR [#32] change_model_func_name -> dev

change model function name decode to lp_decode in response to PR #31

autogl/module/model/gat.py

@@ -104,12 +104,12 @@ class GAT(torch.nn.Module):
                 # x = F.dropout(x, p=self.args["dropout"], training=self.training)
         return x
 
-    def decode(self, z, pos_edge_index, neg_edge_index):
+    def lp_decode(self, z, pos_edge_index, neg_edge_index):
         edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
         logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
         return logits
 
-    def decode_all(self, z):
+    def lp_decode_all(self, z):
         prob_adj = z @ z.t()
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 

autogl/module/model/gcn.py

@@ -93,12 +93,12 @@ class GCN(torch.nn.Module):
                 # x = F.dropout(x, p=self.args["dropout"], training=self.training)
         return x
 
-    def decode(self, z, pos_edge_index, neg_edge_index):
+    def lp_decode(self, z, pos_edge_index, neg_edge_index):
         edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
         logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
         return logits
 
-    def decode_all(self, z):
+    def lp_decode_all(self, z):
         prob_adj = z @ z.t()
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 

autogl/module/model/graphsage.py

@@ -180,12 +180,12 @@ class GraphSAGE(torch.nn.Module):
                 # x = F.dropout(x, p=self.args["dropout"], training=self.training)
         return x
 
-    def decode(self, z, pos_edge_index, neg_edge_index):
+    def lp_decode(self, z, pos_edge_index, neg_edge_index):
         edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)
         logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)
         return logits
 
-    def decode_all(self, z):
+    def lp_decode_all(self, z):
         prob_adj = z @ z.t()
         return (prob_adj > 0).nonzero(as_tuple=False).t()
 

autogl/module/train/link_prediction.py

@@ -198,7 +198,7 @@ class LinkPredictionTrainer(BaseLinkPredictionTrainer):
             optimizer.zero_grad()
             # res = self.model.model.forward(data)
             z = self.model.model.encode(data)
-            link_logits = self.model.model.decode(
+            link_logits = self.model.model.lp_decode(
                 z, data.train_pos_edge_index, neg_edge_index
             )
             link_labels = self.get_link_labels(
@@ -320,7 +320,7 @@ class LinkPredictionTrainer(BaseLinkPredictionTrainer):
         self.model.model.eval()
         with torch.no_grad():
             z = self.predict_only(data)
-            link_logits = self.model.model.decode(z, pos_edge_index, neg_edge_index)
+            link_logits = self.model.model.lp_decode(z, pos_edge_index, neg_edge_index)
             link_probs = link_logits.sigmoid()
 
         return link_probs