Update (dmlc#1319)

examples/pytorch/gin/README.md

@@ -6,7 +6,7 @@ Graph Isomorphism Network (GIN)
 
 Dependencies
 ------------
-- PyTorch 1.0.1+
+- PyTorch 1.1.0+
 - sklearn
 - tqdm
 

examples/pytorch/gin/dataloader.py

@@ -19,7 +19,7 @@ def collate(samples):
     for g in graphs:
         # deal with node feats
         for key in g.node_attr_schemes().keys():
-            g.ndata[key] = torch.from_numpy(g.ndata[key]).float()
+            g.ndata[key] = g.ndata[key].float()
         # no edge feats
     batched_graph = dgl.batch(graphs)
     labels = torch.tensor(labels)

examples/pytorch/gin/main.py

@@ -73,14 +73,14 @@ def eval_net(args, net, dataloader, criterion):
 def main(args):
 
     # set up seeds, args.seed supported
-    torch.manual_seed(seed=0)
-    np.random.seed(seed=0)
+    torch.manual_seed(seed=args.seed)
+    np.random.seed(seed=args.seed)
 
     is_cuda = not args.disable_cuda and torch.cuda.is_available()
 
     if is_cuda:
         args.device = torch.device("cuda:" + str(args.device))
-        torch.cuda.manual_seed_all(seed=0)
+        torch.cuda.manual_seed_all(seed=args.seed)
     else:
         args.device = torch.device("cpu")
 
@@ -109,9 +109,9 @@ def main(args):
     lrbar = tqdm(range(args.epochs), unit="epoch", position=5, ncols=0, file=sys.stdout)
 
     for epoch, _, _ in zip(tbar, vbar, lrbar):
-        scheduler.step()
 
         train(args, model, trainloader, optimizer, criterion, epoch)
+        scheduler.step()
 
         train_loss, train_acc = eval_net(
             args, model, trainloader, criterion)

examples/pytorch/gin/parser.py

@@ -19,6 +19,7 @@ def _parse(self):
         # dataset
         self.parser.add_argument(
             '--dataset', type=str, default="MUTAG",
+            choices=['MUTAG', 'COLLAB', 'IMDBBINARY', 'IMDBMULTI'],
             help='name of dataset (default: MUTAG)')
         self.parser.add_argument(
             '--batch_size', type=int, default=32,
@@ -39,9 +40,6 @@ def _parse(self):
             help='which gpu device to use (default: 0)')
 
         # net
-        self.parser.add_argument(
-            '--net', type=str, default="gin",
-            help='gnn net (default: gin)')
         self.parser.add_argument(
             '--num_layers', type=int, default=5,
             help='number of layers (default: 5)')
@@ -64,9 +62,6 @@ def _parse(self):
         self.parser.add_argument(
             '--learn_eps', action="store_true",
             help='learn the epsilon weighting')
-        self.parser.add_argument(
-            '--degree_as_tag', action="store_true",
-            help='take the degree of nodes as input feature')
 
         # learning
         self.parser.add_argument(