Improving the MoNet example. (#5999)

Co-authored-by: rudongyu <ru_dongyu@outlook.com>

examples/pytorch/monet/citation.py

@@ -78,7 +78,7 @@ def main(args):
     val_mask = g.ndata["val_mask"]
     test_mask = g.ndata["test_mask"]
     in_feats = features.shape[1]
-    n_classes = data.num_labels
+    n_classes = data.num_classes
     n_edges = g.num_edges()
     print(
         """----Data statistics------'
@@ -127,7 +127,7 @@ def main(args):
     )
 
     # initialize graph
-    dur = []
+    mean = 0
     for epoch in range(args.n_epochs):
         model.train()
         if epoch >= 3:
@@ -141,19 +141,18 @@ def main(args):
         optimizer.step()
 
         if epoch >= 3:
-            dur.append(time.time() - t0)
-
-        acc = evaluate(model, features, pseudo, labels, val_mask)
-        print(
-            "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
-            "ETputs(KTEPS) {:.2f}".format(
-                epoch,
-                np.mean(dur),
-                loss.item(),
-                acc,
-                n_edges / np.mean(dur) / 1000,
+            mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)
+            acc = evaluate(model, features, pseudo, labels, val_mask)
+            print(
+                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
+                "ETputs(KTEPS) {:.2f}".format(
+                    epoch,
+                    mean,
+                    loss.item(),
+                    acc,
+                    n_edges / mean / 1000,
+                )
             )
-        )
 
     print()
     acc = evaluate(model, features, pseudo, labels, test_mask)