[Bugfix] fix a typo in consis_loss function (#2616)

examples/pytorch/grand/README.md

@@ -61,7 +61,6 @@ Train a model which follows the original hyperparameters on different datasets.
 python main.py --dataname cora --gpu 0 --lam 1.0 --tem 0.5 --order 8 --sample 4 --input_droprate 0.5 --hidden_droprate 0.5 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 100 --lr 1e-2  --epochs 2000
 # Citeseer:
 python main.py --dataname citeseer --gpu 0 --lam 0.7 --tem 0.3 --order 2 --sample 2 --input_droprate 0.0 --hidden_droprate 0.2 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 100 --lr 1e-2  --epochs 2000
-
 # Pubmed:
 python main.py --dataname pubmed --gpu 0 --lam 1.0 --tem 0.2 --order 5 --sample 4 --input_droprate 0.6 --hidden_droprate 0.8 --dropnode_rate 0.5 --hid_dim 32 --early_stopping 200 --lr 0.2 --epochs 2000 --use_bn
 ```

examples/pytorch/grand/main.py

@@ -58,7 +58,7 @@ def consis_loss(logps, temp, lam):
     sharp_p = (th.pow(avg_p, 1./temp) / th.sum(th.pow(avg_p, 1./temp), dim=1, keepdim=True)).detach()
 
     sharp_p = sharp_p.unsqueeze(2)
-    loss = th.mean(th.sum(th.pow(ps - sharp_p, 1./temp), dim = 1, keepdim=True))
+    loss = th.mean(th.sum(th.pow(ps - sharp_p, 2), dim = 1, keepdim=True))
 
     loss = lam * loss
     return loss
@@ -102,7 +102,6 @@ if __name__ == '__main__':
     test_idx = th.nonzero(test_mask, as_tuple=False).squeeze().to(device)
 
     # Step 2: Create model =================================================================== #
-    
     model = GRAND(n_features, args.hid_dim, n_classes, args.sample, args.order,
                   args.dropnode_rate, args.input_droprate, 
                   args.hidden_droprate, args.use_bn)