fix relgraphconv bug (#3256)

examples/pytorch/rgcn/README.md

@@ -17,43 +17,43 @@ pip install requests torch rdflib pandas
 Example code was tested with rdflib 4.2.2 and pandas 0.23.4
 
 ### Entity Classification
-AIFB: accuracy 92.59% (3 runs, DGL), 95.83% (paper)
+AIFB: accuracy 96.29% (3 runs, DGL), 95.83% (paper)
 ```
 python3 entity_classify.py -d aifb --testing --gpu 0
 ```
 
-MUTAG: accuracy 72.55% (3 runs, DGL), 73.23% (paper)
+MUTAG: accuracy 70.59% (3 runs, DGL), 73.23% (paper)
 ```
 python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0
 ```
 
-BGS: accuracy 89.66% (3 runs, DGL), 83.10% (paper)
+BGS: accuracy 93.10% (3 runs, DGL), 83.10% (paper)
 ```
 python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0
 ```
 
-AM: accuracy 89.73% (3 runs, DGL), 89.29% (paper)
+AM: accuracy 89.22% (3 runs, DGL), 89.29% (paper)
 ```
 python3 entity_classify.py -d am --n-bases=40 --n-hidden=10 --l2norm=5e-4 --testing
 ```
 
 ### Entity Classification with minibatch
-AIFB: accuracy avg(5 runs) 90.56%, best 94.44% (DGL)
+AIFB: accuracy avg(5 runs) 90.00%, best 94.44% (DGL)
 ```
 python3 entity_classify_mp.py -d aifb --testing --gpu 0 --fanout='20,20' --batch-size 128
 ```
 
-MUTAG: accuracy avg(10 runs) 69.41%, best 76.47% (DGL)
+MUTAG: accuracy avg(10 runs) 62.94%, best 72.06% (DGL)
 ```
 python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0 --batch-size 64 --fanout "-1, -1" --use-self-loop --dgl-sparse --n-epochs 20 --sparse-lr 0.01 --dropout 0.5
 ```
 
-BGS: accuracy avg(5 runs) 85.52%, best 93.10% (DGL)
+BGS: accuracy avg(5 runs) 78.62%, best 86.21% (DGL)
 ```
 python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout "-1, -1"  --n-epochs=16 --batch-size=16 --dgl-sparse  --lr 0.01 --sparse-lr 0.05 --dropout 0.3
 ```
 
-AM: accuracy avg(5 runs) 88.59%, best 88.89% (DGL)
+AM: accuracy avg(5 runs) 87.37%, best 89.9% (DGL)
 ```
 python3 entity_classify_mp.py -d am --l2norm 5e-4 --n-bases 40 --testing  --gpu 0 --fanout '35,35' --batch-size 64 --n-hidden 16 --use-self-loop --n-epochs=20 --dgl-sparse --lr 0.01  --sparse-lr 0.02 --dropout 0.7
 ```

python/dgl/nn/pytorch/conv/relgraphconv.py

@@ -218,8 +218,9 @@ class RelGraphConv(nn.Module):
             if isinstance(etypes, list):
                 etypes = th.repeat_interleave(th.arange(len(etypes), device=device),
                                               th.tensor(etypes, device=device))
+            idim = weight.shape[1]
             weight = weight.view(-1, weight.shape[2])
-            flatidx = etypes * weight.shape[1] + h
+            flatidx = etypes * idim + h
             msg = weight.index_select(0, flatidx)
         elif self.low_mem:
             # A more memory-friendly implementation.