[Example] Rgcn support ogbn-mag dataset. (dmlc#1812)

* rgcn support ogbn-mag dataset * upd * multi-gpu val and test * Fix * fix * Add support for ogbn-mag * Fix * Fix * Fix * Fix * Add layer_norm * update * Fix merge * Clean some code * update Readme * upd Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Ubuntu <[email protected]> Co-authored-by: Ubuntu <[email protected]>
feixian15 · Aug 9, 2020 · 4ef01db · 4ef01db
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diff --git a/examples/pytorch/rgcn/README.md b/examples/pytorch/rgcn/README.md
@@ -40,7 +40,7 @@ python3 entity_classify.py -d am --n-bases=40 --n-hidden=10 --l2norm=5e-4 --test
 ### Entity Classification with minibatch
 AIFB: accuracy avg(5 runs) 90.56%, best 94.44% (DGL)
 ```
-python3 entity_classify_mp.py -d aifb --testing --gpu 0 --fanout=20 --batch-size 128
+python3 entity_classify_mp.py -d aifb --testing --gpu 0 --fanout='20,20' --batch-size 128
 ```
 
 MUTAG: accuracy avg(5 runs) 66.77%, best 69.12% (DGL)
@@ -49,16 +49,30 @@ python3 entity_classify_mp.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gp
 ```
 
 BGS: accuracy avg(5 runs) 91.72%, best 96.55% (DGL)
-
 ```
-python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout 40 --n-epochs=40 --batch-size=128
+python3 entity_classify_mp.py -d bgs --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout '40,40' --n-epochs=40 --batch-size=128
 ```
 
 AM: accuracy avg(5 runs) 88.28%, best 90.40% (DGL)
 ```
-python3 entity_classify_mp.py -d am --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout 35 --batch-size 256 --lr 1e-2 --n-hidden 16 --use-self-loop --n-epochs=40
+python3 entity_classify_mp.py -d am --l2norm 5e-4 --n-bases 40 --testing --gpu 0 --fanout '35,35' --batch-size 256 --lr 1e-2 --n-hidden 16 --use-self-loop --n-epochs=40
+```
+
+### Entity Classification on OGBN-MAG
+Test-bd: P3-8xlarge
+
+OGBN-MAG accuracy 46.22
+```
+python3 entity_classify_mp.py -d ogbn-mag --testing --fanout='25,30' --batch-size 512 --n-hidden 64 --lr 0.01 --num-worker 0 --eval-batch-size 8 --low-mem --gpu 0,1,2,3,4,5,6,7 --dropout 0.5 --use-self-loop --n-bases 2 --n-epochs 3 --mix-cpu-gpu --node-feats --layer-norm
 ```
 
+OGBN-MAG without node-feats 43.24
+```
+python3 entity_classify_mp.py -d ogbn-mag --testing --fanout='25,25' --batch-size 256 --n-hidden 64 --lr 0.01 --num-worker 0 --eval-batch-size 8 --low-mem --gpu 0,1,2,3,4,5,6,7 --dropout 0.5 --use-self-loop --n-bases 2 --n-epochs 3 --mix-cpu-gpu --layer-norm
+```
+
+Test-bd: P2-8xlarge
+
 ### Link Prediction
 FB15k-237: MRR 0.151 (DGL), 0.158 (paper)
 ```