[Example] rm gcn spmv (dmlc#2044)

* rm gcn spmv * remove useless test * remove useless test Co-authored-by: Ubuntu <[email protected]>
sanchit-misra · Aug 18, 2020 · 09ec602 · 09ec602
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diff --git a/examples/mxnet/gcn/README.md b/examples/mxnet/gcn/README.md
@@ -19,8 +19,6 @@ Codes
 The folder contains three implementations of GCN:
 - `gcn.py` uses DGL's predefined graph convolution module.
 - `gcn_mp.py` uses user-defined message and reduce functions.
-- `gcn_spmv.py` improves from `gcn_mp.py` by using DGL's builtin functions
-   so SPMV optimization could be applied.
 Modify `train.py` to switch between different implementations.
 
 The provided implementation in `gcn_concat.py` is a bit different from the
@@ -40,8 +38,7 @@ DGLBACKEND=mxnet python3 train.py --dataset cora --gpu 0 --self-loop
 Results (`gcn_concat.py vs. gcn.py`)
 ------------------------------------
 `gcn_concat.py` uses concatenation of hidden units to account for multi-hop
-  skip-connections, while `gcn_spmv.py` uses simple additions (the original paper
-omitted this detail). We feel concatenation is superior
+  skip-connections. We feel concatenation is superior
 because all neighboring information is presented without additional modeling
 assumptions.
 These results are based on single-run training to minimize the cross-entropy
@@ -56,12 +53,6 @@ factorization. Given the small datasets, we can't draw such conclusions from the
 # Final accuracy 57.70% MLP without GCN
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" --n-epochs 200 --n-layers 0
 
-# Final accuracy 68.20% with 2-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "citeseer" --n-epochs 200 --n-layers 1
-
-# Final accuracy 18.40% with 10-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "citeseer" --n-epochs 200 --n-layers 9
-
 # Final accuracy 65.70% with 10-layer GCN with skip connection
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop
 
@@ -74,12 +65,6 @@ DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" -
 # Final accuracy 53.20% MLP without GCN
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-epochs 200 --n-layers 0
 
-# Final accuracy 81.40% with 2-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "cora" --n-epochs 200 --n-layers 1
-
-# Final accuracy 27.60% with 10-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "cora" --n-epochs 200 --n-layers 9
-
 # Final accuracy 72.60% with 2-layer GCN with skip connection
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop
 
@@ -92,12 +77,6 @@ DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-e
 # Final accuracy 70.30% MLP without GCN
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "pubmed" --n-epochs 200 --n-layers 0
 
-# Final accuracy 77.40% with 2-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "pubmed" --n-epochs 200 --n-layers 1
-
-# Final accuracy 36.20% with 10-layer GCN
-DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_spmv.py --dataset "pubmed" --n-epochs 200 --n-layers 9
-
 # Final accuracy 78.30% with 2-layer GCN with skip connection
 DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "pubmed" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop
 

diff --git a/examples/mxnet/gcn/gcn_spmv.py b/examples/mxnet/gcn/gcn_spmv.py
diff --git a/examples/pytorch/gcn/README.md b/examples/pytorch/gcn/README.md
@@ -19,8 +19,6 @@ Codes
 The folder contains three implementations of GCN:
 - `gcn.py` uses DGL's predefined graph convolution module.
 - `gcn_mp.py` uses user-defined message and reduce functions.
-- `gcn_spmv.py` improves from `gcn_mp.py` by using DGL's builtin functions
-   so SPMV optimization could be applied.
 Modify `train.py` to switch between different implementations.
 
 Results

diff --git a/examples/pytorch/gcn/gcn_spmv.py b/examples/pytorch/gcn/gcn_spmv.py
diff --git a/tests/scripts/task_example_test.bat b/tests/scripts/task_example_test.bat
@@ -25,7 +25,6 @@ SET DGL_DOWNLOAD_DIR=!CD!
 PUSHD !GCN_EXAMPLE_DIR!
 python pagerank.py || GOTO :FAIL
 python gcn\gcn.py --dataset cora --gpu !DEV! || GOTO :FAIL
-python gcn\gcn_spmv.py --dataset cora --gpu !DEV! || GOTO :FAIL
 POPD
 ENDLOCAL
 EXIT /B

diff --git a/tests/scripts/task_example_test.sh b/tests/scripts/task_example_test.sh
@@ -40,6 +40,5 @@ pushd $GCN_EXAMPLE_DIR> /dev/null
 
 python3 pagerank.py || fail "run pagerank.py on $1"
 python3 gcn/gcn.py --dataset cora --gpu $dev || fail "run gcn/gcn.py on $1"
-python3 gcn/gcn_spmv.py --dataset cora --gpu $dev || fail "run gcn/gcn_spmv.py on $1"
 
-popd > /dev/null
+popd > /dev/null