feature: add a parse parameter degree_as_nlabel for pytorch-gin demo (#3676)

* feature: add a parse parameter degree_as_nlabel for pytorch-gin demo

* fix some typo

* [fix]: allow to benchmark all of the 9 dataset.

* [Feature] add epoch number to log

* [Feature]:simply list the command lines for all datasets (https://github.com/dmlc/dgl/pull/3676#discussion_r790270705

) and run a test.

* Update README.md
Co-authored-by: Ubuntu <ubuntu@ip-172-31-10-175.ap-northeast-1.compute.internal>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

examples/pytorch/gin/README.md

@@ -28,18 +28,56 @@ An experiment on the GIN in customized settings can be run with
 python main.py [--device 0 | --disable-cuda] --dataset COLLAB \
                --graph_pooling_type max --neighbor_pooling_type sum
 ```
+add `--degree_as_nlabel` to use one-hot encodings of node degrees as node feature vectors
 
 Results
 -------
 
-Run with following with the double SUM pooling way:
-(tested dataset: "MUTAG"(default), "COLLAB", "IMDBBINARY", "IMDBMULTI")
+results may **fluctuate**, due to random factors and the relatively small data set. if you want to follow the paper's setting, consider the script below.
+
 ```bash
+# 4 bioinformatics datasets setting graph_pooling_type=sum, the nodes have categorical input features 
 python main.py --dataset MUTAG --device 0  \
-                --graph_pooling_type sum --neighbor_pooling_type sum
+                --graph_pooling_type sum --neighbor_pooling_type sum --filename MUTAG.txt
+
+python main.py --dataset PTC --device 0  \
+                --graph_pooling_type sum --neighbor_pooling_type sum --filename PTC.txt
+
+python main.py --dataset NCI1 --device 0  \
+                --graph_pooling_type sum --neighbor_pooling_type sum --filename NCI1.txt
+
+python main.py --dataset PROTEINS --device 0  \
+                --graph_pooling_type sum --neighbor_pooling_type sum --filename PROTEINS.txt
+
+# 5 social network datasets setting graph_pooling_type=mean, for the REDDIT datasets, we set all node feature vectors to be the same 
+# (thus, features here are uninformative); for the other social networks, we use one-hot encodings of node degrees.  
+python main.py --dataset COLLAB --device 0  \
+                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename COLLAB.txt
+
+python main.py --dataset IMDBBINARY --device 0  \
+                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBBINARY.txt
+
+python main.py --dataset IMDBMULTI --device 0  \
+                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBMULTI.txt
+
+python main.py --dataset REDDITBINARY --device 0  \
+                --graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITBINARY.txt --fold_idx 6 --epoch 120
+
+python main.py --dataset REDDITMULTI5K --device 0  \
+                --graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITMULTI5K.txt
 ```
 
-* MUTAG: 0.85 (paper: ~0.89)
-* COLLAB: 0.89 (paper: ~0.80)
-* IMDBBINARY: 0.76 (paper: ~0.75)
-* IMDBMULTI: 0.51 (paper: ~0.52)
+one fold of 10 result are below.
+
+| dataset       | our result | paper report |
+| ------------- | ---------- | ------------ |
+| MUTAG         | 89.4      | 89.4 ± 5.6   |
+| PTC           | 68.5      | 64.6 ± 7.0   |
+| NCI1          | 78.5      | 82.7 ± 1.7   |
+| PROTEINS      | 72.3      | 76.2 ± 2.8   |
+| COLLAB        | 81.6      | 80.2 ± 1.9   |
+| IMDBBINARY    | 73.0      | 75.1 ± 5.1   |
+| IMDBMULTI     | 54.0      | 52.3 ± 2.8   |
+| REDDITBINARY  | 88.0      | 92.4 ± 2.5   |
+| REDDITMULTI5K | 54.8      | 57.5 ± 1.5   |
+

examples/pytorch/gin/ginparser.py

@@ -19,7 +19,7 @@ class Parser():
         # dataset
         self.parser.add_argument(
             '--dataset', type=str, default="MUTAG",
-            choices=['MUTAG', 'COLLAB', 'IMDBBINARY', 'IMDBMULTI'],
+            choices=['MUTAG', 'COLLAB', 'IMDBBINARY', 'IMDBMULTI', 'NCI1', 'PROTEINS', 'PTC', 'REDDITBINARY', 'REDDITMULTI5K'],
             help='name of dataset (default: MUTAG)')
         self.parser.add_argument(
             '--batch_size', type=int, default=32,
@@ -30,6 +30,9 @@ class Parser():
         self.parser.add_argument(
             '--filename', type=str, default="",
             help='output file')
+        self.parser.add_argument(
+            '--degree_as_nlabel', action="store_true",
+            help='use one-hot encodings of node degrees as node feature vectors')
         
         # device
         self.parser.add_argument(

examples/pytorch/gin/main.py

@@ -86,8 +86,7 @@ def main(args):
     else:
         args.device = torch.device("cpu")
 
-    dataset = GINDataset(args.dataset, not args.learn_eps)
-
+    dataset = GINDataset(args.dataset, not args.learn_eps, args.degree_as_nlabel)
     trainloader, validloader = GINDataLoader(
         dataset, batch_size=args.batch_size, device=args.device,
         seed=args.seed, shuffle=True,
@@ -129,11 +128,12 @@ def main(args):
 
         if not args.filename == "":
             with open(args.filename, 'a') as f:
-                f.write('%s %s %s %s' % (
+                f.write('%s %s %s %s %s' % (
                     args.dataset,
                     args.learn_eps,
                     args.neighbor_pooling_type,
-                    args.graph_pooling_type
+                    args.graph_pooling_type,
+                    epoch
                 ))
                 f.write("\n")
                 f.write("%f %f %f %f" % (