[Model] reformat and update performance of grace

examples/pytorch/grace/README.md

@@ -32,25 +32,47 @@ This example was implemented by [Hengrui Zhang](https://github.com/hengruizhang9
 --dataname         str     The graph dataset name.                Default is 'cora'.
 --gpu              int     GPU index.                             Default is 0.
 --split            int     Dataset spliting method.               Default is 'random'.
+--epochs           int     Number of training periods.            Default is 500.
+--lr               float   Learning rate.                         Default is 0.001.
+--wd               float   Weight decay.                          Default is 1e-5.
+--temp             float   Temperature.                           Default is 1.0.
+--act_fn           str     Activation function.                   Default is relu.001
+--hid_dim          int     Hidden dimension.                      Default is 256.
+--out_dim          int     Output dimension.                      Default is 256.
+--num_layers       int     Number of GNN layers.                  Default is 2.
+--der1             float   Drop edge ratio 1.                     Default is 0.2. 
+--der2             float   Drop edge ratio 2.                     Default is 0.2. 
+--dfr1             float   Drop feature ratio 1.                  Default is 0.2. 
+--dfr2             float   Drop feature ratio 2.                  Default is 0.2. 
 ```
 
 ## How to run examples
 
-In the paper(as well as authors' repo), the training set and testing set are split randomly with 1:9 ratio. In order to fairly compare it with other models with the split they used, termed public split, in this repo we also provide experiment results using public split. To run the examples,
+In the paper(as well as authors' repo), the training set and testing set are split randomly with 1:9 ratio.  In order to fairly compare it with other methods with public split (20 training nodes each class), in this repo we also provide its results using public split (with fine-tuned hyper-parameters). To run the examples, follow the following instructions.
 
 ```python
 # Cora with random split
-python main.py --dataname cora
+python main.py --dataname cora --epochs 200 --lr 5e-4 --wd 1e-5 --hid_dim 128 --out_dim 128 --act_fn relu --der1 0.2 --der2 0.4 --dfr1 0.3 --dfr2 0.4 --temp 0.4
 
 # Cora with public split
-python main.py --dataname cora --split public
-```
+python main.py --dataname cora --split public --epochs 400 --lr 5e-4 --wd 1e-5 --hid_dim 256 --out_dim 256 --act_fn relu --der1 0.3 --der2 0.4 --dfr1 0.3 --dfr2 0.4 --temp 0.4
+
+# Citeseer with random split
+python main.py --dataname citeseer --epochs 200 --lr 1e-3 --wd 1e-5 --hid_dim 256 --out_dim 256 --act_fn prelu --der1 0.2 --der2 0.0 --dfr1 0.3 --dfr2 0.2 --temp 0.9
+
+# Citeseer with public split
+python main.py --dataname citeseer --split public --epochs 100 --lr 1e-3 --wd 1e-5 --hid_dim 512 --out_dim 512 --act_fn prelu --der1 0.3 --der2 0.3 --dfr1 0.3 --dfr2 0.3 --temp 0.4
 
-replace 'cora' with 'citeseer' or 'pubmed' if you would like to run this example for other datasets.
+# Pubmed with random split
+python main.py --dataname pubmed --epochs 1500 --lr 1e-3 --wd 1e-5 --hid_dim 256 --out_dim 256 --act_fn relu --der1 0.4 --der2 0.1 --dfr1 0.0 --dfr2 0.2 --temp 0.7
+
+# Pubmed with public split
+python main.py --dataname pubmed --split public --epochs 1500 --lr 1e-3 --wd 1e-5 --hid_dim 256 --out_dim 256 --act_fn relu --der1 0.4 --der2 0.1 --dfr1 0.0 --dfr2 0.2 --temp 0.7
+```
 
 ## 	Performance
 
-We use the same hyperparameter settings as provided by the author, you can check config.yaml for detailed hyper-parameters for each dataset.
+For random split, we use the hyper-parameters as stated in the paper. For public split,  we find the given hyper-parameters lead to poor performance, so we select the hyperparameters via  a small grid search.
 
 Random split (Train/Test = 1:9)
 
@@ -64,6 +86,6 @@ Public split
 
 |    Dataset    | Cora | Citeseer | Pubmed |
 | :-----------: | :--: | :------: | :----: |
-| Author's Code | 79.9 |   68.6   |  81.3  |
-|      DGL      | 80.1 |   68.9   |  81.2  |
+| Author's Code | 81.9 |   71.2   |  80.6  |
+|      DGL      | 82.2 |   71.4   |  80.2  |
 

examples/pytorch/grace/main.py

@@ -1,65 +1,81 @@
 import argparse
-
 from model import Grace
 from aug import aug
 from dataset import load
 
+import numpy as np
 import torch as th
 import torch.nn as nn
 
-import yaml
-from yaml import SafeLoader
-
 from eval import label_classification
 import warnings
 
 warnings.filterwarnings('ignore')
 
+
+def count_parameters(model):
+    return sum([np.prod(p.size()) for p in model.parameters() if p.requires_grad])
+
+
 parser = argparse.ArgumentParser()
-parser.add_argument('--dataname', type=str, default='cora', choices = ['cora', 'citeseer', 'pubmed'])
+parser.add_argument('--dataname', type=str, default='cora')
 parser.add_argument('--gpu', type=int, default=0)
-parser.add_argument('--split', type=str, default='random', choices = ['random', 'public'])
+parser.add_argument('--split', type=str, default='random')
+
+parser.add_argument('--epochs', type=int, default=500, help='Number of training periods.')
+parser.add_argument('--lr', type=float, default=0.001, help='Learning rate.')
+parser.add_argument('--wd', type=float, default=1e-5, help='Weight decay.')
+parser.add_argument('--temp', type=float, default=1.0, help='Temperature.')
+
+parser.add_argument('--act_fn', type=str, default='relu')
+
+parser.add_argument("--hid_dim", type=int, default=256, help='Hidden layer dim.')
+parser.add_argument("--out_dim", type=int, default=256, help='Output layer dim.')
+
+parser.add_argument("--num_layers", type=int, default=2, help='Number of GNN layers.')
+parser.add_argument('--der1', type=float, default=0.2, help='Drop edge ratio of the 1st augmentation.')
+parser.add_argument('--der2', type=float, default=0.2, help='Drop edge ratio of the 2nd augmentation.')
+parser.add_argument('--dfr1', type=float, default=0.2, help='Drop feature ratio of the 1st augmentation.')
+parser.add_argument('--dfr2', type=float, default=0.2, help='Drop feature ratio of the 2nd augmentation.')
+
 args = parser.parse_args()
 
 if args.gpu != -1 and th.cuda.is_available():
     args.device = 'cuda:{}'.format(args.gpu)
 else:
     args.device = 'cpu'
 
-
 if __name__ == '__main__':
 
     # Step 1: Load hyperparameters =================================================================== #
-    config = 'config.yaml'
-    config = yaml.load(open(config), Loader=SafeLoader)[args.dataname]
-    lr = config['learning_rate']
-    hid_dim = config['num_hidden']
-    out_dim = config['num_proj_hidden']
+    lr = args.lr
+    hid_dim = args.hid_dim
+    out_dim = args.out_dim
 
-    num_layers = config['num_layers']
-    act_fn = ({'relu': nn.ReLU(), 'prelu': nn.PReLU()})[config['activation']]
+    num_layers = args.num_layers
+    act_fn = ({'relu': nn.ReLU(), 'prelu': nn.PReLU()})[args.act_fn]
 
-    drop_edge_rate_1 = config['drop_edge_rate_1']
-    drop_edge_rate_2 = config['drop_edge_rate_2']
-    drop_feature_rate_1 = config['drop_feature_rate_1']
-    drop_feature_rate_2 = config['drop_feature_rate_2']
+    drop_edge_rate_1 = args.der1
+    drop_edge_rate_2 = args.der2
+    drop_feature_rate_1 = args.dfr1
+    drop_feature_rate_2 = args.dfr2
 
-    temp = config['tau']
-    epochs = config['num_epochs']
-    wd = config['weight_decay']
+    temp = args.temp
+    epochs = args.epochs
+    wd = args.wd
 
-    # Step 2: Prepare data =================================================================== #
+    # Step 2: Prepare data =================================================================== =#
     graph, feat, labels, train_mask, test_mask = load(args.dataname)
-
     in_dim = feat.shape[1]
 
     # Step 3: Create model =================================================================== #
     model = Grace(in_dim, hid_dim, out_dim, num_layers, act_fn, temp)
     model = model.to(args.device)
+    print(f'# params: {count_parameters(model)}')
 
     optimizer = th.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
 
-    # Step 4: Training ======================================================================= #
+    # Step 4: Training =======================================================================
     for epoch in range(epochs):
         model.train()
         optimizer.zero_grad()
@@ -79,11 +95,12 @@
         print(f'Epoch={epoch:03d}, loss={loss.item():.4f}')
 
     # Step 5: Linear evaluation ============================================================== #
-    print("=== Final Evaluation ===")
+    print("=== Final ===")
+
     graph = graph.add_self_loop()
     graph = graph.to(args.device)
     feat = feat.to(args.device)
     embeds = model.get_embedding(graph, feat)
 
     '''Evaluation Embeddings  '''
-    label_classification(embeds, labels, train_mask, test_mask, split=args.split, ratio=0.1)
+    label_classification(embeds, labels, train_mask, test_mask, split=args.split)

examples/pytorch/grace/model.py

@@ -72,7 +72,7 @@ def sim(self, z1, z2):
 
     def get_loss(self, z1, z2):
         # calculate SimCLR loss
-
+        
         f = lambda x: th.exp(x / self.temp)
 
         refl_sim = f(self.sim(z1, z1))        # intra-view pairs