[Model] Simplifying Graph Convolutional Networks. (#405)

* [Model] Simplifying Graph Convolutional Networks.

* - improve code stype (use nn.Linear)
- use `bias` option
- update hyperparameter and corresponding performance

README.md

@@ -16,6 +16,7 @@ A summary of the model accuracy and training speed with the Pytorch backend (on
 | Model | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
 | ----- | ----------------- | ------------ | ------------------------------------ | ---------------------- | ----------- |
 | [GCN](https://arxiv.org/abs/1609.02907)   | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0042s | 1.17x |
+| [SGC](https://arxiv.org/abs/1902.07153) | 81.0% | 81.9% | n/a | 0.0008s | n/a |
 | [TreeLSTM](http://arxiv.org/abs/1503.00075) | 51.0% | 51.72% | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s | 4.3x |
 | [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0273s | 10.4x |
 | [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.633s | 3.5x |

examples/pytorch/README.md

@@ -12,7 +12,8 @@ Here is a summary of the model accuracy and training speed. Our testbed is Amazo
 
 | Model | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time) | DGL speed (epoch time) | Improvement |
 | ----- | ----------------- | ------------ | ------------------------------------ | ---------------------- | ----------- |
-| [GCN](https://arxiv.org/abs/1609.02907)   | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0042s | 1.17x |
+| [GCN](https://arxiv.org/abs/1609.02907)  | 81.5% | 81.0% | [0.0051s (TF)](https://github.com/tkipf/gcn) | 0.0042s | 1.17x |
+| [SGC](https://arxiv.org/abs/1902.07153) | 81.0% | 81.9% | n/a | 0.0008s | n/a |
 | [TreeLSTM](http://arxiv.org/abs/1503.00075) | 51.0% | 51.72% | [14.02s (DyNet)](https://github.com/clab/dynet/tree/master/examples/treelstm) | 3.18s | 4.3x |
 | [R-GCN <br> (classification)](https://arxiv.org/abs/1703.06103) | 73.23% | 73.53% | [0.2853s (Theano)](https://github.com/tkipf/relational-gcn) | 0.0273s | 10.4x |
 | [R-GCN <br> (link prediction)](https://arxiv.org/abs/1703.06103) | 0.158 | 0.151 | [2.204s (TF)](https://github.com/MichSchli/RelationPrediction) | 0.633s | 3.5x |

examples/pytorch/sgc/README.md

+Simple Graph Convolution (SGC)
+============
+
+- Paper link: [Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153)
+- Author's code repo: [https://github.com/Tiiiger/SGC](https://github.com/Tiiiger/SGC). 
+
+Dependencies
+------------
+- PyTorch 0.4.1+
+- requests
+
+``bash
+pip install torch requests
+``
+
+Codes
+-----
+The folder contains an implementation of SGC (`sgc.py`).
+
+Results
+-------
+
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+python sgc.py --dataset cora --gpu 0
+python sgc.py --dataset citeseer --weight-decay 5e-5 --n-epochs 150 --bias --gpu 0
+python sgc.py --dataset pubmed --weight-decay 5e-5 --bias --gpu 0
+```
+
+On NVIDIA V100
+
+* cora: 0.819 (paper: 0.810), 0.0008s/epoch
+* citeseer: 0.725 (paper: 0.719), 0.0008s/epoch
+* pubmed: 0.788 (paper: 0.789), 0.0007s/epoch

examples/pytorch/sgc/sgc.py

+"""
+This code was modified from the GCN implementation in DGL examples.
+Simplifying Graph Convolutional Networks
+Paper: https://arxiv.org/abs/1902.07153
+Code: https://github.com/Tiiiger/SGC
+SGC implementation in DGL.
+"""
+import argparse, time, math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import dgl.function as fn
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+
+class SGCLayer(nn.Module):
+    def __init__(self,
+                 g,
+                 h,
+                 in_feats,
+                 out_feats,
+                 bias=False,
+                 K=2):
+        super(SGCLayer, self).__init__()
+        self.g = g
+        self.weight = nn.Linear(in_feats, out_feats, bias=bias)
+        self.K = K
+        # precomputing message passing
+        for _ in range(self.K):
+            # normalization by square root of src degree
+            h = h * self.g.ndata['norm']
+            self.g.ndata['h'] = h
+            self.g.update_all(fn.copy_src(src='h', out='m'),
+                            fn.sum(msg='m', out='h'))
+            h = self.g.ndata.pop('h')
+            # normalization by square root of dst degree
+            h = h * self.g.ndata['norm']
+        # store precomputed result into a cached variable
+        self.cached_h = h
+
+    def forward(self, mask):
+        h = self.weight(self.cached_h[mask])
+        return h
+
+def evaluate(model, features, labels, mask):
+    model.eval()
+    with torch.no_grad():
+        logits = model(mask) # only compute the evaluation set
+        labels = labels[mask]
+        _, indices = torch.max(logits, dim=1)
+        correct = torch.sum(indices == labels)
+        return correct.item() * 1.0 / len(labels)
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+    features = torch.FloatTensor(data.features)
+    labels = torch.LongTensor(data.labels)
+    train_mask = torch.ByteTensor(data.train_mask)
+    val_mask = torch.ByteTensor(data.val_mask)
+    test_mask = torch.ByteTensor(data.test_mask)
+    in_feats = features.shape[1]
+    n_classes = data.num_labels
+    n_edges = data.graph.number_of_edges()
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+              train_mask.sum().item(),
+              val_mask.sum().item(),
+              test_mask.sum().item()))
+
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        torch.cuda.set_device(args.gpu)
+        features = features.cuda()
+        labels = labels.cuda()
+        train_mask = train_mask.cuda()
+        val_mask = val_mask.cuda()
+        test_mask = test_mask.cuda()
+
+    # graph preprocess and calculate normalization factor
+    g = DGLGraph(data.graph)
+    n_edges = g.number_of_edges()
+    # add self loop
+    g.add_edges(g.nodes(), g.nodes())
+    # normalization
+    degs = g.in_degrees().float()
+    norm = torch.pow(degs, -0.5)
+    norm[torch.isinf(norm)] = 0
+    if cuda:
+        norm = norm.cuda()
+    g.ndata['norm'] = norm.unsqueeze(1)
+
+    # create SGC model
+    model = SGCLayer(g,
+                     features,
+                     in_feats,
+                     n_classes,
+                     args.bias,
+                     K=2)
+
+    if cuda: model.cuda()
+    loss_fcn = torch.nn.CrossEntropyLoss()
+
+    # use optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=args.lr,
+                                 weight_decay=args.weight_decay)
+
+    # initialize graph
+    dur = []
+    for epoch in range(args.n_epochs):
+        model.train()
+        if epoch >= 3:
+            t0 = time.time()
+        # forward
+        logits = model(train_mask) # only compute the train set
+        loss = loss_fcn(logits, labels[train_mask])
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if epoch >= 3:
+            dur.append(time.time() - t0)
+
+        acc = evaluate(model, features, labels, val_mask)
+        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
+              "ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
+                                             acc, n_edges / np.mean(dur) / 1000))
+
+    print()
+    acc = evaluate(model, features, labels, test_mask)
+    print("Test Accuracy {:.4f}".format(acc))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='SGC')
+    register_data_args(parser)
+    parser.add_argument("--gpu", type=int, default=-1,
+            help="gpu")
+    parser.add_argument("--lr", type=float, default=0.2,
+            help="learning rate")
+    parser.add_argument("--bias", action='store_true', default=False,
+            help="flag to use bias")
+    parser.add_argument("--n-epochs", type=int, default=100,
+            help="number of training epochs")
+    parser.add_argument("--weight-decay", type=float, default=5e-6,
+            help="Weight for L2 loss")
+    args = parser.parse_args()
+    print(args)
+
+    main(args)