add reddit example. (#658)

examples/pytorch/sgc/README.md

@@ -16,6 +16,7 @@ pip install torch requests
 Codes
 -----
 The folder contains an implementation of SGC (`sgc.py`).
+`sgc_reddit.py` contains an example of training SGC on the reddit dataset.
 
 Results
 -------
@@ -26,9 +27,14 @@ python3 sgc.py --dataset cora --gpu 0
 python3 sgc.py --dataset citeseer --weight-decay 5e-5 --n-epochs 150 --bias --gpu 0
 python3 sgc.py --dataset pubmed --weight-decay 5e-5 --bias --gpu 0
 ```
+Run the following command to train on the reddit dataset.
+```bash
+python sgc_reddit.py --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
+* reddit: 0.947 (paper: 0.949), 0.6872s in total

examples/pytorch/sgc/sgc_reddit.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,K=2):
+        super(SGCLayer, self).__init__()
+        self.g = g
+        self.weight = nn.Linear(in_feats, out_feats, bias=True)
+        self.K = K
+        # precomputing message passing
+        start = time.perf_counter()
+        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']
+        h = (h-h.mean(0))/h.std(0)
+        precompute_elapse = time.perf_counter()-start
+        print("Precompute Time(s): {:.4f}".format(precompute_elapse))
+        # 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
+    args.dataset = "reddit-self-loop"
+    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
+    start = time.perf_counter()
+    g = DGLGraph(data.graph)
+    n_edges = g.number_of_edges()
+    # 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)
+    preprocess_elapse = time.perf_counter()-start
+    print("Preprocessing Time: {:.4f}".format(preprocess_elapse))
+
+    # create SGC model
+    model = SGCLayer(g,features,in_feats,n_classes,K=2)
+
+    if cuda: model.cuda()
+    loss_fcn = torch.nn.CrossEntropyLoss()
+
+    # use optimizer
+    optimizer = torch.optim.LBFGS(model.parameters())
+
+    # define loss closure
+    def closure():
+        optimizer.zero_grad()
+        output = model(train_mask)
+        loss_train = F.cross_entropy(output, labels[train_mask])
+        loss_train.backward()
+        return loss_train
+
+    # initialize graph
+    dur = []
+    start = time.perf_counter()
+    for epoch in range(args.n_epochs):
+        model.train()
+        logits = model(train_mask) # only compute the train set
+        loss = optimizer.step(closure)
+
+    train_elapse = time.perf_counter()-start
+    print("Train epoch {} | Train Time(s) {:.4f}".format(epoch, train_elapse))
+    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("--bias", action='store_true', default=False,
+            help="flag to use bias")
+    parser.add_argument("--n-epochs", type=int, default=2,
+            help="number of training epochs")
+    args = parser.parse_args()
+    print(args)
+
+    main(args)