chore: import upstream snapshot with attribution

examples/sparse/gcn.py

@@ -0,0 +1,107 @@
+"""
+[Semi-Supervised Classification with Graph Convolutional Networks]
+(https://arxiv.org/abs/1609.02907)
+"""
+
+import dgl.sparse as dglsp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.data import CoraGraphDataset
+from torch.optim import Adam
+
+
+class GCN(nn.Module):
+    def __init__(self, in_size, out_size, hidden_size=16):
+        super().__init__()
+
+        # Two-layer GCN.
+        self.W1 = nn.Linear(in_size, hidden_size)
+        self.W2 = nn.Linear(hidden_size, out_size)
+
+    ############################################################################
+    # (HIGHLIGHT) Take the advantage of DGL sparse APIs to implement the GCN
+    # forward process.
+    ############################################################################
+    def forward(self, A_norm, X):
+        X = A_norm @ self.W1(X)
+        X = F.relu(X)
+        X = A_norm @ self.W2(X)
+        return X
+
+
+def evaluate(g, pred):
+    label = g.ndata["label"]
+    val_mask = g.ndata["val_mask"]
+    test_mask = g.ndata["test_mask"]
+
+    # Compute accuracy on validation/test set.
+    val_acc = (pred[val_mask] == label[val_mask]).float().mean()
+    test_acc = (pred[test_mask] == label[test_mask]).float().mean()
+    return val_acc, test_acc
+
+
+def train(model, g, A_norm, X):
+    label = g.ndata["label"]
+    train_mask = g.ndata["train_mask"]
+    optimizer = Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
+    loss_fcn = nn.CrossEntropyLoss()
+
+    for epoch in range(200):
+        model.train()
+
+        # Forward.
+        logits = model(A_norm, X)
+
+        # Compute loss with nodes in the training set.
+        loss = loss_fcn(logits[train_mask], label[train_mask])
+
+        # Backward.
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # Compute prediction.
+        pred = logits.argmax(dim=1)
+
+        # Evaluate the prediction.
+        val_acc, test_acc = evaluate(g, pred)
+        if epoch % 20 == 0:
+            print(
+                f"In epoch {epoch}, loss: {loss:.3f}, val acc: {val_acc:.3f}"
+                f", test acc: {test_acc:.3f}"
+            )
+
+
+if __name__ == "__main__":
+    # If CUDA is available, use GPU to accelerate the training, use CPU
+    # otherwise.
+    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    # Load graph from the existing dataset.
+    dataset = CoraGraphDataset()
+    g = dataset[0].to(dev)
+    num_classes = dataset.num_classes
+    X = g.ndata["feat"]
+
+    # Create the adjacency matrix of graph.
+    indices = torch.stack(g.edges())
+    N = g.num_nodes()
+    A = dglsp.spmatrix(indices, shape=(N, N))
+
+    ############################################################################
+    # (HIGHLIGHT) Compute the symmetrically normalized adjacency matrix with
+    # Sparse Matrix API
+    ############################################################################
+    I = dglsp.identity(A.shape, device=dev)
+    A_hat = A + I
+    D_hat = dglsp.diag(A_hat.sum(1)) ** -0.5
+    A_norm = D_hat @ A_hat @ D_hat
+
+    # Create model.
+    in_size = X.shape[1]
+    out_size = num_classes
+    model = GCN(in_size, out_size).to(dev)
+
+    # Kick off training.
+    train(model, g, A_norm, X)