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