wehub-resource-sync
95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
"""
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[Simplifying Graph Convolutional Networks]
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(https://arxiv.org/abs/1902.07153)
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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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################################################################################
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# (HIGHLIGHT) Take the advantage of DGL sparse APIs to implement the feature
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# pre-computation.
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################################################################################
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def pre_compute(A, X, k):
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for _ in range(k):
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X = A @ 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, X_sgc):
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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=2e-1, weight_decay=5e-6)
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for epoch in range(20):
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# Forward.
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logits = model(X_sgc)
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# Compute loss with nodes in the training set.
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loss = F.cross_entropy(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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print(
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f"In epoch {epoch}, loss: {loss:.3f}, val acc: {val_acc:.3f}, test"
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f" 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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# Create the sparse adjacency matrix A
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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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# Calculate the symmetrically normalized adjacency matrix.
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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(dim=1)) ** -0.5
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A_hat = D_hat @ A_hat @ D_hat
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# 2-hop diffusion.
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k = 2
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X = g.ndata["feat"]
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X_sgc = pre_compute(A_hat, X, k)
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# Create model.
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in_size = X.shape[1]
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out_size = dataset.num_classes
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model = nn.Linear(in_size, out_size).to(dev)
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# Kick off training.
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train(model, g, X_sgc)
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