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from torch_geometric.datasets import Planetoid |
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import torch |
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import torch.nn.functional as F |
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from torch_geometric.nn import MessagePassing |
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from torch_geometric.utils import add_self_loops, degree |
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dataset = Planetoid(root='/tmp/Cora', name='Cora') |
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class GraphConvolution(MessagePassing): |
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def __init__(self, in_channels, out_channels,bias=True, **kwargs): |
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super(GraphConvolution, self).__init__(aggr='add', **kwargs) |
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self.lin = torch.nn.Linear(in_channels, out_channels,bias=bias) |
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def forward(self, x, edge_index): |
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edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0)) |
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x = self.lin(x) |
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return self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x) |
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def message(self, x_j, edge_index, size): |
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row, col = edge_index |
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deg = degree(row, size[0], dtype=x_j.dtype) |
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deg_inv_sqrt = deg.pow(-0.5) |
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norm = deg_inv_sqrt[row] * deg_inv_sqrt[col] |
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return norm.view(-1, 1) * x_j |
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def update(self, aggr_out): |
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return aggr_out |
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class Net(torch.nn.Module): |
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def __init__(self,nfeat, nhid, nclass, dropout): |
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super(Net, self).__init__() |
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self.conv1 = GraphConvolution(nfeat, nhid) |
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self.conv2 = GraphConvolution(nhid, nclass) |
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self.dropout=dropout |
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def forward(self, data): |
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x, edge_index = data.x, data.edge_index |
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x = self.conv1(x, edge_index) |
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x = F.relu(x) |
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x = F.dropout(x, self.dropout, training=self.training) |
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x = self.conv2(x, edge_index) |
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return F.log_softmax(x, dim=1) |
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nfeat=dataset.num_node_features |
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nhid=16 |
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nclass=dataset.num_classes |
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dropout=0.5 |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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model = Net(nfeat, nhid, nclass, dropout).to(device) |
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data = dataset[0].to(device) |
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optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) |
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model.train() |
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for epoch in range(200): |
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optimizer.zero_grad() |
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out = model(data) |
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loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask]) |
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loss.backward() |
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optimizer.step() |
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model.eval() |
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_, pred = model(data).max(dim=1) |
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correct = float (pred[data.test_mask].eq(data.y[data.test_mask]).sum().item()) |
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acc = correct / data.test_mask.sum().item() |
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print('Accuracy: {:.4f}'.format(acc)) |
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