详解Pytorch+PyG实现GCN过程示例

from torch_geometric.datasets import Planetoid
import torch_geometric.transforms as T
dataset = Planetoid(root='/path/to/dataset', name='Cora', transform=T.NormalizeFeatures())
data = dataset[0]
print(data)

import torch.nn.functional as F
from torch_geometric.nn import GCNConv
class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(dataset.num_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)
    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = F.relu(x)
        x = F.dropout(x, training=self.training)
        x = self.conv2(x, edge_index)
        return F.log_softmax(x, dim=1)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
def train():
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)
    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()
def test():
    model.eval()
    _, pred = model(data.x, data.edge_index).max(dim=1)
    correct = int(pred[data.test_mask].eq(data.y[data.test_mask]).sum().item())
    acc = correct / int(data.test_mask.sum())
    return acc
for epoch in range(1, 201):
    train()
    test_acc = test()
    print(f'Epoch: {epoch:03d}, Test Acc: {test_acc:.4f}')