cf1cc19bb79ae4128ef5437279de475835374a46,benchmark/runtime/dgl/train.py,,train_runtime,#Any#Any#Any#Any#,9

Before Change

    norm[torch.isinf(norm)] = 0
    g.ndata["norm"] = norm.unsqueeze(1).to(device)

    model = Net(g, x.size(1), data.num_labels).to(device)
    model.train()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

    if torch.cuda.is_available():

After Change

    for epoch in range(epochs):
        optimizer.zero_grad()
        out = model(x)
        loss = F.nll_loss(out[mask], y.view(-1))
        loss.backward()
        optimizer.step()

Instances