db28ee240981457335c6fd9c38e542066df214cb,examples/cluster_gcn.py,,test,#,59

Before Change

    logits = model(data.x, data.edge_index)
    accs = []
    for _, mask in data("train_mask", "val_mask", "test_mask"):
        pred = logits[mask].max(1)[1]
        acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
        accs.append(acc)
    return accs

After Change

def test():
    model.eval()
    total_correct, total_nodes = [0, 0, 0], [0, 0, 0]
    for data in test_loader:
        data = data.to(device)
        logits = model(data.x, data.edge_index)
        pred = logits.argmax(dim=1)

        masks = [data.train_mask, data.val_mask, data.test_mask]
        for i, mask in enumerate(masks):
            total_correct[i] += (pred[mask] == data.y[mask]).sum().item()
            total_nodes[i] += mask.sum().item()

    return (torch.Tensor(total_correct) / torch.Tensor(total_nodes)).tolist()


for epoch in range(1, 31):

Instances