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

Before Change

def train_runtime(Net, data, epochs, device):
    g = DGLGraph(data.graph)
    g.set_n_initializer(dgl.init.zero_initializer)
    x = torch.tensor(data.features, dtype=torch.float, device=device)
    mask = torch.tensor(data.train_mask, dtype=torch.uint8, device=device)
    y = torch.tensor(data.labels, dtype=torch.long, device=device)[mask]
    g.add_edges(g.nodes(), g.nodes())
    norm = torch.pow(g.in_degrees().float(), -0.5)
    norm[torch.isinf(norm)] = 0
    g.ndata["norm"] = norm.unsqueeze(1).to(device)

    model = Net(g, x.size(1), data.num_labels).to(device)

After Change

def train_runtime(model, data, epochs, device):
    if hasattr(data, "features"):
        x = torch.tensor(data.features, dtype=torch.float, device=device)
    else:
        x = None
    mask = data.train_mask if hasattr(data, "train_mask") else data.train_idx
    y = torch.tensor(data.labels, dtype=torch.long, device=device)[mask]

    model = model.to(device)

Instances