is_sorted=True)
adj_t = adj_t.to_symmetric()
torch.save(adj_t, path)
adj_t = gcn_norm(adj_t, add_self_loops=False)
if args.low_memory:
adj_t = adj_t.to(torch.half)
print(f'Done! [{time.perf_counter() - t:.2f}s]')
train_idx = dataset.get_idx_split('train')
valid_idx = dataset.get_idx_split('valid')
test_idx = dataset.get_idx_split('test')
y_train = torch.from_numpy(dataset.paper_label[train_idx]).to(torch.long)
y_valid = torch.from_numpy(dataset.paper_label[valid_idx]).to(torch.long)
model = LabelPropagation(args.num_layers, args.alpha)
N, C = dataset.num_papers, dataset.num_classes
t = time.perf_counter()
print('Propagating labels...', end=' ', flush=True)
if args.low_memory:
y = torch.zeros(N, C, dtype=torch.half)
y[train_idx] = F.one_hot(y_train, C).to(torch.half)
out = model(y, adj_t, post_step=lambda x: x)
y_pred = out.argmax(dim=-1)
else:
y = torch.zeros(N, C)
y[train_idx] = F.one_hot(y_train, C).to(torch.float)
out = model(y, adj_t)
y_pred = out.argmax(dim=-1)
from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
import torch_geometric.transforms as T
from torch_geometric.nn import LabelPropagation
root = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'OGB')
dataset = PygNodePropPredDataset('ogbn-arxiv',
root,
transform=T.Compose([
T.ToUndirected(),
T.ToSparseTensor(),
]))
split_idx = dataset.get_idx_split()
evaluator = Evaluator(name='ogbn-arxiv')
data = dataset[0]
model = LabelPropagation(num_layers=3, alpha=0.9)
out = model(data.y, data.adj_t, mask=split_idx['train'])
y_pred = out.argmax(dim=-1, keepdim=True)
val_acc = evaluator.eval({
'y_true': data.y[split_idx['valid']],
'y_pred': y_pred[split_idx['valid']],
})['acc']
test_acc = evaluator.eval({
'y_true': data.y[split_idx['test']],
'y_pred': y_pred[split_idx['test']],
})['acc']
print(f'Val: {val_acc:.4f}, Test: {test_acc:.4f}')