def test_glt_ogbnproducts()

in benchmarks/api/bench_sampler.py [0:0]

• 28 lines of code
• 3 McCabe index (conditional complexity)

def test_glt_ogbnproducts(mode='GPU'):
  if mode == 'GPU':
    graph_mode = 'CUDA'
  else:
    graph_mode = 'ZERO_COPY'
  root = osp.join(osp.dirname(osp.dirname(osp.realpath(__file__))),
                  '..', 'data', 'products')
  dataset = PygNodePropPredDataset('ogbn-products', root)
  train_idx = dataset.get_idx_split()["train"]
  train_loader = torch.utils.data.DataLoader(train_idx,
                                             batch_size=1024,
                                             pin_memory=True,
                                             shuffle=True)
  csr_topo = glt.data.Topology(dataset[0].edge_index)
  g = glt.data.Graph(csr_topo, graph_mode, device=0)
  device = torch.device('cuda:0')
  sampler = glt.sampler.NeighborSampler(g, [15, 10, 5], device=device)
  total_time = 0
  sampled_edges = 0
  for seeds in train_loader:
    seeds = seeds.to(0)
    torch.cuda.synchronize()
    start = time.time()
    row = sampler.sample_from_nodes(seeds).row
    torch.cuda.synchronize()
    total_time += time.time() - start
    sampled_edges += row.shape[0]
  print('Sampled Edges per secs: {} M'.format(sampled_edges / total_time / 1000000))