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import argparse
import torch
import torch.nn.functional as F
from tqdm import tqdm
import torch_geometric
import torch_geometric.transforms as T
from torch_geometric.datasets import OGB_MAG
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import HeteroConv, Linear, SAGEConv
from torch_geometric.utils import trim_to_layer
parser = argparse.ArgumentParser()
parser.add_argument('--use-sparse-tensor', action='store_true')
args = parser.parse_args()
if torch.cuda.is_available():
device = torch.device('cuda')
elif torch_geometric.is_xpu_available():
device = torch.device('xpu')
else:
device = torch.device('cpu')
transforms = [T.ToUndirected(merge=True)]
if args.use_sparse_tensor:
transforms.append(T.ToSparseTensor())
dataset = OGB_MAG(root='../../data', preprocess='metapath2vec',
transform=T.Compose(transforms))
data = dataset[0].to(device, 'x', 'y')
class HierarchicalHeteroGraphSage(torch.nn.Module):
def __init__(self, edge_types, hidden_channels, out_channels, num_layers):
super().__init__()
self.convs = torch.nn.ModuleList()
for _ in range(num_layers):
conv = HeteroConv(
{
edge_type: SAGEConv((-1, -1), hidden_channels)
for edge_type in edge_types
}, aggr='sum')
self.convs.append(conv)
self.lin = Linear(hidden_channels, out_channels)
def forward(self, x_dict, edge_index_dict, num_sampled_edges_dict,
num_sampled_nodes_dict):
for i, conv in enumerate(self.convs):
x_dict, edge_index_dict, _ = trim_to_layer(
layer=i,
num_sampled_nodes_per_hop=num_sampled_nodes_dict,
num_sampled_edges_per_hop=num_sampled_edges_dict,
x=x_dict,
edge_index=edge_index_dict,
)
x_dict = conv(x_dict, edge_index_dict)
x_dict = {key: x.relu() for key, x in x_dict.items()}
return self.lin(x_dict['paper'])
model = HierarchicalHeteroGraphSage(
edge_types=data.edge_types,
hidden_channels=64,
out_channels=dataset.num_classes,
num_layers=2,
).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
kwargs = {'batch_size': 1024, 'num_workers': 0}
train_loader = NeighborLoader(
data,
num_neighbors=[10] * 2,
shuffle=True,
input_nodes=('paper', data['paper'].train_mask),
**kwargs,
)
val_loader = NeighborLoader(
data,
num_neighbors=[10] * 2,
shuffle=False,
input_nodes=('paper', data['paper'].val_mask),
**kwargs,
)
def train():
model.train()
total_examples = total_loss = 0
for batch in tqdm(train_loader):
batch = batch.to(device)
optimizer.zero_grad()
out = model(
batch.x_dict,
batch.adj_t_dict
if args.use_sparse_tensor else batch.edge_index_dict,
num_sampled_nodes_dict=batch.num_sampled_nodes_dict,
num_sampled_edges_dict=batch.num_sampled_edges_dict,
)
batch_size = batch['paper'].batch_size
loss = F.cross_entropy(out[:batch_size], batch['paper'].y[:batch_size])
loss.backward()
optimizer.step()
total_examples += batch_size
total_loss += float(loss) * batch_size
return total_loss / total_examples
@torch.no_grad()
def test(loader):
model.eval()
total_examples = total_correct = 0
for batch in tqdm(loader):
batch = batch.to(device)
out = model(
batch.x_dict,
batch.adj_t_dict
if args.use_sparse_tensor else batch.edge_index_dict,
num_sampled_nodes_dict=batch.num_sampled_nodes_dict,
num_sampled_edges_dict=batch.num_sampled_edges_dict,
)
batch_size = batch['paper'].batch_size
pred = out[:batch_size].argmax(dim=-1)
total_examples += batch_size
total_correct += int((pred == batch['paper'].y[:batch_size]).sum())
return total_correct / total_examples
for epoch in range(1, 6):
loss = train()
val_acc = test(val_loader)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_acc:.4f}')
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