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import os.path as osp
import torch
import torch.nn.functional as F
from tqdm import tqdm
from torch_geometric.datasets import Reddit
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn.models.basic_gnn import GraphSAGE
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Reddit')
dataset = Reddit(path)
# Already send node features/labels to GPU for faster access during sampling:
data = dataset[0].to(device, 'x', 'y')
kwargs = {'batch_size': 1024, 'num_workers': 6, 'persistent_workers': True}
loader = NeighborLoader(data, input_nodes=data.train_mask,
num_neighbors=[20, 10, 5], shuffle=True, **kwargs)
model = GraphSAGE(
dataset.num_features,
hidden_channels=64,
out_channels=dataset.num_classes,
num_layers=3,
).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train(trim=False):
for batch in tqdm(loader):
optimizer.zero_grad()
batch = batch.to(device)
if not trim:
out = model(batch.x, batch.edge_index)
else:
out = model(
batch.x,
batch.edge_index,
num_sampled_nodes_per_hop=batch.num_sampled_nodes,
num_sampled_edges_per_hop=batch.num_sampled_edges,
)
out = out[:batch.batch_size]
y = batch.y[:batch.batch_size]
loss = F.cross_entropy(out, y)
loss.backward()
optimizer.step()
print('One epoch training without Hierarchical Graph Sampling:')
train(trim=False)
print('One epoch training with Hierarchical Graph Sampling:')
train(trim=True)
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