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import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
from torch_geometric.transforms import NormalizeFeatures
if torch.cuda.device_count() < 2:
quit('This example requires multiple GPUs')
path = osp.dirname(osp.realpath(__file__))
path = osp.join(path, '..', '..', 'data', 'Planetoid')
dataset = Planetoid(root=path, name='Cora', transform=NormalizeFeatures())
data = dataset[0].to('cuda:0')
class GCN(torch.nn.Module):
def __init__(self, in_channels, out_channels, device1, device2):
super().__init__()
self.device1 = device1
self.device2 = device2
self.conv1 = GCNConv(in_channels, 16).to(device1)
self.conv2 = GCNConv(16, out_channels).to(device2)
def forward(self, x, edge_index):
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv1(x, edge_index).relu()
# Move data to the second device:
x, edge_index = x.to(self.device2), edge_index.to(self.device2)
x = F.dropout(x, p=0.5, training=self.training)
x = self.conv2(x, edge_index)
return x
model = GCN(
dataset.num_features,
dataset.num_classes,
device1='cuda:0',
device2='cuda:1',
)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
def train():
model.train()
optimizer.zero_grad()
out = model(data.x, data.edge_index).to('cuda:0')
loss = F.cross_entropy(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
return float(loss)
@torch.no_grad()
def test():
model.eval()
out = model(data.x, data.edge_index)
pred = out.argmax(dim=-1).to('cuda:0')
accs = []
for mask in [data.train_mask, data.val_mask, data.test_mask]:
accs.append(int((pred[mask] == data.y[mask]).sum()) / int(mask.sum()))
return accs
best_val_acc = test_acc = 0
times = []
for epoch in range(1, 201):
loss = train()
train_acc, val_acc, tmp_test_acc = test()
if val_acc > best_val_acc:
best_val_acc = val_acc
test_acc = tmp_test_acc
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, '
f'Val: {val_acc:.4f}, Test: {test_acc:.4f}')
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