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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 BatchNorm, Linear, MixHopConv
if torch.cuda.is_available():
device = torch.device('cuda')
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
dataset = Planetoid(path, name='Cora')
data = dataset[0]
class MixHop(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = MixHopConv(dataset.num_features, 60, powers=[0, 1, 2])
self.norm1 = BatchNorm(3 * 60)
self.conv2 = MixHopConv(3 * 60, 60, powers=[0, 1, 2])
self.norm2 = BatchNorm(3 * 60)
self.conv3 = MixHopConv(3 * 60, 60, powers=[0, 1, 2])
self.norm3 = BatchNorm(3 * 60)
self.lin = Linear(3 * 60, dataset.num_classes)
def forward(self, x, edge_index):
x = F.dropout(x, p=0.7, training=self.training)
x = self.conv1(x, edge_index)
x = self.norm1(x)
x = F.dropout(x, p=0.9, training=self.training)
x = self.conv2(x, edge_index)
x = self.norm2(x)
x = F.dropout(x, p=0.9, training=self.training)
x = self.conv3(x, edge_index)
x = self.norm3(x)
x = F.dropout(x, p=0.9, training=self.training)
return self.lin(x)
model, data = MixHop().to(device), data.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5, weight_decay=0.005)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=40,
gamma=0.01)
def train():
model.train()
optimizer.zero_grad()
out = model(data.x, data.edge_index)
loss = F.cross_entropy(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
scheduler.step()
return float(loss)
@torch.no_grad()
def test():
model.eval()
pred = model(data.x, data.edge_index).argmax(dim=-1)
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
for epoch in range(1, 101):
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: {best_val_acc:.4f}, Test: {test_acc:.4f}')
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