1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
|
import os.path as osp
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(dataset.num_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, x, edge_index):
x = F.relu(self.conv1(x, edge_index, None))
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index, None)
return F.log_softmax(x, dim=1)
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')
model, data = Net().to(device), data.to(device)
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)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
return loss.item()
@torch.no_grad()
def test():
model.eval()
out, accs = model(data.x, data.edge_index), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
pred = out[mask].argmax(1)
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
accs.append(acc)
return accs
model(data.x, data.edge_index)
writer = SummaryWriter()
writer.add_graph(model, [data.x, data.edge_index])
best_val_acc = test_acc = 0
for epoch in range(1, 201):
train_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}, Train: {train_acc:.4f}, '
f'Val: {best_val_acc:.4f}, Test: {test_acc:.4f}')
writer.add_scalar('Loss/train', train_loss, epoch)
writer.add_scalar('Accuracy/train', train_acc, epoch)
writer.add_scalar('Accuracy/val', val_acc, epoch)
writer.add_scalar('Accuracy/test', test_acc, epoch)
|
