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
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
|
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import BatchNorm1d as BatchNorm
from torch.nn import Linear, ReLU, Sequential
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GINConv, global_add_pool
from torch_geometric.transforms import OneHotDegree
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', '..', 'data', 'TU')
dataset = TUDataset(path, name='IMDB-BINARY', transform=OneHotDegree(135))
dataset = dataset.shuffle()
test_dataset = dataset[:len(dataset) // 10]
train_dataset = dataset[len(dataset) // 10:]
test_loader = DataLoader(test_dataset, batch_size=128)
train_loader = DataLoader(train_dataset, batch_size=128)
class GIN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
super().__init__()
self.convs = torch.nn.ModuleList()
self.batch_norms = torch.nn.ModuleList()
for i in range(num_layers):
mlp = Sequential(
Linear(in_channels, 2 * hidden_channels),
BatchNorm(2 * hidden_channels),
ReLU(),
Linear(2 * hidden_channels, hidden_channels),
)
conv = GINConv(mlp, train_eps=True)
self.convs.append(conv)
self.batch_norms.append(BatchNorm(hidden_channels))
in_channels = hidden_channels
self.lin1 = Linear(hidden_channels, hidden_channels)
self.batch_norm1 = BatchNorm(hidden_channels)
self.lin2 = Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, batch):
for conv, batch_norm in zip(self.convs, self.batch_norms):
x = F.relu(batch_norm(conv(x, edge_index)))
x = global_add_pool(x, batch)
x = F.relu(self.batch_norm1(self.lin1(x)))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
return F.log_softmax(x, dim=-1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GIN(dataset.num_features, 64, dataset.num_classes, num_layers=3)
model = model.to(device)
model = torch.jit.script(model)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train():
model.train()
total_loss = 0.
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data.x, data.edge_index, data.batch)
loss = F.nll_loss(out, data.y)
loss.backward()
total_loss += loss.item() * data.num_graphs
optimizer.step()
return total_loss / len(train_dataset)
@torch.no_grad()
def test(loader):
model.eval()
total_correct = 0
for data in loader:
data = data.to(device)
out = model(data.x, data.edge_index, data.batch)
pred = out.max(dim=1)[1]
total_correct += pred.eq(data.y).sum().item()
return total_correct / len(loader.dataset)
for epoch in range(1, 101):
loss = train()
train_acc = test(train_loader)
test_acc = test(test_loader)
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, '
f'Train: {train_acc:.4f}, Test: {test_acc:.4f}')
|
