-
Notifications
You must be signed in to change notification settings - Fork 8
/
torch_classification_example.py
87 lines (67 loc) · 2.92 KB
/
torch_classification_example.py
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
import argparse
import torch
import torch.nn.functional as F
from torch_geometric.loader import DataLoader
from torch_geometric.logging import log
from torch_geometric.nn import MLP, GINConv, global_add_pool
from torch_loader import GraphClassificationBench
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--hidden_channels', type=int, default=32)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--lr', type=float, default=5e-4)
parser.add_argument('--epochs', type=int, default=100)
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
file_path = "data/"
train_dataset = GraphClassificationBench(file_path, split='train', easy=False, small=False)
train_loader = DataLoader(train_dataset, args.batch_size, shuffle=True)
val_dataset = GraphClassificationBench(file_path, split='val', easy=False, small=False)
val_loader = DataLoader(val_dataset, args.batch_size)
test_dataset = GraphClassificationBench(file_path, split='test', easy=False, small=False)
test_loader = DataLoader(test_dataset, args.batch_size)
class Net(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
super().__init__()
self.convs = torch.nn.ModuleList()
for _ in range(num_layers):
mlp = MLP([in_channels, hidden_channels, hidden_channels])
self.convs.append(GINConv(nn=mlp, train_eps=False))
in_channels = hidden_channels
self.mlp = MLP([hidden_channels, hidden_channels, out_channels],
norm=None, dropout=0.5)
def forward(self, x, edge_index, batch):
for conv in self.convs:
x = conv(x, edge_index).relu()
x = global_add_pool(x, batch)
return self.mlp(x)
model = Net(train_dataset.num_features, args.hidden_channels, train_dataset.num_classes,
args.num_layers).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
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.cross_entropy(out, data.y)
loss.backward()
optimizer.step()
total_loss += float(loss) * data.num_graphs
return total_loss / len(train_loader.dataset)
@torch.no_grad()
def test(loader):
model.eval()
total_correct = 0
for data in loader:
data = data.to(device)
pred = model(data.x, data.edge_index, data.batch).argmax(dim=-1)
total_correct += int((pred == data.y).sum())
return total_correct / len(loader.dataset)
for epoch in range(1, args.epochs + 1):
loss = train()
train_acc = test(train_loader)
val_acc = test(val_loader)
test_acc = test(test_loader)
log(Epoch=epoch, Loss=loss, Train=train_acc, Val=val_acc, Test=test_acc)