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
|
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Linear, ReLU, Sequential
import torch_geometric.transforms as T
from torch_geometric.datasets import MNISTSuperpixels
from torch_geometric.loader import DataListLoader
from torch_geometric.nn import (
DataParallel,
NNConv,
SplineConv,
global_mean_pool,
)
from torch_geometric.typing import WITH_TORCH_SPLINE_CONV
path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data', 'MNIST')
dataset = MNISTSuperpixels(path, transform=T.Cartesian()).shuffle()
loader = DataListLoader(dataset, batch_size=1024, shuffle=True)
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
if WITH_TORCH_SPLINE_CONV:
self.conv1 = SplineConv(dataset.num_features, 32, dim=2,
kernel_size=5)
self.conv2 = SplineConv(32, 64, dim=2, kernel_size=5)
else:
nn1 = Sequential(Linear(2, 25), ReLU(),
Linear(25, dataset.num_features * 32))
self.conv1 = NNConv(dataset.num_features, 32, nn1, aggr='mean')
nn2 = Sequential(Linear(2, 25), ReLU(), Linear(25, 32 * 64))
self.conv2 = NNConv(32, 64, nn2, aggr='mean')
self.lin1 = torch.nn.Linear(64, 128)
self.lin2 = torch.nn.Linear(128, dataset.num_classes)
def forward(self, data):
print(f'Inside model - num graphs: {data.num_graphs}, '
f'device: {data.batch.device}')
x, edge_index, edge_attr = data.x, data.edge_index, data.edge_attr
x = F.elu(self.conv1(x, edge_index, edge_attr))
x = F.elu(self.conv2(x, edge_index, edge_attr))
x = global_mean_pool(x, data.batch)
x = F.elu(self.lin1(x))
return F.log_softmax(self.lin2(x), dim=1)
model = Net()
print(f"Let's use {torch.cuda.device_count()} GPUs!")
model = DataParallel(model)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for data_list in loader:
optimizer.zero_grad()
output = model(data_list)
print(f'Outside model - num graphs: {output.size(0)}')
y = torch.cat([data.y for data in data_list]).to(output.device)
loss = F.nll_loss(output, y)
loss.backward()
optimizer.step()
|
