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dgi model 3f92768 Apr 19, 2019
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import os.path as osp
import torch
import torch.nn as nn
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCNConv, DeepGraphInfomax
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
dataset = Planetoid(path, dataset)
class Encoder(nn.Module):
def __init__(self, in_channels, hidden_channels):
super(Encoder, self).__init__()
self.conv = GCNConv(in_channels, hidden_channels, cached=True)
self.prelu = nn.PReLU(hidden_channels)
def forward(self, x, edge_index):
x = self.conv(x, edge_index)
x = self.prelu(x)
return x
def corruption(x, edge_index):
return x[torch.randperm(x.size(0))], edge_index
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = DeepGraphInfomax(
hidden_channels=512,
encoder=Encoder(dataset.num_features, 512),
summary=lambda z, *args, **kwargs: z.mean(dim=0),
corruption=corruption).to(device)
data = dataset[0].to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train():
model.train()
optimizer.zero_grad()
pos_z, neg_z, summary = model(data.x, data.edge_index)
loss = model.loss(pos_z, neg_z, summary)
loss.backward()
optimizer.step()
return loss.item()
def test():
model.eval()
z, _, _ = model(data.x, data.edge_index)
acc = model.test(
z[data.train_mask],
data.y[data.train_mask],
z[data.test_mask],
data.y[data.test_mask],
max_iter=150)
return acc
for epoch in range(1, 301):
loss = train()
print('Epoch: {:03d}, Loss: {:.4f}'.format(epoch, loss))
acc = test()
print('Accuracy: {:.4f}'.format(acc))
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