gat_pyg.py

from torch import nn
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import degree, remove_self_loops, add_self_loops
from torch_geometric.nn import GATConv


class GATNet(nn.Module):
    def __init__(self, dataset):
        super(GATNet, self).__init__()
        self.conv1 = GATConv(dataset.num_features, 8, heads=8, dropout=0.6)
        self.conv2 = GATConv(8*8, dataset.num_classes, dropout=0.6)

    def forward(self, data):
        x = F.dropout(data.x, p=0.6, training=self.training)
        x = F.elu(self.conv1(x, data.edge_index))
        x = F.dropout(x, p=0.6, training=self.training)
        x = self.conv2(x, data.edge_index)
        return F.log_softmax(x, dim=1)