models/gnn.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GINConv, GATConv, SAGEConv, global_mean_pool


class GCN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super(GCN, self).__init__()
        self.conv1 = GCNConv(in_channels, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, hidden_channels)
        self.lin = nn.Linear(hidden_channels, out_channels)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)
        return self.lin(x)


class GIN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super(GIN, self).__init__()
        nn1 = nn.Sequential(
            nn.Linear(in_channels, hidden_channels),
            nn.ReLU(),
            nn.Linear(hidden_channels, hidden_channels),
        )
        nn2 = nn.Sequential(
            nn.Linear(hidden_channels, hidden_channels),
            nn.ReLU(),
            nn.Linear(hidden_channels, hidden_channels),
        )
        self.conv1 = GINConv(nn1)
        self.conv2 = GINConv(nn2)
        self.lin = nn.Linear(hidden_channels, out_channels)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)
        return self.lin(x)


class GAT(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, heads=4):
        super(GAT, self).__init__()
        self.conv1 = GATConv(in_channels, hidden_channels, heads=heads)
        self.conv2 = GATConv(hidden_channels * heads, hidden_channels, heads=1)
        self.lin = nn.Linear(hidden_channels, out_channels)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = F.elu(self.conv1(x, edge_index))
        x = F.elu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)
        return self.lin(x)


class GraphSAGE(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels):
        super(GraphSAGE, self).__init__()
        self.conv1 = SAGEConv(in_channels, hidden_channels)
        self.conv2 = SAGEConv(hidden_channels, hidden_channels)
        self.lin = nn.Linear(hidden_channels, out_channels)

    def forward(self, data):
        x, edge_index, batch = data.x, data.edge_index, data.batch
        x = F.relu(self.conv1(x, edge_index))
        x = F.relu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)
        return self.lin(x)