DGCN-PyTorch/wd_gc.py

import torch
import torch.nn as nn

class WD_GCN(nn.Module):
    def __init__(self, A, X, edges, hidden_feat=[2, 2]):
        super(WD_GCN, self).__init__()
        self.device = 1
        self.A = A
        self.X = X
        self.T, self.N = X.shape[0], X.shape[1]    # T = number of nodes, N = number of node features
        self.v = torch.cuda.FloatTensor([self.N, 1])
        self.edge_src_nodes = torch.matmul(edges[[0, 1]].transpose(1, 0).float(), self.v).cuda()
        self.edge_trg_nodes = torch.matmul(edges[[0, 2]].transpose(1, 0).float(), self.v).cuda()
        self.tanh = torch.nn.Tanh()
        self.sigmoid = torch.nn.Sigmoid()
        self.relu = torch.nn.ReLU(inplace=False)
        self.AX = self.compute_AX(A, X)

        # GCN parameters
        self.W = nn.Parameter(torch.randn(X.shape[-1], hidden_feat[0]).cuda())

        # Edge classification/link prediction parameters
        self.U = torch.randn(2*hidden_feat[0], hidden_feat[1]).cuda()


        # LSTM parameters
        self.Wf = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Wj = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Wc = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Wo = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Uf = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Uj = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Uc = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.Uo = nn.Parameter(torch.randn(hidden_feat[0], hidden_feat[0]).cuda())
        self.bf = nn.Parameter(torch.randn(hidden_feat[0]).cuda())
        self.bj = nn.Parameter(torch.randn(hidden_feat[0]).cuda())
        self.bc = nn.Parameter(torch.randn(hidden_feat[0]).cuda())
        self.bo = nn.Parameter(torch.randn(hidden_feat[0]).cuda())
        self.h_init = torch.randn(hidden_feat[0]).cuda()
        self.c_init = torch.randn(hidden_feat[0]).cuda()


    def __call__(self, A=None, X=None, edges=None):
        return self.forward(A, X, edges)

    def forward(self, A=None, X=None, edges=None):
        if type(A) == list:
            AX = self.compute_AX(A, X)
            edge_src_nodes = torch.matmul(edges[[0, 1]].transpose(1, 0).float(), self.v)
            edge_trg_nodes = torch.matmul(edges[[0, 2]].transpose(1, 0).float(), self.v)
        else:
            AX = self.AX
            edge_src_nodes = self.edge_src_nodes
            edge_trg_nodes = self.edge_trg_nodes

        Y = self.relu(torch.matmul(AX.cuda(), self.W.cuda()))
        Z = self.LSTM(Y)
        Z_mat_edge_src_nodes = Z.reshape(-1, Z.shape[-1])[edge_src_nodes.long()]
        Z_mat_edge_trg_nodes = Z.reshape(-1, Z.shape[-1])[edge_trg_nodes.long()]
        Z_mat = torch.cat((Z_mat_edge_src_nodes, Z_mat_edge_trg_nodes), dim=1).cuda()
        output = torch.matmul(Z_mat, self.U)    # this is for prediction head

        return output

    def compute_AX(self, A, X):
        AX = torch.zeros(self.T, self.N, X.shape[-1]).cuda()
        for k in range(len(A)):
            AX[k] = torch.sparse.mm(A[k].cuda(), X[k])

        return AX

    def LSTM(self, Y):
        c = self.c_init.repeat(self.N, 1)
        h = self.h_init.repeat(self.N, 1)
        Z = torch.zeros(Y.shape)
        for time in range(Y.shape[0]):
            f = self.sigmoid(torch.matmul(Y[time], self.Wf) + torch.matmul(h, self.Uf) + self.bf.repeat(self.N, 1))
            j = self.sigmoid(torch.matmul(Y[time], self.Wj) + torch.matmul(h, self.Uj) + self.bj.repeat(self.N, 1))
            o = self.sigmoid(torch.matmul(Y[time], self.Wo) + torch.matmul(h, self.Uo) + self.bo.repeat(self.N, 1))
            ct = self.sigmoid(torch.matmul(Y[time], self.Wc) + torch.matmul(h, self.Uc) + self.bc.repeat(self.N, 1))
            c = j * ct + f * c
            h = o * self.tanh(c)
            Z[time] = h

        return Z