def forward(self, graph_list, node_feat, edge_feat):
n2e_sp, e2e_sp, e2n_sp, subg_sp = S2VLIB.PrepareLoopyBP(graph_list)
if type(node_feat) is torch.cuda.FloatTensor:
n2e_sp = n2e_sp.cuda()
e2e_sp = e2e_sp.cuda()
e2n_sp = e2n_sp.cuda()
subg_sp = subg_sp.cuda()
node_feat = Variable(node_feat)
edge_feat = Variable(edge_feat)
n2e_sp = Variable(n2e_sp)
e2e_sp = Variable(e2e_sp)
e2n_sp = Variable(e2n_sp)
subg_sp = Variable(subg_sp)
h = self.loopy_bp(node_feat, edge_feat, n2e_sp, e2e_sp, e2n_sp, subg_sp)
return h
def forward(self, graph_list, node_feat, edge_feat):
n2e_sp, e2e_sp, e2n_sp, subg_sp = S2VLIB.PrepareLoopyBP(graph_list)
if is_cuda_float(node_feat):
n2e_sp = n2e_sp.cuda()
e2e_sp = e2e_sp.cuda()
e2n_sp = e2n_sp.cuda()
subg_sp = subg_sp.cuda()
node_feat = Variable(node_feat)
if edge_feat is not None:
edge_feat = Variable(edge_feat)
n2e_sp = Variable(n2e_sp)
e2e_sp = Variable(e2e_sp)
e2n_sp = Variable(e2n_sp)
subg_sp = Variable(subg_sp)
h = self.loopy_bp(node_feat, edge_feat, n2e_sp, e2e_sp, e2n_sp,
subg_sp)
return h