def test_dense_sage_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
adj = g.adjacency_matrix(ctx=ctx).to_dense()
sage = nn.SAGEConv(5, 2, 'gcn')
dense_sage = nn.DenseSAGEConv(5, 2)
dense_sage.fc.weight.data = sage.fc_neigh.weight.data
dense_sage.fc.bias.data = sage.fc_neigh.bias.data
feat = F.randn((100, 5))
sage = sage.to(ctx)
dense_sage = dense_sage.to(ctx)
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
def test_dense_sage_conv(g):
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx).to_dense()
sage = nn.SAGEConv(5, 2, 'gcn')
dense_sage = nn.DenseSAGEConv(5, 2)
dense_sage.fc.weight.data = sage.fc_neigh.weight.data
dense_sage.fc.bias.data = sage.fc_neigh.bias.data
if len(g.ntypes) == 2:
feat = (F.randn(
(g.number_of_src_nodes(), 5)), F.randn(
(g.number_of_dst_nodes(), 5)))
else:
feat = F.randn((g.number_of_nodes(), 5))
sage = sage.to(ctx)
dense_sage = dense_sage.to(ctx)
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage), g
