def test_dense_graph_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()
conv = nn.GraphConv(5, 2, norm=False, bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=False, bias=True)
dense_conv.weight.data = conv.weight.data
dense_conv.bias.data = conv.bias.data
feat = F.randn((100, 5))
conv = conv.to(ctx)
dense_conv = dense_conv.to(ctx)
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
def test_dense_graph_conv(norm_type, g):
ctx = F.ctx()
# TODO(minjie): enable the following option after #1385
adj = g.adjacency_matrix(ctx=ctx).to_dense()
conv = nn.GraphConv(5, 2, norm=norm_type, bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=norm_type, bias=True)
dense_conv.weight.data = conv.weight.data
dense_conv.bias.data = conv.bias.data
feat = F.randn((g.number_of_src_nodes(), 5))
conv = conv.to(ctx)
dense_conv = dense_conv.to(ctx)
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
