cheb.initialize(ctx=ctx)
dense_cheb.initialize(ctx=ctx)
for i in range(len(cheb.fc)):
dense_cheb.fc[i].weight.set_data(cheb.fc[i].weight.data())
if cheb.bias is not None:
dense_cheb.bias.set_data(cheb.bias.data())
feat = F.randn((100, 5))
out_cheb = cheb(g, feat, [2.0])
out_dense_cheb = dense_cheb(adj, feat, 2.0)
assert F.allclose(out_cheb, out_dense_cheb)
@pytest.mark.parametrize('norm_type', ['both', 'right', 'none'])
@pytest.mark.parametrize('g', [random_graph(100), random_bipartite(100, 200)])
def test_dense_graph_conv(g, norm_type):
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
conv = nn.GraphConv(5, 2, norm=norm_type, bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=norm_type, bias=True)
conv.initialize(ctx=ctx)
dense_conv.initialize(ctx=ctx)
dense_conv.weight.set_data(conv.weight.data())
dense_conv.bias.set_data(conv.bias.data())
feat = F.randn((g.number_of_src_nodes(), 5))
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
dense_cheb.initialize(ctx=ctx)
for i in range(len(cheb.fc)):
dense_cheb.fc[i].weight.set_data(
cheb.fc[i].weight.data())
if cheb.bias is not None:
dense_cheb.bias.set_data(
cheb.bias.data())
feat = F.randn((100, 5))
out_cheb = cheb(g, feat, [2.0])
out_dense_cheb = dense_cheb(adj, feat, 2.0)
assert F.allclose(out_cheb, out_dense_cheb)
@pytest.mark.parametrize('norm_type', ['both', 'right', 'none'])
@pytest.mark.parametrize('g', [random_graph(100), random_bipartite(100, 200)])
def test_dense_graph_conv(g, norm_type):
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
conv = nn.GraphConv(5, 2, norm=norm_type, bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=norm_type, bias=True)
conv.initialize(ctx=ctx)
dense_conv.initialize(ctx=ctx)
dense_conv.weight.set_data(
conv.weight.data())
dense_conv.bias.set_data(
conv.bias.data())
feat = F.randn((g.number_of_src_nodes(), 5))
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)