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
@pytest.mark.parametrize('g', [
random_dglgraph(20),
random_graph(20),
random_bipartite(20, 10),
random_block(20)
])
def test_edge_conv(g):
ctx = F.ctx()
edge_conv = nn.EdgeConv(5, 2).to(ctx)
print(edge_conv)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
if not g.is_homograph() and not g.is_block:
# bipartite
h1 = edge_conv(g, (h0, h0[:10]))
dense_sage.fc.weight.set_data(sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_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))
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
@pytest.mark.parametrize(
'g', [random_dglgraph(20),
random_graph(20),
random_bipartite(20, 10)])
def test_edge_conv(g):
ctx = F.ctx()
edge_conv = nn.EdgeConv(5, 2)
edge_conv.initialize(ctx=ctx)
print(edge_conv)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
if not g.is_homograph():
# bipartite
h1 = edge_conv(g, (h0, h0[:10]))
else:
sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_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))
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
@pytest.mark.parametrize('g', [random_dglgraph(20), random_graph(20), random_bipartite(20, 10), random_block(20)])
def test_edge_conv(g):
ctx = F.ctx()
edge_conv = nn.EdgeConv(5, 2)
edge_conv.initialize(ctx=ctx)
print(edge_conv)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
if not g.is_homograph() and not g.is_block:
# bipartite
h1 = edge_conv(g, (h0, h0[:10]))
else:
h1 = edge_conv(g, h0)
assert h1.shape == (g.number_of_dst_nodes(), 2)