def test_cheb_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
cheb = nn.ChebConv(10, 20, 3) # k = 3
cheb.initialize(ctx=ctx)
print(cheb)
# test#1: basic
h0 = F.randn((20, 10))
h1 = cheb(g, h0)
assert h1.shape == (20, 20)
def test_cheb_conv(out_dim):
g = dgl.from_networkx(nx.erdos_renyi_graph(20, 0.3)).to(F.ctx())
ctx = F.ctx()
cheb = nn.ChebConv(10, out_dim, 3) # k = 3
cheb.initialize(ctx=ctx)
print(cheb)
# test#1: basic
h0 = F.randn((20, 10))
h1 = cheb(g, h0)
assert h1.shape == (20, out_dim)
def test_dense_cheb_conv():
for k in range(1, 4):
ctx = F.ctx()
g = dgl.from_scipy(sp.sparse.random(100, 100, density=0.3)).to(F.ctx())
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
cheb = nn.ChebConv(5, 2, k)
dense_cheb = nn.DenseChebConv(5, 2, k)
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)
