def test_dense_cheb_conv():
for k in range(1, 4):
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
adj = g.adjacency_matrix(ctx=ctx).to_dense()
cheb = nn.ChebConv(5, 2, k)
dense_cheb = nn.DenseChebConv(5, 2, k)
for i in range(len(cheb.fc)):
dense_cheb.W.data[i] = cheb.fc[i].weight.data.t()
if cheb.bias is not None:
dense_cheb.bias.data = cheb.bias.data
feat = F.randn((100, 5))
cheb = cheb.to(ctx)
dense_cheb = dense_cheb.to(ctx)
out_cheb = cheb(g, feat, [2.0])
out_dense_cheb = dense_cheb(adj, feat, 2.0)
assert F.allclose(out_cheb, out_dense_cheb)
def test_dense_cheb_conv():
for k in range(1, 4):
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
g = g.to(F.ctx())
adj = g.adjacency_matrix(transpose=False, ctx=ctx).to_dense()
cheb = nn.ChebConv(5, 2, k, None)
dense_cheb = nn.DenseChebConv(5, 2, k)
#for i in range(len(cheb.fc)):
# dense_cheb.W.data[i] = cheb.fc[i].weight.data.t()
dense_cheb.W.data = cheb.linear.weight.data.transpose(-1, -2).view(k, 5, 2)
if cheb.linear.bias is not None:
dense_cheb.bias.data = cheb.linear.bias.data
feat = F.randn((100, 5))
cheb = cheb.to(ctx)
dense_cheb = dense_cheb.to(ctx)
out_cheb = cheb(g, feat, [2.0])
out_dense_cheb = dense_cheb(adj, feat, 2.0)
print(k, out_cheb, out_dense_cheb)
assert F.allclose(out_cheb, out_dense_cheb)
