def test_gmm_conv():
ctx = F.ctx()
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_nodes(), 2)
g = dgl.graph(nx.erdos_renyi_graph(20, 0.3))
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_nodes(), 2)
g = dgl.bipartite(sp.sparse.random(20, 10, 0.1))
gmm_conv = nn.GMMConv((5, 4), 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
hd = F.randn((g.number_of_dst_nodes(), 4))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, (h0, hd), pseudo)
assert h1.shape == (g.number_of_dst_nodes(), 2)
def test_gmm_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
h0 = F.randn((g.number_of_src_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_dst_nodes(), 2)
def test_gmm_conv():
ctx = F.ctx()
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_nodes(), 2)
g = dgl.graph(nx.erdos_renyi_graph(20, 0.3))
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_nodes(), 2)
g = dgl.bipartite(sp.sparse.random(20, 10, 0.1))
gmm_conv = nn.GMMConv((5, 4), 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
# test #1: basic
h0 = F.randn((g.number_of_src_nodes(), 5))
hd = F.randn((g.number_of_dst_nodes(), 4))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, (h0, hd), pseudo)
assert h1.shape == (g.number_of_dst_nodes(), 2)
g = dgl.graph(sp.sparse.random(100, 100, density=0.001))
seed_nodes = np.unique(g.edges()[1].asnumpy())
block = dgl.to_block(g, seed_nodes)
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'mean')
gmm_conv.initialize(ctx=ctx)
h0 = F.randn((block.number_of_src_nodes(), 5))
pseudo = F.randn((block.number_of_edges(), 5))
h = gmm_conv(block, h0, pseudo)
assert h.shape[0] == block.number_of_dst_nodes()
assert h.shape[-1] == 2
def test_gmm_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
gmm_conv = nn.GMMConv(5, 2, 5, 3, 'max')
gmm_conv.initialize(ctx=ctx)
print(gmm_conv)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 5))
h1 = gmm_conv(g, h0, pseudo)
assert h1.shape == (g.number_of_nodes(), 2)
