def test_gmm_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((100, 5))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.1), readonly=True)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((100, 5))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
g = dgl.bipartite(sp.sparse.random(100, 50, density=0.1), readonly=True)
gmmconv = nn.GMMConv((5, 2), 10, 3, 4, 'mean')
feat = F.randn((100, 5))
feat_dst = F.randn((50, 2))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, (feat, feat_dst), pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
def test_gmm_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((g.number_of_nodes(), 5))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
def test_gmm_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((100, 5))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.1), readonly=True)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((100, 5))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
g = dgl.bipartite(sp.sparse.random(100, 50, density=0.1), readonly=True)
gmmconv = nn.GMMConv((5, 2), 10, 3, 4, 'mean')
feat = F.randn((100, 5))
feat_dst = F.randn((50, 2))
pseudo = F.randn((g.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(g, (feat, feat_dst), pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.001))
seed_nodes = th.unique(g.edges()[1])
block = dgl.to_block(g, seed_nodes)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((block.number_of_src_nodes(), 5))
pseudo = F.randn((block.number_of_edges(), 3))
gmmconv = gmmconv.to(ctx)
h = gmmconv(block, feat, pseudo)
assert h.shape[0] == block.number_of_dst_nodes()
assert h.shape[-1] == 10
def test_gmm_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
gmmconv = nn.GMMConv(5, 10, 3, 4, 'mean')
feat = F.randn((100, 5))
pseudo = F.randn((g.number_of_edges(), 3))
if F.gpu_ctx():
gmmconv = gmmconv.to(ctx)
feat = feat.to(ctx)
pseudo = pseudo.to(ctx)
h = gmmconv(g, feat, pseudo)
# currently we only do shape check
assert h.shape[-1] == 10
