def test_cf_conv(g, idtype):
g = g.astype(idtype).to(F.ctx())
cfconv = nn.CFConv(node_in_feats=2,
edge_in_feats=3,
hidden_feats=2,
out_feats=3)
ctx = F.ctx()
if F.gpu_ctx():
cfconv = cfconv.to(ctx)
node_feats = F.randn((g.number_of_nodes(), 2))
edge_feats = F.randn((g.number_of_edges(), 3))
h = cfconv(g, node_feats, edge_feats)
# current we only do shape check
assert h.shape[-1] == 3
def test_cf_conv():
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
cfconv = nn.CFConv(node_in_feats=2,
edge_in_feats=3,
hidden_feats=2,
out_feats=3)
ctx = F.ctx()
if F.gpu_ctx():
cfconv = cfconv.to(ctx)
node_feats = F.randn((100, 2))
edge_feats = F.randn((g.number_of_edges(), 3))
h = cfconv(g, node_feats, edge_feats)
# current we only do shape check
assert h.shape[-1] == 3
def test_cf_conv(g, idtype, out_dim):
g = g.astype(idtype).to(F.ctx())
cfconv = nn.CFConv(node_in_feats=2,
edge_in_feats=3,
hidden_feats=2,
out_feats=out_dim)
ctx = F.ctx()
if F.gpu_ctx():
cfconv = cfconv.to(ctx)
src_feats = F.randn((g.number_of_src_nodes(), 2))
edge_feats = F.randn((g.number_of_edges(), 3))
h = cfconv(g, src_feats, edge_feats)
# current we only do shape check
assert h.shape[-1] == out_dim
# case for bipartite graphs
dst_feats = F.randn((g.number_of_dst_nodes(), 3))
h = cfconv(g, (src_feats, dst_feats), edge_feats)
# current we only do shape check
assert h.shape[-1] == out_dim
