def test_gin_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
gin_conv = nn.GINConv(lambda x: x, 'mean', 0.1)
gin_conv.initialize(ctx=ctx)
print(gin_conv)
# test #1: basic
feat = F.randn((g.number_of_nodes(), 5))
h = gin_conv(g, feat)
assert h.shape == (20, 5)
# test #2: bipartite
g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
feat = (F.randn((100, 5)), F.randn((200, 5)))
h = gin_conv(g, feat)
return h.shape == (20, 5)
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)
feat = F.randn((block.number_of_src_nodes(), 5))
h = gin_conv(block, feat)
assert h.shape == (block.number_of_dst_nodes(), 12)
def test_gin_conv_bi(g, idtype, aggregator_type):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gin_conv = nn.GINConv(lambda x: x, aggregator_type, 0.1)
gin_conv.initialize(ctx=ctx)
print(gin_conv)
# test #2: bipartite
feat = (F.randn((g.number_of_src_nodes(), 5)), F.randn((g.number_of_dst_nodes(), 5)))
h = gin_conv(g, feat)
return h.shape == (g.number_of_dst_nodes(), 5)
def test_gin_conv(g, idtype, aggregator_type):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
gin_conv = nn.GINConv(lambda x: x, aggregator_type, 0.1)
gin_conv.initialize(ctx=ctx)
print(gin_conv)
# test #1: basic
feat = F.randn((g.number_of_src_nodes(), 5))
h = gin_conv(g, feat)
assert h.shape == (g.number_of_dst_nodes(), 5)
def test_gin_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
gin_conv = nn.GINConv(lambda x: x, 'mean', 0.1)
gin_conv.initialize(ctx=ctx)
print(gin_conv)
# test #1: basic
h0 = F.randn((g.number_of_nodes(), 5))
h1 = gin_conv(g, h0)
assert h1.shape == (g.number_of_nodes(), 5)
def test_gin_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
gin_conv = nn.GINConv(lambda x: x, 'mean', 0.1)
gin_conv.initialize(ctx=ctx)
print(gin_conv)
# test #1: basic
feat = F.randn((g.number_of_nodes(), 5))
h = gin_conv(g, feat)
assert h.shape == (20, 5)
# test #2: bipartite
g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
feat = (F.randn((100, 5)), F.randn((200, 5)))
h = gin_conv(g, feat)
return h.shape == (20, 5)
