def test_sage_conv():
for aggre_type in ['mean', 'pool', 'gcn']:
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1),
readonly=True)
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.1))
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
dst_dim = 5 if aggre_type != 'gcn' else 10
sage = nn.SAGEConv((10, dst_dim), 2, aggre_type)
feat = (F.randn((100, 10)), F.randn((200, dst_dim)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 200
def test_sage_conv(idtype, g, aggre_type, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
sage = nn.SAGEConv(5, out_dim, aggre_type)
feat = F.randn((g.number_of_src_nodes(), 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
def test_sage_conv_bi2(idtype, aggre_type, out_dim):
# Test the case for graphs without edges
g = dgl.heterograph({('_U', '_E', '_V'): ([], [])}, {'_U': 5, '_V': 3})
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
sage = nn.SAGEConv((3, 3), out_dim, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
sage = nn.SAGEConv((3, 1), out_dim, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == 3
def test_sage_conv_bi2(idtype, aggre_type):
# Test the case for graphs without edges
g = dgl.bipartite([], num_nodes=(5, 3))
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
sage = nn.SAGEConv((3, 3), 2, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
sage = nn.SAGEConv((3, 1), 2, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
def test_sage_conv_bi(idtype, g, aggre_type, out_dim):
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
dst_dim = 5 if aggre_type != 'gcn' else 10
sage = nn.SAGEConv((10, dst_dim), out_dim, aggre_type)
feat = (F.randn((g.number_of_src_nodes(), 10)), F.randn((g.number_of_dst_nodes(), dst_dim)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == out_dim
assert h.shape[0] == g.number_of_dst_nodes()
def test_sage_conv():
g = dgl.DGLGraph(nx.erdos_renyi_graph(20, 0.3))
ctx = F.ctx()
graphsage = nn.SAGEConv(10, 20)
graphsage.initialize(ctx=ctx)
print(graphsage)
# test#1: basic
h0 = F.randn((20, 10))
h1 = graphsage(g, h0)
assert h1.shape == (20, 20)
def test_sage_conv(aggre_type):
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.1))
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
dst_dim = 5 if aggre_type != 'gcn' else 10
sage = nn.SAGEConv((10, dst_dim), 2, aggre_type)
feat = (F.randn((100, 10)), F.randn((200, dst_dim)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 200
# Test the case for graphs without edges
g = dgl.bipartite([], num_nodes=(5, 3))
sage = nn.SAGEConv((3, 3), 2, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
sage = nn.SAGEConv((3, 1), 2, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
def test_dense_sage_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
sage = nn.SAGEConv(5, 2, 'gcn')
dense_sage = nn.DenseSAGEConv(5, 2)
sage.initialize(ctx=ctx)
dense_sage.initialize(ctx=ctx)
dense_sage.fc.weight.set_data(sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_data(sage.fc_neigh.bias.data())
feat = F.randn((100, 5))
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
def test_dense_sage_conv(g):
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
sage = nn.SAGEConv(5, 2, 'gcn')
dense_sage = nn.DenseSAGEConv(5, 2)
sage.initialize(ctx=ctx)
dense_sage.initialize(ctx=ctx)
dense_sage.fc.weight.set_data(sage.fc_neigh.weight.data())
dense_sage.fc.bias.set_data(sage.fc_neigh.bias.data())
if len(g.ntypes) == 2:
feat = (F.randn(
(g.number_of_src_nodes(), 5)), F.randn(
(g.number_of_dst_nodes(), 5)))
else:
feat = F.randn((g.number_of_nodes(), 5))
out_sage = sage(g, feat)
out_dense_sage = dense_sage(adj, feat)
assert F.allclose(out_sage, out_dense_sage)
def test_sage_conv(aggre_type):
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.1), readonly=True)
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.graph(sp.sparse.random(100, 100, density=0.1))
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((100, 5))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 10
g = dgl.bipartite(sp.sparse.random(100, 200, density=0.1))
dst_dim = 5 if aggre_type != 'gcn' else 10
sage = nn.SAGEConv((10, dst_dim), 2, aggre_type)
feat = (F.randn((100, 10)), F.randn((200, dst_dim)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 200
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)
sage = nn.SAGEConv(5, 10, aggre_type)
feat = F.randn((block.number_of_src_nodes(), 5))
sage.initialize(ctx=ctx)
h = sage(block, feat)
assert h.shape[0] == block.number_of_dst_nodes()
assert h.shape[-1] == 10
# Test the case for graphs without edges
g = dgl.bipartite([], num_nodes=(5, 3))
sage = nn.SAGEConv((3, 3), 2, 'gcn')
feat = (F.randn((5, 3)), F.randn((3, 3)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
for aggre_type in ['mean', 'pool']:
sage = nn.SAGEConv((3, 1), 2, aggre_type)
feat = (F.randn((5, 3)), F.randn((3, 1)))
sage.initialize(ctx=ctx)
h = sage(g, feat)
assert h.shape[-1] == 2
assert h.shape[0] == 3
def test_hetero_conv(agg):
g = dgl.heterograph({
('user', 'follows', 'user'): [(0, 1), (0, 2), (2, 1), (1, 3)],
('user', 'plays', 'game'): [(0, 0), (0, 2), (0, 3), (1, 0), (2, 2)],
('store', 'sells', 'game'): [(0, 0), (0, 3), (1, 1), (1, 2)]
})
conv = nn.HeteroGraphConv(
{
'follows': nn.GraphConv(2, 3),
'plays': nn.GraphConv(2, 4),
'sells': nn.GraphConv(3, 4)
}, agg)
conv.initialize(ctx=F.ctx())
print(conv)
uf = F.randn((4, 2))
gf = F.randn((4, 4))
sf = F.randn((2, 3))
uf_dst = F.randn((4, 3))
gf_dst = F.randn((4, 4))
h = conv(g, {'user': uf})
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 1, 4)
h = conv(g, {'user': uf, 'store': sf})
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 2, 4)
h = conv(g, {'store': sf})
assert set(h.keys()) == {'game'}
if agg != 'stack':
assert h['game'].shape == (4, 4)
else:
assert h['game'].shape == (4, 1, 4)
# test with pair input
conv = nn.HeteroGraphConv(
{
'follows': nn.SAGEConv(2, 3, 'mean'),
'plays': nn.SAGEConv((2, 4), 4, 'mean'),
'sells': nn.SAGEConv(3, 4, 'mean')
}, agg)
conv.initialize(ctx=F.ctx())
h = conv(g, ({'user': uf}, {'user': uf, 'game': gf}))
assert set(h.keys()) == {'user', 'game'}
if agg != 'stack':
assert h['user'].shape == (4, 3)
assert h['game'].shape == (4, 4)
else:
assert h['user'].shape == (4, 1, 3)
assert h['game'].shape == (4, 1, 4)
# pair input requires both src and dst type features to be provided
h = conv(g, ({'user': uf}, {'game': gf}))
assert set(h.keys()) == {'game'}
if agg != 'stack':
assert h['game'].shape == (4, 4)
else:
assert h['game'].shape == (4, 1, 4)
# test with mod args
class MyMod(mx.gluon.nn.Block):
def __init__(self, s1, s2):
super(MyMod, self).__init__()
self.carg1 = 0
self.s1 = s1
self.s2 = s2
def forward(self, g, h, arg1=None): # mxnet does not support kwargs
if arg1 is not None:
self.carg1 += 1
return F.zeros((g.number_of_dst_nodes(), self.s2))
mod1 = MyMod(2, 3)
mod2 = MyMod(2, 4)
mod3 = MyMod(3, 4)
conv = nn.HeteroGraphConv({
'follows': mod1,
'plays': mod2,
'sells': mod3
}, agg)
conv.initialize(ctx=F.ctx())
mod_args = {'follows': (1, ), 'plays': (1, )}
h = conv(g, {'user': uf, 'store': sf}, mod_args)
assert mod1.carg1 == 1
assert mod2.carg1 == 1
assert mod3.carg1 == 0