def test_graph_conv(idtype, out_dim):
g = dgl.from_networkx(nx.path_graph(3))
g = g.astype(idtype).to(F.ctx())
ctx = F.ctx()
adj = g.adjacency_matrix(transpose=True, ctx=ctx)
conv = nn.GraphConv(5, out_dim, norm='none', bias=True)
conv.initialize(ctx=ctx)
# test#1: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
check_close(h1, _AXWb(adj, h0, conv.weight, conv.bias))
# test#2: more-dim
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
check_close(h1, _AXWb(adj, h0, conv.weight, conv.bias))
conv = nn.GraphConv(5, out_dim)
conv.initialize(ctx=ctx)
# test#3: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test#4: basic
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
conv = nn.GraphConv(5, out_dim)
conv.initialize(ctx=ctx)
with autograd.train_mode():
# test#3: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test#4: basic
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test not override features
g.ndata["h"] = 2 * F.ones((3, 1))
h1 = conv(g, h0)
assert len(g.ndata) == 1
assert len(g.edata) == 0
assert "h" in g.ndata
check_close(g.ndata['h'], 2 * F.ones((3, 1)))
def test_graph_conv():
g = dgl.DGLGraph(nx.path_graph(3))
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx)
conv = nn.GraphConv(5, 2, norm=False, bias=True)
conv.initialize(ctx=ctx)
# test#1: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
check_close(h1, _AXWb(adj, h0, conv.weight, conv.bias))
# test#2: more-dim
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
check_close(h1, _AXWb(adj, h0, conv.weight, conv.bias))
conv = nn.GraphConv(5, 2)
conv.initialize(ctx=ctx)
# test#3: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test#4: basic
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
conv = nn.GraphConv(5, 2)
conv.initialize(ctx=ctx)
with autograd.train_mode():
# test#3: basic
h0 = F.ones((3, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test#4: basic
h0 = F.ones((3, 5, 5))
h1 = conv(g, h0)
assert len(g.ndata) == 0
assert len(g.edata) == 0
# test not override features
g.ndata["h"] = 2 * F.ones((3, 1))
h1 = conv(g, h0)
assert len(g.ndata) == 1
assert len(g.edata) == 0
assert "h" in g.ndata
check_close(g.ndata['h'], 2 * F.ones((3, 1)))
def test_graph_conv():
g = dgl.DGLGraph(nx.path_graph(3))
adj = g.adjacency_matrix()
ctx = mx.cpu(0)
conv = nn.GraphConv(5, 2, norm=False, bias=True)
conv.initialize(ctx=ctx)
# test#1: basic
h0 = mx.nd.ones((3, 5))
h1 = conv(h0, g)
check_eq(h1, _AXWb(adj, h0, conv.weight, conv.bias))
# test#2: more-dim
h0 = mx.nd.ones((3, 5, 5))
h1 = conv(h0, g)
check_eq(h1, _AXWb(adj, h0, conv.weight, conv.bias))
conv = nn.GraphConv(5, 2)
conv.initialize(ctx=ctx)
# test#3: basic
h0 = mx.nd.ones((3, 5))
h1 = conv(h0, g)
# test#4: basic
h0 = mx.nd.ones((3, 5, 5))
h1 = conv(h0, g)
conv = nn.GraphConv(5, 2)
conv.initialize(ctx=ctx)
with autograd.train_mode():
# test#3: basic
h0 = mx.nd.ones((3, 5))
h1 = conv(h0, g)
# test#4: basic
h0 = mx.nd.ones((3, 5, 5))
h1 = conv(h0, g)
# test repeated features
g.ndata["_gconv_feat"] = 2 * mx.nd.ones((3, 1))
h1 = conv(h0, g)
assert "_gconv_feat" in g.ndata
def test_graph_conv2(idtype, g, norm, weight, bias):
g = g.astype(idtype).to(F.ctx())
conv = nn.GraphConv(5, 2, norm=norm, weight=weight, bias=bias)
conv.initialize(ctx=F.ctx())
ext_w = F.randn((5, 2)).as_in_context(F.ctx())
nsrc = ndst = g.number_of_nodes()
h = F.randn((nsrc, 5)).as_in_context(F.ctx())
if weight:
h_out = conv(g, h)
else:
h_out = conv(g, h, ext_w)
assert h_out.shape == (ndst, 2)
def test_dense_graph_conv(g, norm_type):
ctx = F.ctx()
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
conv = nn.GraphConv(5, 2, norm=norm_type, bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=norm_type, bias=True)
conv.initialize(ctx=ctx)
dense_conv.initialize(ctx=ctx)
dense_conv.weight.set_data(conv.weight.data())
dense_conv.bias.set_data(conv.bias.data())
feat = F.randn((g.number_of_src_nodes(), 5))
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
def test_graph_conv2_bi(idtype, g, norm, weight, bias, out_dim):
g = g.astype(idtype).to(F.ctx())
conv = nn.GraphConv(5, out_dim, norm=norm, weight=weight, bias=bias)
conv.initialize(ctx=F.ctx())
ext_w = F.randn((5, out_dim)).as_in_context(F.ctx())
nsrc = g.number_of_src_nodes()
ndst = g.number_of_dst_nodes()
h = F.randn((nsrc, 5)).as_in_context(F.ctx())
h_dst = F.randn((ndst, out_dim)).as_in_context(F.ctx())
if weight:
h_out = conv(g, (h, h_dst))
else:
h_out = conv(g, (h, h_dst), ext_w)
assert h_out.shape == (ndst, out_dim)
def test_graph_conv2(g, norm, weight, bias):
conv = nn.GraphConv(5, 2, norm=norm, weight=weight, bias=bias)
conv.initialize(ctx=F.ctx())
ext_w = F.randn((5, 2)).as_in_context(F.ctx())
nsrc = g.number_of_nodes() if isinstance(
g, dgl.DGLGraph) else g.number_of_src_nodes()
ndst = g.number_of_nodes() if isinstance(
g, dgl.DGLGraph) else g.number_of_dst_nodes()
h = F.randn((nsrc, 5)).as_in_context(F.ctx())
if weight:
h = conv(g, h)
else:
h = conv(g, h, ext_w)
assert h.shape == (ndst, 2)
def test_dense_graph_conv():
ctx = F.ctx()
g = dgl.DGLGraph(sp.sparse.random(100, 100, density=0.3), readonly=True)
adj = g.adjacency_matrix(ctx=ctx).tostype('default')
conv = nn.GraphConv(5, 2, norm='none', bias=True)
dense_conv = nn.DenseGraphConv(5, 2, norm=False, bias=True)
conv.initialize(ctx=ctx)
dense_conv.initialize(ctx=ctx)
dense_conv.weight.set_data(conv.weight.data())
dense_conv.bias.set_data(conv.bias.data())
feat = F.randn((100, 5))
out_conv = conv(g, feat)
out_dense_conv = dense_conv(adj, feat)
assert F.allclose(out_conv, out_dense_conv)
def test_graph_conv2(g, norm, weight, bias):
conv = nn.GraphConv(5, 2, norm=norm, weight=weight, bias=bias)
conv.initialize(ctx=F.ctx())
ext_w = F.randn((5, 2)).as_in_context(F.ctx())
nsrc = g.number_of_nodes() if isinstance(g, dgl.DGLGraph) else g.number_of_src_nodes()
ndst = g.number_of_nodes() if isinstance(g, dgl.DGLGraph) else g.number_of_dst_nodes()
h = F.randn((nsrc, 5)).as_in_context(F.ctx())
h_dst = F.randn((ndst, 2)).as_in_context(F.ctx())
if weight:
h_out = conv(g, h)
else:
h_out = conv(g, h, ext_w)
assert h_out.shape == (ndst, 2)
if not isinstance(g, dgl.DGLGraph) and len(g.ntypes) == 2:
# bipartite, should also accept pair of tensors
if weight:
h_out2 = conv(g, (h, h_dst))
else:
h_out2 = conv(g, (h, h_dst), ext_w)
assert h_out2.shape == (ndst, 2)
assert F.array_equal(h_out, h_out2)
def test_hetero_conv(agg, idtype):
g = dgl.heterograph({
('user', 'follows', 'user'): ([0, 0, 2, 1], [1, 2, 1, 3]),
('user', 'plays', 'game'): ([0, 0, 0, 1, 2], [0, 2, 3, 0, 2]),
('store', 'sells', 'game'): ([0, 0, 1, 1], [0, 3, 1, 2])},
idtype=idtype, device=F.ctx())
conv = nn.HeteroGraphConv({
'follows': nn.GraphConv(2, 3, allow_zero_in_degree=True),
'plays': nn.GraphConv(2, 4, allow_zero_in_degree=True),
'sells': nn.GraphConv(3, 4, allow_zero_in_degree=True)},
agg)
conv.initialize(ctx=F.ctx())
print(conv)
uf = F.randn((4, 2))
gf = F.randn((4, 4))
sf = F.randn((2, 3))
h = conv(g, {'user': uf, 'store': sf, '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, 2, 4)
block = dgl.to_block(g.to(F.cpu()), {'user': [0, 1, 2, 3], 'game': [0, 1, 2, 3], 'store': []}).to(F.ctx())
h = conv(block, ({'user': uf, 'game': gf, 'store': sf}, {'user': uf, 'game': gf, 'store': sf[0:0]}))
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(block, {'user': uf, 'game': gf, '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)
# 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, 'game': gf}, mod_args)
assert mod1.carg1 == 1
assert mod2.carg1 == 1
assert mod3.carg1 == 0
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
