def test_signed_conv():
in_channels, out_channels = (16, 32)
pos_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
neg_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = pos_ei.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = SignedConv(in_channels, out_channels, first_aggr=True)
assert conv.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
out1 = conv(x, pos_ei, neg_ei)
assert out1.size() == (num_nodes, 2 * out_channels)
jit_conv = conv.jittable(x=x, pos_edge_index=pos_ei, neg_edge_index=neg_ei)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(x, pos_ei, neg_ei).tolist() == out1.tolist()
conv = SignedConv(out_channels, out_channels, first_aggr=False)
assert conv.__repr__() == 'SignedConv(32, 32, first_aggr=False)'
out2 = conv(out1, pos_ei, neg_ei)
assert out2.size() == (num_nodes, 2 * out_channels)
jit_conv = conv.jittable(x=out1,
pos_edge_index=pos_ei,
neg_edge_index=neg_ei)
jit_conv = torch.jit.script(jit_conv)
assert jit_conv(out1, pos_ei, neg_ei).tolist() == out2.tolist()
def __init__(self,
in_channels,
hidden_channels,
num_layers,
lamb=5,
bias=True):
super(SignedGCN, self).__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.num_layers = num_layers
self.lamb = lamb
self.conv1 = SignedConv(in_channels,
hidden_channels // 2,
first_aggr=True)
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(
SignedConv(hidden_channels // 2,
hidden_channels // 2,
first_aggr=False))
self.lin = torch.nn.Linear(2 * hidden_channels, 3)
self.reset_parameters()
def __init__(self,
num_features,
n_classes,
num_hidden,
num_hidden_layers,
dropout,
activation,
first_aggr='add',
bias=True):
super(PSigned, self).__init__()
# dropout
if dropout:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = nn.Dropout(p=0.)
#activation
self.activation = activation
# input layer
self.conv_input = SignedConv(num_features,
num_hidden,
first_aggr=first_aggr,
bias=bias)
# Hidden layers
self.layers = nn.ModuleList()
for _ in range(num_hidden_layers):
self.layers.append(
SignedConv(num_hidden,
num_hidden,
first_aggr=first_aggr,
bias=bias))
# output layer
self.conv_output = SignedConv(num_hidden,
n_classes,
first_aggr=first_aggr,
bias=bias)
def test_signed_conv():
x = torch.randn(4, 16)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
row, col = edge_index
adj = SparseTensor(row=row, col=col, sparse_sizes=(4, 4))
conv1 = SignedConv(16, 32, first_aggr=True)
assert conv1.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
conv2 = SignedConv(32, 48, first_aggr=False)
assert conv2.__repr__() == 'SignedConv(32, 48, first_aggr=False)'
out1 = conv1(x, edge_index, edge_index)
assert out1.size() == (4, 64)
assert conv1(x, adj.t(), adj.t()).tolist() == out1.tolist()
out2 = conv2(out1, edge_index, edge_index)
assert out2.size() == (4, 96)
assert conv2(out1, adj.t(), adj.t()).tolist() == out2.tolist()
if is_full_test():
t = '(Tensor, Tensor, Tensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert jit1(x, edge_index, edge_index).tolist() == out1.tolist()
assert jit2(out1, edge_index, edge_index).tolist() == out2.tolist()
t = '(Tensor, SparseTensor, SparseTensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert jit1(x, adj.t(), adj.t()).tolist() == out1.tolist()
assert jit2(out1, adj.t(), adj.t()).tolist() == out2.tolist()
adj = adj.sparse_resize((4, 2))
assert torch.allclose(conv1((x, x[:2]), edge_index, edge_index), out1[:2],
atol=1e-6)
assert torch.allclose(conv1((x, x[:2]), adj.t(), adj.t()), out1[:2],
atol=1e-6)
assert torch.allclose(conv2((out1, out1[:2]), edge_index, edge_index),
out2[:2], atol=1e-6)
assert torch.allclose(conv2((out1, out1[:2]), adj.t(), adj.t()), out2[:2],
atol=1e-6)
if is_full_test():
t = '(PairTensor, Tensor, Tensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert torch.allclose(jit1((x, x[:2]), edge_index, edge_index),
out1[:2], atol=1e-6)
assert torch.allclose(jit2((out1, out1[:2]), edge_index, edge_index),
out2[:2], atol=1e-6)
t = '(PairTensor, SparseTensor, SparseTensor) -> Tensor'
jit1 = torch.jit.script(conv1.jittable(t))
jit2 = torch.jit.script(conv2.jittable(t))
assert torch.allclose(jit1((x, x[:2]), adj.t(), adj.t()), out1[:2],
atol=1e-6)
assert torch.allclose(jit2((out1, out1[:2]), adj.t(), adj.t()),
out2[:2], atol=1e-6)
def test_signed_conv():
in_channels, out_channels = (16, 32)
pos_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
neg_ei = torch.tensor([[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]])
num_nodes = pos_ei.max().item() + 1
x = torch.randn((num_nodes, in_channels))
conv = SignedConv(in_channels, out_channels, first_aggr=True)
assert conv.__repr__() == 'SignedConv(16, 32, first_aggr=True)'
x = conv(x, pos_ei, neg_ei)
assert x.size() == (num_nodes, 2 * out_channels)
conv = SignedConv(out_channels, out_channels, first_aggr=False)
assert conv.__repr__() == 'SignedConv(32, 32, first_aggr=False)'
assert conv(x, pos_ei, neg_ei).size() == (num_nodes, 2 * out_channels)