def get_queries(self, queries):
"""Compute embedding and biases of queries."""
lhs = givens_reflection(self.rel_diag(queries[:, 1]),
self.entity(queries[:, 0]))
rel = self.rel(queries[:, 1])
lhs_biases = self.bh(queries[:, 0])
return lhs + rel, lhs_biases
def get_queries(self, queries):
"""Compute embedding and biases of queries."""
c = F.softplus(self.c[queries[:, 1]])
rel, _ = torch.chunk(self.rel(queries[:, 1]), 2, dim=1)
rel = expmap0(rel, c)
lhs = givens_reflection(self.rel_diag(queries[:, 1]),
self.entity(queries[:, 0]))
lhs = expmap0(lhs, c)
res = project(mobius_add(lhs, rel, c), c)
return (res, c), self.bh(queries[:, 0])
def get_queries(self, queries):
"""Compute embedding and biases of queries."""
c = F.softplus(self.c[queries[:, 1]])
head = self.entity(queries[:, 0])
rot_mat, ref_mat = torch.chunk(self.rel_diag(queries[:, 1]), 2, dim=1)
rot_q = givens_rotations(rot_mat, head).view((-1, 1, self.rank))
ref_q = givens_reflection(ref_mat, head).view((-1, 1, self.rank))
cands = torch.cat([ref_q, rot_q], dim=1)
context_vec = self.context_vec(queries[:, 1]).view((-1, 1, self.rank))
att_weights = torch.sum(context_vec * cands * self.scale,
dim=-1,
keepdim=True)
att_weights = self.act(att_weights)
att_q = torch.sum(att_weights * cands, dim=1)
lhs = expmap0(att_q, c)
rel, _ = torch.chunk(self.rel(queries[:, 1]), 2, dim=1)
rel = expmap0(rel, c)
res = project(mobius_add(lhs, rel, c), c)
return (res, c), self.bh(queries[:, 0])
def get_reflection_queries(self, queries):
lhs_ref_e = givens_reflection(self.ref(queries[:, 1]),
self.entity(queries[:, 0]))
return lhs_ref_e