def _get_triple_score(self, head: BoxTensor, tail: BoxTensor,
relation: BoxTensor, label: Tensor) -> torch.Tensor:
""" Gets score using conditionals.
:: note: We do not need to worry about the dimentions of the boxes. If
it can sensibly broadcast it will.
"""
head.data[..., 0, :] = head.data[..., 0, :] % 1
head.data[..., 1, :] = torch.sigmoid(head.data[..., 1, :])
tail.data[..., 0, :] = tail.data[..., 0, :] % 1
tail.data[..., 1, :] = torch.sigmoid(tail.data[..., 1, :])
triple = torch.stack((head.data, tail.data), dim=-3)
int_lengths = head.per_dim_int_length(triple)
triple = torch.stack((head.data, tail.data), dim=-3)
head_tail_box_vol = head._intersection_volume(triple, True, eps=1e-20)
# score = tail_head_relation_box_vol - tail_relation_box.log_soft_volume(
# temp=self.softbox_temp)
tail_data = tail.data.view(-1, 1, 2, self.embedding_dim)
int_lengths_tail = tail.per_dim_int_length(tail_data, True)
tail_volume = torch.sum(torch.log(int_lengths_tail.clamp_min(1e-20)),
dim=-1)
score = head_tail_box_vol - tail_volume
triple = torch.stack((head.data, tail.data), dim=-3)
target_probs = label
if not self.is_eval():
self.surr_loss = self.pull_loss(triple, target_probs, int_lengths)
else:
self.surr_loss = 0
return score
def _get_triple_score(self,
head: BoxTensor,
tail: BoxTensor,
relation: BoxTensor,
triple_type: str = 'pos') -> torch.Tensor:
""" Gets score using conditionals.
:: note: We do not need to worry about the dimentions of the boxes. If
it can sensibly broadcast it will.
"""
triple = torch.stack((head.data, tail.data), dim=-3)
int_lengths = head.per_dim_int_length(triple)
head_tail_box_vol = torch.sum(
torch.log(int_lengths.clamp_min(0) + 1e-8), dim=-1)
# score = tail_head_relation_box_vol - tail_relation_box.log_soft_volume(
# temp=self.softbox_temp)
tail_data = tail.data.view(-1, 1, 2, self.embedding_dim)
score = head_tail_box_vol - tail._intersection_volume(tail_data, True)
if triple_type == 'pos':
triple = torch.stack((head.data, tail.data), dim=-3)
target_probs = torch.ones_like(score)
if not self.is_eval():
self.surr_loss = self.pull_loss(triple, target_probs,
int_lengths)
else:
self.surr_loss = 0
return score
