def _get_triple_score(self, head: BoxTensor, tail: BoxTensor,
relation: BoxTensor) -> 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.
"""
if self.is_eval():
if len(head.data.shape) > len(tail.data.shape):
tail.data = torch.cat(head.data.shape[-3] * [tail.data])
elif len(head.data.shape) < len(tail.data.shape):
head.data = torch.cat(tail.data.shape[-3] * [head.data])
head_tail_box_vol = head.intersection_log_soft_volume(
tail,
temp=self.softbox_temp,
gumbel_beta=self.gumbel_beta,
bayesian=True)
# score = tail_head_relation_box_vol - tail_relation_box.log_soft_volume(
# temp=self.softbox_temp)
score = head_tail_box_vol - tail.log_soft_volume(
temp=self.softbox_temp)
if len(np.where(score > 0)[0]):
breakpoint()
return score
def _get_triple_score(self, head: BoxTensor, tail: BoxTensor,
relation_head: torch.Tensor,
relation_tail: torch.Tensor) -> torch.Tensor:
head = self.get_relation_transform(box=head, relation=relation_head)
tail = self.get_relation_transform(tail, relation_tail)
head_tail_box_vol = head.intersection_log_soft_volume(
tail, temp=self.softbox_temp)
score = head_tail_box_vol - tail.log_soft_volume(
temp=self.softbox_temp)
return score
def _get_triple_score(self, head: BoxTensor, tail: BoxTensor,
relation: BoxTensor) -> 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_relation_box = relation.intersection(head)
#tail_relation_box = relation.intersection(tail)
# tail_head_relation_box_vol = tail.intersection_log_soft_volume(
# head_relation_box, temp=self.softbox_temp)
# score = tail_head_relation_box_vol - head_relation_box.log_soft_volume(
# temp=self.softbox_temp)
head_tail_box_vol = head.intersection_log_soft_volume(
tail, temp=self.softbox_temp)
# score = tail_head_relation_box_vol - tail_relation_box.log_soft_volume(
# temp=self.softbox_temp)
score = head_tail_box_vol - tail.log_soft_volume(
temp=self.softbox_temp)
return score
