def test_forward_good(self):
pos_scores = torch.full((3, ), 2, requires_grad=True)
neg_scores = torch.full((3, 5), 1, requires_grad=True)
loss_fn = RankingLoss(1.)
loss = loss_fn(pos_scores, neg_scores)
self.assertTensorEqual(loss, torch.zeros(()))
loss.backward()
def test_no_pos(self):
pos_scores = torch.empty((0, ), requires_grad=True)
neg_scores = torch.empty((0, 3), requires_grad=True)
loss_fn = RankingLoss(1.)
loss = loss_fn(pos_scores, neg_scores)
self.assertTensorEqual(loss, torch.zeros(()))
loss.backward()
def test_forward(self):
pos_scores = torch.tensor([0.8181, 0.5700, 0.3506], requires_grad=True)
neg_scores = torch.tensor([
[0.4437, 0.6573, 0.9986, 0.2548, 0.0998],
[0.6175, 0.4061, 0.4582, 0.5382, 0.3126],
[0.9869, 0.2028, 0.1667, 0.0044, 0.9934],
],
requires_grad=True)
loss_fn = RankingLoss(1.)
loss = loss_fn(pos_scores, neg_scores)
self.assertTensorEqual(loss, torch.tensor(13.4475))
loss.backward()
self.assertTrue((pos_scores.grad != 0).any())
self.assertTrue((neg_scores.grad != 0).any())
def __init__(
self,
global_optimizer: Optimizer,
loss_fn: LossFunction,
margin: float,
relations: List[RelationSchema],
) -> None:
super().__init__()
self.global_optimizer = global_optimizer
self.entity_optimizers: Dict[Tuple[EntityName, Partition], Optimizer] = {}
if loss_fn is LossFunction.LOGISTIC:
self.loss_fn = LogisticLoss()
elif loss_fn is LossFunction.RANKING:
self.loss_fn = RankingLoss(margin)
elif loss_fn is LossFunction.SOFTMAX:
self.loss_fn = SoftmaxLoss()
else:
raise NotImplementedError("Unknown loss function: %s" % loss_fn)
self.relations = relations