def test_no_pos(self):
pos_scores = torch.empty((0, ), requires_grad=True)
neg_scores = torch.empty((0, 3), requires_grad=True)
loss_fn = RankingLossFunction(margin=1.0)
loss = loss_fn(pos_scores, neg_scores)
self.assertTensorEqual(loss, torch.zeros(()))
loss.backward()
def test_forward_bad(self):
pos_scores = torch.full((3, ), -1.0, requires_grad=True)
neg_scores = torch.zeros((3, 5), requires_grad=True)
loss_fn = RankingLossFunction(margin=1.0)
loss = loss_fn(pos_scores, neg_scores)
self.assertTensorEqual(loss, torch.tensor(30.0))
loss.backward()
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 = RankingLossFunction(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 = RankingLossFunction(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 test_forward_weight(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,
)
weight = torch.full((3, ), 1.23)
loss_fn = RankingLossFunction(margin=1.0)
loss = loss_fn(pos_scores, neg_scores, weight)
self.assertTensorEqual(loss, torch.tensor(13.4475 * 1.23))
loss.backward()
self.assertTrue((pos_scores.grad != 0).any())
self.assertTrue((neg_scores.grad != 0).any())
weight = torch.tensor([0.2, 0.4, 0.0])
loss = loss_fn(pos_scores, neg_scores, weight)
self.assertTensorEqual(loss, torch.tensor(2.4658))
