def test_lifted_structure_loss(self):
neg_margin = 0.5
loss_func = LiftedStructureLoss(neg_margin=neg_margin)
embedding_angles = [0, 20, 40, 60, 80]
embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
labels = torch.LongTensor([0, 0, 1, 1, 2])
loss = loss_func(embeddings, labels)
loss.backward()
pos_pairs = [(0,1), (1,0), (2,3), (3,2)]
neg_pairs = [(0,2), (0,3), (0,4), (1,2), (1,3), (1,4), (2,0), (2,1), (2,4), (3,0), (3,1), (3,4), (4,0), (4,1), (4,2), (4,3)]
total_loss = 0
for a1,p in pos_pairs:
anchor, positive = embeddings[a1], embeddings[p]
pos_pair_component = torch.sqrt(torch.sum((anchor-positive)**2))
neg_pair_component = 0
for a2,n in neg_pairs:
negative = embeddings[n]
if a2 == a1:
neg_pair_component += torch.exp(neg_margin - torch.sqrt(torch.sum((anchor-negative)**2)))
elif a2 == p:
neg_pair_component += torch.exp(neg_margin - torch.sqrt(torch.sum((positive-negative)**2)))
else:
continue
total_loss += torch.relu(torch.log(neg_pair_component) + pos_pair_component)**2
total_loss /= 2*len(pos_pairs)
self.assertTrue(torch.isclose(loss, total_loss))
def test_with_no_valid_pairs(self):
loss_func = LiftedStructureLoss(neg_margin=0.5)
embedding_angles = [0]
embeddings = torch.tensor([c_f.angle_to_coord(a) for a in embedding_angles], requires_grad=True, dtype=torch.float) #2D embeddings
labels = torch.LongTensor([0])
loss = loss_func(embeddings, labels)
loss.backward()
self.assertEqual(loss, 0)