def test_with_no_valid_pairs(self):
loss_func = GeneralizedLiftedStructureLoss(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)
def test_generalized_lifted_structure_loss(self):
neg_margin = 0.5
loss_func = GeneralizedLiftedStructureLoss(neg_margin=neg_margin)
for dtype in TEST_DTYPES:
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=dtype).to(self.device) #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)]
correct_total = 0
for i in range(len(embeddings)):
correct_pos_loss = 0
correct_neg_loss = 0
for a, p in pos_pairs:
if a == i:
anchor, positive = embeddings[a], embeddings[p]
correct_pos_loss += torch.exp(
torch.sqrt(torch.sum((anchor - positive)**2)))
if correct_pos_loss > 0:
correct_pos_loss = torch.log(correct_pos_loss)
for a, n in neg_pairs:
if a == i:
anchor, negative = embeddings[a], embeddings[n]
correct_neg_loss += torch.exp(
neg_margin -
torch.sqrt(torch.sum((anchor - negative)**2)))
if correct_neg_loss > 0:
correct_neg_loss = torch.log(correct_neg_loss)
correct_total += torch.relu(correct_pos_loss +
correct_neg_loss)
correct_total /= embeddings.size(0)
rtol = 1e-2 if dtype == torch.float16 else 1e-5
self.assertTrue(torch.isclose(loss, correct_total, rtol=rtol))