def test_beta_datatype(self):
margin, nu, beta = 0.1, 0, 1
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta)
self.assertTrue(len(loss_func.beta) == 1)
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta, learn_beta=True)
self.assertTrue(len(loss_func.beta) == 1)
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta, learn_beta=True, num_classes=35)
self.assertTrue(len(loss_func.beta) == 35)
def test_with_no_valid_triplets(self):
margin, nu, beta = 0.1, 0, 1
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta)
embedding_angles = [0, 20, 40, 60, 80]
embeddings = torch.FloatTensor(
[c_f.angle_to_coord(a) for a in embedding_angles]) #2D embeddings
labels = torch.LongTensor([0, 1, 2, 3, 4])
self.assertEqual(loss_func(embeddings, labels), 0)
def test_with_no_valid_triplets(self):
margin, nu, beta = 0.1, 0, 1
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta)
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, 1, 2, 3, 4])
loss = loss_func(embeddings, labels)
loss.backward()
self.assertEqual(loss, 0)
def test_margin_loss(self):
for dtype in [torch.float16, torch.float32, torch.float64]:
for learn_beta, num_classes in [(False, None), (True, None),
(False, 3), (True, 3)]:
margin, nu, beta = 0.1, 0.1, 1
loss_func = MarginLoss(margin=margin,
nu=nu,
beta=beta,
learn_beta=learn_beta,
num_classes=num_classes)
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()
triplets = [(0, 1, 2), (0, 1, 3), (0, 1, 4), (1, 0, 2),
(1, 0, 3), (1, 0, 4), (2, 3, 0), (2, 3, 1),
(2, 3, 4), (3, 2, 0), (3, 2, 1), (3, 2, 4)]
correct_total_loss = 0
num_non_zero = 0
for a, p, n in triplets:
anchor, positive, negative = embeddings[a], embeddings[
p], embeddings[n]
pos_loss = torch.relu(
torch.sqrt(torch.sum((anchor - positive)**2)) - beta +
margin)
neg_loss = torch.relu(
beta - torch.sqrt(torch.sum((anchor - negative)**2)) +
margin)
correct_total_loss += pos_loss + neg_loss
if pos_loss > 0:
num_non_zero += 1
if neg_loss > 0:
num_non_zero += 1
if num_non_zero > 0:
correct_total_loss /= num_non_zero
if learn_beta:
if num_classes is None:
correct_beta_reg_loss = (loss_func.beta * nu)
else:
anchor_idx = [x[0] for x in triplets]
correct_beta_reg_loss = torch.sum(
loss_func.beta[labels[anchor_idx]] *
nu) / num_non_zero
correct_total_loss += correct_beta_reg_loss.item()
self.assertTrue(torch.isclose(loss, correct_total_loss))
def test_margin_loss(self):
margin, nu, beta = 0.1, 0, 1
loss_func = MarginLoss(margin=margin, nu=nu, beta=beta)
embedding_angles = [0, 20, 40, 60, 80]
embeddings = torch.FloatTensor(
[c_f.angle_to_coord(a) for a in embedding_angles]) #2D embeddings
labels = torch.LongTensor([0, 0, 1, 1, 2])
loss = loss_func(embeddings, labels)
triplets = [(0, 1, 2), (0, 1, 3), (0, 1, 4), (1, 0, 2), (1, 0, 3),
(1, 0, 4), (2, 3, 0), (2, 3, 1), (2, 3, 4), (3, 2, 0),
(3, 2, 1), (3, 2, 4)]
correct_total_loss = 0
num_non_zero = 0
for a, p, n in triplets:
anchor, positive, negative = embeddings[a], embeddings[
p], embeddings[n]
pos_loss = torch.relu(
torch.sqrt(torch.sum((anchor - positive)**2)) - beta + margin)
neg_loss = torch.relu(beta -
torch.sqrt(torch.sum((anchor -
negative)**2)) +
margin)
correct_total_loss += pos_loss + neg_loss
if pos_loss > 0:
num_non_zero += 1
if neg_loss > 0:
num_non_zero += 1
if num_non_zero > 0:
correct_total_loss /= num_non_zero
self.assertTrue(torch.isclose(loss, correct_total_loss))