def test_angular_loss(self):
loss_func = AngularLoss(alpha=40)
for dtype in [torch.float16, torch.float32, torch.float64]:
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()
sq_tan_alpha = torch.tan(
torch.tensor(np.radians(40), dtype=dtype).to(self.device))**2
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_losses = [0, 0, 0, 0]
for a, p, n in triplets:
anchor, positive, negative = embeddings[a], embeddings[
p], embeddings[n]
exponent = 4 * sq_tan_alpha * torch.matmul(
anchor + positive, negative) - 2 * (
1 + sq_tan_alpha) * torch.matmul(anchor, positive)
correct_losses[a] += torch.exp(exponent)
total_loss = 0
for c in correct_losses:
total_loss += torch.log(1 + c)
total_loss /= len(correct_losses)
self.assertTrue(torch.isclose(loss, total_loss))
def test_with_no_valid_triplets(self):
loss_func = AngularLoss(alpha=40)
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])
loss = loss_func(embeddings, labels)
self.assertEqual(loss, 0)
def test_with_no_valid_triplets(self):
loss_func = AngularLoss(alpha=40)
for dtype in [torch.float16, torch.float32, torch.float64]:
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)