def test_intra_pair_variance_loss(self):
pos_eps, neg_eps = 0.01, 0.02
loss_func = IntraPairVarianceLoss(pos_eps, neg_eps)
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),
]
pos_total, neg_total = 0, 0
mean_pos = 0
mean_neg = 0
for a, p in pos_pairs:
mean_pos += torch.matmul(embeddings[a], embeddings[p])
for a, n in neg_pairs:
mean_neg += torch.matmul(embeddings[a], embeddings[n])
mean_pos /= len(pos_pairs)
mean_neg /= len(neg_pairs)
for a, p in pos_pairs:
pos_total += (torch.relu(
((1 - pos_eps) * mean_pos -
torch.matmul(embeddings[a], embeddings[p])))**2)
for a, n in neg_pairs:
neg_total += (torch.relu(
(torch.matmul(embeddings[a], embeddings[n]) -
(1 + neg_eps) * mean_neg))**2)
pos_total /= len(pos_pairs)
neg_total /= len(neg_pairs)
correct_total = pos_total + neg_total
rtol = 1e-2 if dtype == torch.float16 else 1e-5
self.assertTrue(torch.isclose(loss, correct_total, rtol=rtol))
def test_with_no_valid_pairs(self):
loss_func = IntraPairVarianceLoss(0.01,0.01)
for dtype in TEST_DTYPES:
embedding_angles = [0]
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])
loss = loss_func(embeddings, labels)
loss.backward()
self.assertEqual(loss, 0)