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import unittest
import torch as T
from TTS.tts.layers.losses import BCELossMasked, L1LossMasked, MSELossMasked, SSIMLoss
from TTS.tts.utils.helpers import sequence_mask
class L1LossMaskedTests(unittest.TestCase):
def test_in_out(self): # pylint: disable=no-self-use
# test input == target
layer = L1LossMasked(seq_len_norm=False)
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
dummy_input = T.rand(4, 8, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
# seq_len_norm = True
# test input == target
layer = L1LossMasked(seq_len_norm=True)
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
dummy_input = T.rand(4, 8, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
class MSELossMaskedTests(unittest.TestCase):
def test_in_out(self): # pylint: disable=no-self-use
# test input == target
layer = MSELossMasked(seq_len_norm=False)
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
dummy_input = T.rand(4, 8, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
# seq_len_norm = True
# test input == target
layer = MSELossMasked(seq_len_norm=True)
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
dummy_input = T.rand(4, 8, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(5, 9)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
class SSIMLossTests(unittest.TestCase):
def test_in_out(self): # pylint: disable=no-self-use
# test input == target
layer = SSIMLoss()
dummy_input = T.ones(4, 57, 128).float()
dummy_target = T.ones(4, 57, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.arange(0, 4 * 57 * 128)
dummy_input = dummy_input.reshape(4, 57, 128).float()
dummy_target = T.arange(-4 * 57 * 128, 0)
dummy_target = dummy_target.reshape(4, 57, 128).float()
dummy_target = -dummy_target
dummy_length = (T.ones(4) * 58).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() >= 1.0, "0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 57, 128).float()
dummy_target = T.zeros(4, 57, 128).float()
dummy_length = (T.arange(54, 58)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0.0
dummy_input = T.rand(4, 57, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(54, 58)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
# seq_len_norm = True
# test input == target
layer = L1LossMasked(seq_len_norm=True)
dummy_input = T.ones(4, 57, 128).float()
dummy_target = T.ones(4, 57, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 0.0
# test input != target
dummy_input = T.ones(4, 57, 128).float()
dummy_target = T.zeros(4, 57, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.item() == 1.0, "1.0 vs {}".format(output.item())
# test if padded values of input makes any difference
dummy_input = T.ones(4, 57, 128).float()
dummy_target = T.zeros(4, 57, 128).float()
dummy_length = (T.arange(54, 58)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert abs(output.item() - 1.0) < 1e-5, "1.0 vs {}".format(output.item())
dummy_input = T.rand(4, 57, 128).float()
dummy_target = dummy_input.detach()
dummy_length = (T.arange(54, 58)).long()
mask = ((sequence_mask(dummy_length).float() - 1.0) * 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.item() == 0, "0 vs {}".format(output.item())
class BCELossTest(unittest.TestCase):
def test_in_out(self): # pylint: disable=no-self-use
layer = BCELossMasked(pos_weight=5.0)
length = T.tensor([95])
target = (
1.0 - sequence_mask(length - 1, 100).float()
) # [0, 0, .... 1, 1] where the first 1 is the last mel frame
true_x = target * 200 - 100 # creates logits of [-100, -100, ... 100, 100] corresponding to target
zero_x = T.zeros(target.shape) - 100.0 # simulate logits if it never stops decoding
early_x = -200.0 * sequence_mask(length - 3, 100).float() + 100.0 # simulate logits on early stopping
late_x = -200.0 * sequence_mask(length + 1, 100).float() + 100.0 # simulate logits on late stopping
loss = layer(true_x, target, length)
self.assertEqual(loss.item(), 0.0)
loss = layer(early_x, target, length)
self.assertAlmostEqual(loss.item(), 2.1053, places=4)
loss = layer(late_x, target, length)
self.assertAlmostEqual(loss.item(), 5.2632, places=4)
loss = layer(zero_x, target, length)
self.assertAlmostEqual(loss.item(), 5.2632, places=4)
# pos_weight should be < 1 to penalize early stopping
layer = BCELossMasked(pos_weight=0.2)
loss = layer(true_x, target, length)
self.assertEqual(loss.item(), 0.0)
# when pos_weight < 1 overweight the early stopping loss
loss_early = layer(early_x, target, length)
loss_late = layer(late_x, target, length)
self.assertGreater(loss_early.item(), loss_late.item())
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