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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()) | |