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| import unittest | |
| import torch | |
| from training.loss.fast_speech_2_loss_gen import FastSpeech2LossGen | |
| class TestFastSpeech2LossGen(unittest.TestCase): | |
| def setUp(self): | |
| self.loss_gen = FastSpeech2LossGen() | |
| def test_forward(self): | |
| # Reproducible results | |
| torch.random.manual_seed(0) | |
| # Test with all inputs of shape (1, 11) | |
| src_masks = torch.zeros((1, 11), dtype=torch.bool) | |
| mel_masks = torch.zeros((1, 11), dtype=torch.bool) | |
| mel_targets = torch.randn((1, 11, 11)) | |
| # postnet = torch.randn((1, 11, 11)) | |
| mel_predictions = torch.randn((1, 11, 11)) | |
| log_duration_predictions = torch.randn((1, 11)) | |
| u_prosody_ref = torch.randn((1, 11)) | |
| u_prosody_pred = torch.randn((1, 11)) | |
| p_prosody_ref = torch.randn((1, 11, 11)) | |
| p_prosody_pred = torch.randn((1, 11, 11)) | |
| durations = torch.randn((1, 11)) | |
| pitch_predictions = torch.randn((1, 11)) | |
| p_targets = torch.randn((1, 11)) | |
| attn_logprob = torch.randn((1, 1, 11, 11)) | |
| attn_soft = torch.randn((1, 11, 11)) | |
| attn_hard = torch.randn((1, 11, 11)) | |
| step = 20000 | |
| src_lens = torch.ones((1,), dtype=torch.long) | |
| mel_lens = torch.ones((1,), dtype=torch.long) | |
| energy_pred = torch.randn((1, 11)) | |
| energy_target = torch.randn((1, 11)) | |
| ( | |
| total_loss, | |
| mel_loss, | |
| # mel_loss_postnet, | |
| ssim_loss, | |
| # ssim_loss_postnet, | |
| duration_loss, | |
| u_prosody_loss, | |
| p_prosody_loss, | |
| pitch_loss, | |
| ctc_loss, | |
| bin_loss, | |
| energy_loss, | |
| ) = self.loss_gen.forward( | |
| src_masks, | |
| mel_masks, | |
| mel_targets, | |
| mel_predictions, | |
| # postnet, | |
| log_duration_predictions, | |
| u_prosody_ref, | |
| u_prosody_pred, | |
| p_prosody_ref, | |
| p_prosody_pred, | |
| durations, | |
| pitch_predictions, | |
| p_targets, | |
| attn_logprob, | |
| attn_soft, | |
| attn_hard, | |
| step, | |
| src_lens, | |
| mel_lens, | |
| energy_pred, | |
| energy_target, | |
| ) | |
| self.assertIsInstance(total_loss, torch.Tensor) | |
| self.assertIsInstance(mel_loss, torch.Tensor) | |
| # self.assertIsInstance(mel_loss_postnet, torch.Tensor) | |
| self.assertIsInstance(ssim_loss, torch.Tensor) | |
| # self.assertIsInstance(ssim_loss_postnet, torch.Tensor) | |
| self.assertIsInstance(duration_loss, torch.Tensor) | |
| self.assertIsInstance(u_prosody_loss, torch.Tensor) | |
| self.assertIsInstance(p_prosody_loss, torch.Tensor) | |
| self.assertIsInstance(pitch_loss, torch.Tensor) | |
| self.assertIsInstance(ctc_loss, torch.Tensor) | |
| self.assertIsInstance(bin_loss, torch.Tensor) | |
| self.assertIsInstance(energy_loss, torch.Tensor) | |
| # Assert the value of losses | |
| self.assertTrue( | |
| torch.all( | |
| torch.tensor( | |
| [ | |
| total_loss, | |
| mel_loss, | |
| # mel_loss_postnet, | |
| ssim_loss, | |
| # ssim_loss_postnet, | |
| duration_loss, | |
| u_prosody_loss, | |
| p_prosody_loss, | |
| pitch_loss, | |
| ctc_loss, | |
| bin_loss, | |
| energy_loss, | |
| ], | |
| ) | |
| >= 0, | |
| ), | |
| ) | |
| if __name__ == "__main__": | |
| unittest.main() | |