loss_functions.py 2.2 KB

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  1. # *****************************************************************************
  2. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
  3. #
  4. # Redistribution and use in source and binary forms, with or without
  5. # modification, are permitted provided that the following conditions are met:
  6. # * Redistributions of source code must retain the above copyright
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  8. # * Redistributions in binary form must reproduce the above copyright
  9. # notice, this list of conditions and the following disclaimer in the
  10. # documentation and/or other materials provided with the distribution.
  11. # * Neither the name of the NVIDIA CORPORATION nor the
  12. # names of its contributors may be used to endorse or promote products
  13. # derived from this software without specific prior written permission.
  14. #
  15. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  16. # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  17. # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  18. # DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
  19. # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
  20. # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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  22. # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  23. # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
  24. # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  25. #
  26. # *****************************************************************************
  27. import torch
  28. import torch.nn as nn
  29. from tacotron2.loss_function import Tacotron2Loss
  30. from waveglow.loss_function import WaveGlowLoss
  31. def get_loss_function(loss_function, sigma=1.0):
  32. if loss_function == 'Tacotron2':
  33. loss = Tacotron2Loss()
  34. elif loss_function == 'WaveGlow':
  35. loss = WaveGlowLoss(sigma=sigma)
  36. else:
  37. raise NotImplementedError(
  38. "unknown loss function requested: {}".format(loss_function))
  39. loss.cuda()
  40. return loss