Architectures/Vocoder/AdversarialLoss.py

# -*- coding: utf-8 -*-

# Copyright 2021 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)


import torch
import torch.nn.functional as F


def discriminator_adv_loss(disc_real_outputs, disc_generated_outputs):
    loss = 0
    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
        dr_fun, dr_dir = dr
        dg_fun, dg_dir = dg
        r_loss_fun = torch.mean(F.softplus(1 - dr_fun) ** 2)
        g_loss_fun = torch.mean(F.softplus(dg_fun) ** 2)
        r_loss_dir = torch.mean(F.softplus(1 - dr_dir) ** 2)
        g_loss_dir = torch.mean(-F.softplus(1 - dg_dir) ** 2)
        r_loss = r_loss_fun + r_loss_dir
        g_loss = g_loss_fun + g_loss_dir
        loss += (r_loss + g_loss)

    return loss / len(disc_generated_outputs)


def generator_adv_loss(disc_outputs):
    loss = 0
    for dg in disc_outputs:
        l = torch.mean(F.softplus(1 - dg) ** 2)
        loss += l

    return loss / len(disc_outputs)


class GeneratorAdversarialLoss(torch.nn.Module):

    def __init__(self,
                 average_by_discriminators=True,
                 loss_type="mse", ):
        """Initialize GeneratorAversarialLoss module."""
        super().__init__()
        self.average_by_discriminators = average_by_discriminators
        assert loss_type in ["mse", "hinge"], f"{loss_type} is not supported."
        if loss_type == "mse":
            self.criterion = self._mse_loss
        else:
            self.criterion = self._hinge_loss

    def forward(self, outputs):
        """
        Calcualate generator adversarial loss.

        Args:
            outputs (Tensor or list): Discriminator outputs or list of
                discriminator outputs.

        Returns:
            Tensor: Generator adversarial loss value.
        """
        if isinstance(outputs, (tuple, list)):
            adv_loss = 0.0
            for i, outputs_ in enumerate(outputs):
                if isinstance(outputs_, (tuple, list)):
                    outputs_ = outputs_[-1]
                adv_loss = adv_loss + self.criterion(outputs_)
            if self.average_by_discriminators:
                adv_loss /= i + 1
        else:
            adv_loss = self.criterion(outputs)

        return adv_loss

    def _mse_loss(self, x):
        return F.mse_loss(x, x.new_ones(x.size()))

    def _hinge_loss(self, x):
        return -x.mean()


class DiscriminatorAdversarialLoss(torch.nn.Module):

    def __init__(self,
                 average_by_discriminators=True,
                 loss_type="mse", ):
        super().__init__()
        self.average_by_discriminators = average_by_discriminators
        assert loss_type in ["mse", "hinge"], f"{loss_type} is not supported."
        if loss_type == "mse":
            self.fake_criterion = self._mse_fake_loss
            self.real_criterion = self._mse_real_loss
        else:
            self.fake_criterion = self._hinge_fake_loss
            self.real_criterion = self._hinge_real_loss

    def forward(self, outputs_hat, outputs):
        """
        Calcualate discriminator adversarial loss.

        Args:
            outputs_hat (Tensor or list): Discriminator outputs or list of
                discriminator outputs calculated from generator outputs.
            outputs (Tensor or list): Discriminator outputs or list of
                discriminator outputs calculated from groundtruth.

        Returns:
            Tensor: Discriminator real loss value.
            Tensor: Discriminator fake loss value.
        """
        if isinstance(outputs, (tuple, list)):
            real_loss = 0.0
            fake_loss = 0.0
            for i, (outputs_hat_, outputs_) in enumerate(zip(outputs_hat, outputs)):
                if isinstance(outputs_hat_, (tuple, list)):
                    outputs_hat_ = outputs_hat_[-1]
                    outputs_ = outputs_[-1]
                real_loss = real_loss + self.real_criterion(outputs_)
                fake_loss = fake_loss + self.fake_criterion(outputs_hat_)
            if self.average_by_discriminators:
                fake_loss /= i + 1
                real_loss /= i + 1
        else:
            real_loss = self.real_criterion(outputs)
            fake_loss = self.fake_criterion(outputs_hat)

        return real_loss + fake_loss

    def _mse_real_loss(self, x):
        return F.mse_loss(x, x.new_ones(x.size()))

    def _mse_fake_loss(self, x):
        return F.mse_loss(x, x.new_zeros(x.size()))

    def _hinge_real_loss(self, x):
        return -torch.mean(torch.min(x - 1, x.new_zeros(x.size())))

    def _hinge_fake_loss(self, x):
        return -torch.mean(torch.min(-x - 1, x.new_zeros(x.size())))