PaddleSpeech/paddlespeech/t2s/modules/adversarial_loss.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from espnet(https://github.com/espnet/espnet)
"""Adversarial loss modules."""
import paddle
import paddle.nn.functional as F
from paddle import nn


class GeneratorAdversarialLoss(nn.Layer):
    """Generator adversarial loss 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.
        Parameters
        ----------
        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)):
                    # case including feature maps
                    outputs_ = outputs_[-1]
                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, paddle.ones_like(x))

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


class DiscriminatorAdversarialLoss(nn.Layer):
    """Discriminator adversarial loss module."""

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

    def forward(self, outputs_hat, outputs):
        """Calcualate discriminator adversarial loss.
        Parameters
        ----------
        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)):
                    # case including feature maps
                    outputs_hat_ = outputs_hat_[-1]
                    outputs_ = outputs_[-1]
                real_loss += self.real_criterion(outputs_)
                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, paddle.ones_like(x))

    def _mse_fake_loss(self, x):
        return F.mse_loss(x, paddle.zeros_like(x))