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126 lines
4.3 KiB
126 lines
4.3 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from espnet(https://github.com/espnet/espnet)
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"""Adversarial loss modules."""
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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class GeneratorAdversarialLoss(nn.Layer):
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"""Generator adversarial loss module."""
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def __init__(
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self,
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average_by_discriminators=True,
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loss_type="mse", ):
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"""Initialize GeneratorAversarialLoss module."""
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super().__init__()
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self.average_by_discriminators = average_by_discriminators
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assert loss_type in ["mse", "hinge"], f"{loss_type} is not supported."
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if loss_type == "mse":
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self.criterion = self._mse_loss
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else:
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self.criterion = self._hinge_loss
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def forward(self, outputs):
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"""Calcualate generator adversarial loss.
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Parameters
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----------
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outputs: Tensor or List
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Discriminator outputs or list of discriminator outputs.
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Returns
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----------
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Tensor
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Generator adversarial loss value.
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"""
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if isinstance(outputs, (tuple, list)):
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adv_loss = 0.0
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for i, outputs_ in enumerate(outputs):
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if isinstance(outputs_, (tuple, list)):
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# case including feature maps
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outputs_ = outputs_[-1]
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adv_loss += self.criterion(outputs_)
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if self.average_by_discriminators:
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adv_loss /= i + 1
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else:
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adv_loss = self.criterion(outputs)
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return adv_loss
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def _mse_loss(self, x):
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return F.mse_loss(x, paddle.ones_like(x))
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def _hinge_loss(self, x):
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return -x.mean()
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class DiscriminatorAdversarialLoss(nn.Layer):
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"""Discriminator adversarial loss module."""
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def __init__(
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self,
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average_by_discriminators=True,
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loss_type="mse", ):
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"""Initialize DiscriminatorAversarialLoss module."""
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super().__init__()
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self.average_by_discriminators = average_by_discriminators
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assert loss_type in ["mse"], f"{loss_type} is not supported."
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if loss_type == "mse":
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self.fake_criterion = self._mse_fake_loss
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self.real_criterion = self._mse_real_loss
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def forward(self, outputs_hat, outputs):
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"""Calcualate discriminator adversarial loss.
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Parameters
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----------
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outputs_hat : Tensor or list
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Discriminator outputs or list of
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discriminator outputs calculated from generator outputs.
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outputs : Tensor or list
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Discriminator outputs or list of
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discriminator outputs calculated from groundtruth.
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Returns
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----------
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Tensor
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Discriminator real loss value.
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Tensor
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Discriminator fake loss value.
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"""
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if isinstance(outputs, (tuple, list)):
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real_loss = 0.0
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fake_loss = 0.0
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for i, (outputs_hat_,
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outputs_) in enumerate(zip(outputs_hat, outputs)):
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if isinstance(outputs_hat_, (tuple, list)):
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# case including feature maps
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outputs_hat_ = outputs_hat_[-1]
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outputs_ = outputs_[-1]
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real_loss += self.real_criterion(outputs_)
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fake_loss += self.fake_criterion(outputs_hat_)
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if self.average_by_discriminators:
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fake_loss /= i + 1
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real_loss /= i + 1
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else:
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real_loss = self.real_criterion(outputs)
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fake_loss = self.fake_criterion(outputs_hat)
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return real_loss, fake_loss
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def _mse_real_loss(self, x):
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return F.mse_loss(x, paddle.ones_like(x))
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def _mse_fake_loss(self, x):
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return F.mse_loss(x, paddle.zeros_like(x))
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