Source code for mmedit.models.losses.gan_loss

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.autograd as autograd
import torch.nn as nn

from ..registry import LOSSES

[docs]@LOSSES.register_module() class GANLoss(nn.Module): """Define GAN loss. Args: gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. real_label_val (float): The value for real label. Default: 1.0. fake_label_val (float): The value for fake label. Default: 0.0. loss_weight (float): Loss weight. Default: 1.0. Note that loss_weight is only for generators; and it is always 1.0 for discriminators. """ def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): super().__init__() self.gan_type = gan_type self.loss_weight = loss_weight self.real_label_val = real_label_val self.fake_label_val = fake_label_val if self.gan_type == 'vanilla': self.loss = nn.BCEWithLogitsLoss() elif self.gan_type == 'lsgan': self.loss = nn.MSELoss() elif self.gan_type == 'wgan': self.loss = self._wgan_loss elif self.gan_type == 'hinge': self.loss = nn.ReLU() else: raise NotImplementedError( f'GAN type {self.gan_type} is not implemented.') def _wgan_loss(self, input, target): """wgan loss. Args: input (Tensor): Input tensor. target (bool): Target label. Returns: Tensor: wgan loss. """ return -input.mean() if target else input.mean()
[docs] def get_target_label(self, input, target_is_real): """Get target label. Args: input (Tensor): Input tensor. target_is_real (bool): Whether the target is real or fake. Returns: (bool | Tensor): Target tensor. Return bool for wgan, otherwise, return Tensor. """ if self.gan_type == 'wgan': return target_is_real target_val = ( self.real_label_val if target_is_real else self.fake_label_val) return input.new_ones(input.size()) * target_val
[docs] def forward(self, input, target_is_real, is_disc=False): """ Args: input (Tensor): The input for the loss module, i.e., the network prediction. target_is_real (bool): Whether the targe is real or fake. is_disc (bool): Whether the loss for discriminators or not. Default: False. Returns: Tensor: GAN loss value. """ target_label = self.get_target_label(input, target_is_real) if self.gan_type == 'hinge': if is_disc: # for discriminators in hinge-gan input = -input if target_is_real else input loss = self.loss(1 + input).mean() else: # for generators in hinge-gan loss = -input.mean() else: # other gan types loss = self.loss(input, target_label) # loss_weight is always 1.0 for discriminators return loss if is_disc else loss * self.loss_weight
def gradient_penalty_loss(discriminator, real_data, fake_data, mask=None): """Calculate gradient penalty for wgan-gp. Args: discriminator (nn.Module): Network for the discriminator. real_data (Tensor): Real input data. fake_data (Tensor): Fake input data. mask (Tensor): Masks for inpainting. Default: None. Returns: Tensor: A tensor for gradient penalty. """ batch_size = real_data.size(0) alpha = torch.rand(batch_size, 1, 1, 1).to(real_data) # interpolate between real_data and fake_data interpolates = alpha * real_data + (1. - alpha) * fake_data interpolates = autograd.Variable(interpolates, requires_grad=True) disc_interpolates = discriminator(interpolates) gradients = autograd.grad( outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones_like(disc_interpolates), create_graph=True, retain_graph=True, only_inputs=True)[0] if mask is not None: gradients = gradients * mask gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() if mask is not None: gradients_penalty /= torch.mean(mask) return gradients_penalty
[docs]@LOSSES.register_module() class GradientPenaltyLoss(nn.Module): """Gradient penalty loss for wgan-gp. Args: loss_weight (float): Loss weight. Default: 1.0. """ def __init__(self, loss_weight=1.): super().__init__() self.loss_weight = loss_weight
[docs] def forward(self, discriminator, real_data, fake_data, mask=None): """Forward function. Args: discriminator (nn.Module): Network for the discriminator. real_data (Tensor): Real input data. fake_data (Tensor): Fake input data. mask (Tensor): Masks for inpainting. Default: None. Returns: Tensor: Loss. """ loss = gradient_penalty_loss( discriminator, real_data, fake_data, mask=mask) return loss * self.loss_weight
[docs]@LOSSES.register_module() class DiscShiftLoss(nn.Module): """Disc shift loss. Args: loss_weight (float, optional): Loss weight. Defaults to 1.0. """ def __init__(self, loss_weight=0.1): super().__init__() self.loss_weight = loss_weight
[docs] def forward(self, x): """Forward function. Args: x (Tensor): Tensor with shape (n, c, h, w) Returns: Tensor: Loss. """ loss = torch.mean(x**2) return loss * self.loss_weight
Read the Docs v: v0.12.0
On Read the Docs
Project Home

Free document hosting provided by Read the Docs.