107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from models.networks import define_F
|
|
from models.loss import GANLoss
|
|
|
|
|
|
def create_generator_loss(opt_loss, env):
|
|
type = opt_loss['type']
|
|
if type == 'pix':
|
|
return PixLoss(opt_loss, env)
|
|
elif type == 'feature':
|
|
return FeatureLoss(opt_loss, env)
|
|
elif type == 'generator_gan':
|
|
return GeneratorGanLoss(opt_loss, env)
|
|
elif type == 'discriminator_gan':
|
|
return DiscriminatorGanLoss(opt_loss, env)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
|
|
class ConfigurableLoss(nn.Module):
|
|
def __init__(self, opt, env):
|
|
super(ConfigurableLoss, self).__init__()
|
|
self.opt = opt
|
|
self.env = env
|
|
|
|
def forward(self, net, state):
|
|
raise NotImplementedError
|
|
|
|
|
|
def get_basic_criterion_for_name(name, device):
|
|
if name == 'l1':
|
|
return nn.L1Loss().to(device)
|
|
elif name == 'l2':
|
|
return nn.MSELoss().to(device)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
|
|
class PixLoss(ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super(PixLoss, self).__init__(opt, env)
|
|
self.opt = opt
|
|
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
|
|
|
|
def forward(self, net, state):
|
|
return self.criterion(state[self.opt['fake']], state[self.opt['real']])
|
|
|
|
|
|
class FeatureLoss(ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super(FeatureLoss, self).__init__(opt, env)
|
|
self.opt = opt
|
|
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
|
|
self.netF = define_F(which_model=opt['which_model_F']).to(self.env['device'])
|
|
|
|
def forward(self, net, state):
|
|
with torch.no_grad():
|
|
logits_real = self.netF(state[self.opt['real']])
|
|
logits_fake = self.netF(state[self.opt['fake']])
|
|
return self.criterion(logits_fake, logits_real)
|
|
|
|
|
|
class GeneratorGanLoss(ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super(GeneratorGanLoss, self).__init__(opt, env)
|
|
self.opt = opt
|
|
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
|
|
self.netD = env['discriminators'][opt['discriminator']]
|
|
|
|
def forward(self, net, state):
|
|
if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
|
|
if self.opt['gan_type'] == 'crossgan':
|
|
pred_g_fake = self.netD(state[self.opt['fake']], state['lq'])
|
|
else:
|
|
pred_g_fake = self.netD(state[self.opt['fake']])
|
|
return self.criterion(pred_g_fake, True)
|
|
elif self.opt['gan_type'] == 'ragan':
|
|
pred_d_real = self.netD(state[self.opt['real']]).detach()
|
|
pred_g_fake = self.netD(state[self.opt['fake']])
|
|
return (self.cri_gan(pred_d_real - torch.mean(pred_g_fake), False) +
|
|
self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
|
|
class DiscriminatorGanLoss(ConfigurableLoss):
|
|
def __init__(self, opt, env):
|
|
super(DiscriminatorGanLoss, self).__init__(opt, env)
|
|
self.opt = opt
|
|
self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
|
|
|
|
def forward(self, net, state):
|
|
if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
|
|
if self.opt['gan_type'] == 'crossgan':
|
|
pred_g_fake = net(state[self.opt['fake']].detach(), state['lq'])
|
|
else:
|
|
pred_g_fake = net(state[self.opt['fake']].detach())
|
|
return self.criterion(pred_g_fake, False)
|
|
elif self.opt['gan_type'] == 'ragan':
|
|
pred_d_real = self.netD(state[self.opt['real']])
|
|
pred_g_fake = self.netD(state[self.opt['fake']].detach())
|
|
return (self.cri_gan(pred_d_real - torch.mean(pred_g_fake), True) +
|
|
self.cri_gan(pred_g_fake - torch.mean(pred_d_real), False)) / 2
|
|
else:
|
|
raise NotImplementedError
|