forked from mrq/DL-Art-School
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
from models.networks import define_F
|
|
from models.loss import GANLoss
|
|
from torchvision.utils import save_image
|
|
|
|
|
|
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
|
|
self.metrics = []
|
|
|
|
def forward(self, net, state):
|
|
raise NotImplementedError
|
|
|
|
def extra_metrics(self):
|
|
return self.metrics
|
|
|
|
|
|
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'])
|
|
if not env['opt']['dist']:
|
|
self.netF = torch.nn.parallel.DataParallel(self.netF)
|
|
|
|
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'])
|
|
|
|
def forward(self, net, state):
|
|
netD = self.env['discriminators'][self.opt['discriminator']]
|
|
if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
|
|
if self.opt['gan_type'] == 'crossgan':
|
|
pred_g_fake = netD(state[self.opt['fake']], state['lq_fullsize_ref'])
|
|
else:
|
|
pred_g_fake = netD(state[self.opt['fake']])
|
|
return self.criterion(pred_g_fake, True)
|
|
elif self.opt['gan_type'] == 'ragan':
|
|
pred_d_real = netD(state[self.opt['real']]).detach()
|
|
pred_g_fake = 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):
|
|
self.metrics = []
|
|
|
|
if self.opt['gan_type'] == 'crossgan':
|
|
d_real = net(state[self.opt['real']], state['lq_fullsize_ref'])
|
|
d_fake = net(state[self.opt['fake']].detach(), state['lq_fullsize_ref'])
|
|
mismatched_lq = torch.roll(state['lq_fullsize_ref'], shifts=1, dims=0)
|
|
d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
|
|
d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
|
|
else:
|
|
d_real = net(state[self.opt['real']])
|
|
d_fake = net(state[self.opt['fake']].detach())
|
|
self.metrics.append(("d_fake", torch.mean(d_fake)))
|
|
|
|
if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan']:
|
|
l_real = self.criterion(d_real, True)
|
|
l_fake = self.criterion(d_fake, False)
|
|
l_total = l_real + l_fake
|
|
if self.opt['gan_type'] == 'crossgan':
|
|
l_mreal = self.criterion(d_mismatch_real, False)
|
|
l_mfake = self.criterion(d_mismatch_fake, False)
|
|
l_total += l_mreal + l_mfake
|
|
self.metrics.append(("l_mismatch", l_mfake + l_mreal))
|
|
self.metrics.append(("l_fake", l_fake))
|
|
return l_total
|
|
elif self.opt['gan_type'] == 'ragan':
|
|
return (self.cri_gan(d_real - torch.mean(d_fake), True) +
|
|
self.cri_gan(d_fake - torch.mean(d_real), False))
|
|
else:
|
|
raise NotImplementedError
|
|
|