DL-Art-School/codes/models/steps/losses.py

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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 == 'interpreted_feature':
return InterpretedFeatureLoss(opt_loss, env)
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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):
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super(ConfigurableLoss, self).__init__()
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self.opt = opt
self.env = env
self.metrics = []
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def forward(self, net, state):
raise NotImplementedError
def extra_metrics(self):
return self.metrics
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def get_basic_criterion_for_name(name, device):
if name == 'l1':
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return nn.L1Loss().to(device)
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elif name == 'l2':
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return nn.MSELoss().to(device)
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else:
raise NotImplementedError
class PixLoss(ConfigurableLoss):
def __init__(self, opt, env):
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super(PixLoss, self).__init__(opt, env)
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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):
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super(FeatureLoss, self).__init__(opt, env)
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self.opt = opt
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
self.netF = define_F(which_model=opt['which_model_F'],
load_path=opt['load_path'] if 'load_path' in opt.keys() else None).to(self.env['device'])
if not env['opt']['dist']:
self.netF = torch.nn.parallel.DataParallel(self.netF)
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def forward(self, net, state):
with torch.no_grad():
logits_real = self.netF(state[self.opt['real']])
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logits_fake = self.netF(state[self.opt['fake']])
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return self.criterion(logits_fake, logits_real)
# Special form of feature loss which first computes the feature embedding for the truth space, then uses a second
# network which was trained to replicate that embedding on an altered input space (for example, LR or greyscale) to
# compute the embedding in the generated space. Useful for weakening the influence of the feature network in controlled
# ways.
class InterpretedFeatureLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(InterpretedFeatureLoss, self).__init__(opt, env)
self.opt = opt
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
self.netF_real = define_F(which_model=opt['which_model_F']).to(self.env['device'])
self.netF_gen = define_F(which_model=opt['which_model_F'], load_path=opt['load_path']).to(self.env['device'])
if not env['opt']['dist']:
self.netF_real = torch.nn.parallel.DataParallel(self.netF_real)
self.netF_gen = torch.nn.parallel.DataParallel(self.netF_gen)
def forward(self, net, state):
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logits_real = self.netF_real(state[self.opt['real']])
logits_fake = self.netF_gen(state[self.opt['fake']])
return self.criterion(logits_fake, logits_real)
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class GeneratorGanLoss(ConfigurableLoss):
def __init__(self, opt, env):
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super(GeneratorGanLoss, self).__init__(opt, env)
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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', 'crossgan_lrref']:
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if self.opt['gan_type'] == 'crossgan':
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pred_g_fake = netD(state[self.opt['fake']], state['lq_fullsize_ref'])
elif self.opt['gan_type'] == 'crossgan_lrref':
pred_g_fake = netD(state[self.opt['fake']], state['lq'])
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else:
pred_g_fake = netD(state[self.opt['fake']])
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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']])
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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):
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super(DiscriminatorGanLoss, self).__init__(opt, env)
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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':
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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)
elif self.opt['gan_type'] == 'crossgan_lrref':
d_real = net(state[self.opt['real']], state['lq'])
d_fake = net(state[self.opt['fake']].detach(), state['lq'])
mismatched_lq = torch.roll(state['lq'], 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)))
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self.metrics.append(("d_real", torch.mean(d_real)))
if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan', 'crossgan_lrref']:
l_real = self.criterion(d_real, True)
l_fake = self.criterion(d_fake, False)
l_total = l_real + l_fake
if 'crossgan' in self.opt['gan_type']:
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))
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self.metrics.append(("l_real", l_real))
return l_total
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elif self.opt['gan_type'] == 'ragan':
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return (self.criterion(d_real - torch.mean(d_fake), True) +
self.criterion(d_fake - torch.mean(d_real), False))
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else:
raise NotImplementedError