6657a406ac
- Allow original SPSRNet to have a specifiable block increment - Cleanup - Bug fixes in code that hasnt been touched in awhile.
162 lines
6.7 KiB
Python
162 lines
6.7 KiB
Python
import torch
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import torch.nn as nn
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from models.networks import define_F
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from models.loss import GANLoss
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def create_generator_loss(opt_loss, env):
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type = opt_loss['type']
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if type == 'pix':
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return PixLoss(opt_loss, env)
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elif type == 'feature':
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return FeatureLoss(opt_loss, env)
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elif type == 'interpreted_feature':
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return InterpretedFeatureLoss(opt_loss, env)
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elif type == 'generator_gan':
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return GeneratorGanLoss(opt_loss, env)
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elif type == 'discriminator_gan':
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return DiscriminatorGanLoss(opt_loss, env)
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else:
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raise NotImplementedError
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class ConfigurableLoss(nn.Module):
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def __init__(self, opt, env):
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super(ConfigurableLoss, self).__init__()
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self.opt = opt
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self.env = env
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self.metrics = []
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def forward(self, net, state):
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raise NotImplementedError
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def extra_metrics(self):
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return self.metrics
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def get_basic_criterion_for_name(name, device):
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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:
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raise NotImplementedError
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class PixLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(PixLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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def forward(self, net, state):
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return self.criterion(state[self.opt['fake']], state[self.opt['real']])
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class FeatureLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(FeatureLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.netF = define_F(which_model=opt['which_model_F'],
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load_path=opt['load_path'] if 'load_path' in opt.keys() else None).to(self.env['device'])
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if not env['opt']['dist']:
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self.netF = torch.nn.parallel.DataParallel(self.netF)
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def forward(self, net, state):
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with torch.no_grad():
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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)
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# Special form of feature loss which first computes the feature embedding for the truth space, then uses a second
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# network which was trained to replicate that embedding on an altered input space (for example, LR or greyscale) to
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# compute the embedding in the generated space. Useful for weakening the influence of the feature network in controlled
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# ways.
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class InterpretedFeatureLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(InterpretedFeatureLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
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self.netF_real = define_F(which_model=opt['which_model_F']).to(self.env['device'])
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self.netF_gen = define_F(which_model=opt['which_model_F'], load_path=opt['load_path']).to(self.env['device'])
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if not env['opt']['dist']:
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self.netF_real = torch.nn.parallel.DataParallel(self.netF_real)
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self.netF_gen = torch.nn.parallel.DataParallel(self.netF_gen)
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def forward(self, net, state):
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logits_real = self.netF_real(state[self.opt['real']])
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logits_fake = self.netF_gen(state[self.opt['fake']])
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return self.criterion(logits_fake, logits_real)
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class GeneratorGanLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(GeneratorGanLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
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def forward(self, net, state):
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netD = self.env['discriminators'][self.opt['discriminator']]
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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'])
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elif self.opt['gan_type'] == 'crossgan_lrref':
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pred_g_fake = netD(state[self.opt['fake']], state['lq'])
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else:
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pred_g_fake = netD(state[self.opt['fake']])
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return self.criterion(pred_g_fake, True)
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elif self.opt['gan_type'] == 'ragan':
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pred_d_real = netD(state[self.opt['real']]).detach()
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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) +
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self.cri_gan(pred_g_fake - torch.mean(pred_d_real), True)) / 2
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else:
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raise NotImplementedError
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class DiscriminatorGanLoss(ConfigurableLoss):
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def __init__(self, opt, env):
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super(DiscriminatorGanLoss, self).__init__(opt, env)
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self.opt = opt
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self.criterion = GANLoss(opt['gan_type'], 1.0, 0.0).to(env['device'])
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def forward(self, net, state):
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self.metrics = []
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if self.opt['gan_type'] == 'crossgan':
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d_real = net(state[self.opt['real']], state['lq_fullsize_ref'])
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d_fake = net(state[self.opt['fake']].detach(), state['lq_fullsize_ref'])
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mismatched_lq = torch.roll(state['lq_fullsize_ref'], shifts=1, dims=0)
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d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
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d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
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elif self.opt['gan_type'] == 'crossgan_lrref':
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d_real = net(state[self.opt['real']], state['lq'])
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d_fake = net(state[self.opt['fake']].detach(), state['lq'])
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mismatched_lq = torch.roll(state['lq'], shifts=1, dims=0)
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d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
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d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
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else:
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d_real = net(state[self.opt['real']])
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d_fake = net(state[self.opt['fake']].detach())
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self.metrics.append(("d_fake", torch.mean(d_fake)))
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self.metrics.append(("d_real", torch.mean(d_real)))
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if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan', 'crossgan_lrref']:
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l_real = self.criterion(d_real, True)
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l_fake = self.criterion(d_fake, False)
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l_total = l_real + l_fake
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if 'crossgan' in self.opt['gan_type']:
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l_mreal = self.criterion(d_mismatch_real, False)
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l_mfake = self.criterion(d_mismatch_fake, False)
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l_total += l_mreal + l_mfake
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self.metrics.append(("l_mismatch", l_mfake + l_mreal))
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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) +
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self.criterion(d_fake - torch.mean(d_real), False))
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else:
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raise NotImplementedError
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