DL-Art-School/codes/models/steps/losses.py
James Betker fb595e72a4 Supporting infrastructure in ExtensibleTrainer to train spsr4
Need to be able to train 2 nets in one step: the backbone will be entirely separate
with its own optimizer (for an extremely low LR).

This functionality was already present, just not implemented correctly.
2020-09-11 22:57:06 -06:00

155 lines
5.9 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 == 'interpreted_feature':
return InterpretedFeatureLoss(opt_loss, env)
elif type == 'generator_gan':
return GeneratorGanLoss(opt_loss, env)
elif type == 'discriminator_gan':
return DiscriminatorGanLoss(opt_loss, env)
else:
raise NotImplementedError
# Converts params to a list of tensors extracted from state. Works with list/tuple params as well as scalars.
def extract_params_from_state(params, state):
if isinstance(params, list) or isinstance(params, tuple):
p = [state[r] for r in params]
else:
p = [state[params]]
return p
class ConfigurableLoss(nn.Module):
def __init__(self, opt, env):
super(ConfigurableLoss, self).__init__()
self.opt = opt
self.env = env
self.metrics = []
# net is either a scalar network being trained or a list of networks being trained, depending on the configuration.
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, _, 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'],
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)
def forward(self, _, 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)
# 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, _, state):
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)
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, _, state):
netD = self.env['discriminators'][self.opt['discriminator']]
fake = extract_params_from_state(self.opt['fake'], state)
if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea']:
pred_g_fake = netD(*fake)
return self.criterion(pred_g_fake, True)
elif self.opt['gan_type'] == 'ragan':
real = extract_params_from_state(self.opt['real'], state)
real = [r.detach() for r in real]
pred_d_real = netD(*real).detach()
pred_g_fake = netD(*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 = []
real = extract_params_from_state(self.opt['real'], state)
fake = extract_params_from_state(self.opt['fake'], state)
fake = [f.detach() for f in fake]
d_real = net(*real)
d_fake = net(*fake)
self.metrics.append(("d_fake", torch.mean(d_fake)))
self.metrics.append(("d_real", torch.mean(d_real)))
if self.opt['gan_type'] in ['gan', 'pixgan']:
l_real = self.criterion(d_real, True)
l_fake = self.criterion(d_fake, False)
l_total = l_real + l_fake
return l_total
elif self.opt['gan_type'] == 'ragan':
return (self.criterion(d_real - torch.mean(d_fake), True) +
self.criterion(d_fake - torch.mean(d_real), False))
else:
raise NotImplementedError