DL-Art-School/codes/models/steps/losses.py
2020-09-22 18:27:52 -06:00

225 lines
9.6 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)
elif type == 'geometric':
return GeometricSimilarityGeneratorLoss(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)
elif name == 'cosine':
return nn.CosineEmbeddingLoss().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']])
if self.opt['criterion'] == 'cosine':
return self.criterion(logits_fake, logits_real, torch.ones(1, device=logits_fake.device))
else:
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
import torchvision
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'])
self.noise = None if 'noise' not in opt.keys() else opt['noise']
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]
if self.noise:
nreal = []
nfake = []
for i, t in enumerate(real):
if isinstance(t, torch.Tensor):
nreal.append(t + torch.randn_like(t) * self.noise)
nfake.append(fake[i] + torch.randn_like(t) * self.noise)
else:
nreal.append(t)
nfake.append(fake[i])
real = nreal
fake = nfake
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
import random
import functools
# Computes a loss created by comparing the output of a generator to the output from the same generator when fed an
# input that has been altered randomly by rotation or flip.
# The "real" parameter to this loss is the actual output of the generator (from an injection point)
# The "fake" parameter is the LR input that produced the "real" parameter when fed through the generator.
class GeometricSimilarityGeneratorLoss(ConfigurableLoss):
def __init__(self, opt, env):
super(GeometricSimilarityGeneratorLoss, self).__init__(opt, env)
self.opt = opt
self.generator = opt['generator']
self.criterion = get_basic_criterion_for_name(opt['criterion'], env['device'])
self.gen_input_for_alteration = opt['input_alteration_index'] if 'input_alteration_index' in opt.keys() else 0
self.gen_output_to_use = opt['generator_output_index'] if 'generator_output_index' in opt.keys() else None
self.detach_fake = opt['detach_fake'] if 'detach_fake' in opt.keys() else False
# Returns a random alteration and its counterpart (that undoes the alteration)
def random_alteration(self):
return random.choice([(functools.partial(torch.flip, dims=(2,)), functools.partial(torch.flip, dims=(2,))),
(functools.partial(torch.flip, dims=(3,)), functools.partial(torch.flip, dims=(3,))),
(functools.partial(torch.rot90, k=1, dims=[2,3]), functools.partial(torch.rot90, k=3, dims=[2,3])),
(functools.partial(torch.rot90, k=2, dims=[2,3]), functools.partial(torch.rot90, k=2, dims=[2,3])),
(functools.partial(torch.rot90, k=3, dims=[2,3]), functools.partial(torch.rot90, k=1, dims=[2,3]))])
def forward(self, net, state):
self.metrics = []
net = self.env['generators'][self.generator] # Get the network from an explicit parameter.
# The <net> parameter is not reliable for generator losses since often they are combined with many networks.
fake = extract_params_from_state(self.opt['fake'], state)
alteration, undo_fn = self.random_alteration()
altered = []
for i, t in enumerate(fake):
if i == self.gen_input_for_alteration:
altered.append(alteration(t))
else:
altered.append(t)
if self.detach_fake:
with torch.no_grad():
upsampled_altered = net(*altered)
else:
upsampled_altered = net(*altered)
if self.gen_output_to_use:
upsampled_altered = upsampled_altered[self.gen_output_to_use]
# Undo alteration on HR image
upsampled_altered = undo_fn(upsampled_altered)
return self.criterion(state[self.opt['real']], upsampled_altered)