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model_gan.py
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model_gan.py
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from collections import OrderedDict
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
from torch.optim import Adam
from torch.nn.parallel import DataParallel # , DistributedDataParallel
from models.select_network import define_G, define_D, define_F
from models.model_base import ModelBase
from models.loss import GANLoss
from models.loss_ssim import SSIMLoss
class ModelGAN(ModelBase):
"""Train with pixel-VGG-GAN loss"""
def __init__(self, opt):
super(ModelGAN, self).__init__(opt)
# ------------------------------------
# define network
# ------------------------------------
self.netG = define_G(opt).to(self.device)
self.netG = DataParallel(self.netG)
if self.is_train:
self.netF = define_F(opt).to(self.device)
self.netD = define_D(opt).to(self.device)
self.netF = DataParallel(self.netF)
self.netD = DataParallel(self.netD)
"""
# ----------------------------------------
# Preparation before training with data
# Save model during training
# ----------------------------------------
"""
# ----------------------------------------
# initialize training
# ----------------------------------------
def init_train(self):
self.opt_train = self.opt['train'] # training option
self.load() # load model
self.netG.train() # set training mode,for BN
self.netD.train() # set training mode,for BN
self.define_loss() # define loss
self.define_optimizer() # define optimizer
self.define_scheduler() # define scheduler
self.log_dict = OrderedDict() # log
# ----------------------------------------
# load pre-trained G and D model
# ----------------------------------------
def load(self):
load_path_G = self.opt['path']['pretrained_netG']
if load_path_G is not None:
print('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG)
load_path_D = self.opt['path']['pretrained_netD']
if self.opt['is_train'] and load_path_D is not None:
print('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD)
# ----------------------------------------
# save model
# ----------------------------------------
def save(self, iter_label):
self.save_network(self.save_dir, self.netG, 'G', iter_label)
self.save_network(self.save_dir, self.netD, 'D', iter_label)
# ----------------------------------------
# define loss
# ----------------------------------------
def define_loss(self):
# ------------------------------------
# G_loss
# ------------------------------------
if self.opt_train['G_lossfn_weight'] > 0:
G_lossfn_type = self.opt_train['G_lossfn_type']
if G_lossfn_type == 'l1':
self.G_lossfn = nn.L1Loss().to(self.device)
elif G_lossfn_type == 'l2':
self.G_lossfn = nn.MSELoss().to(self.device)
elif G_lossfn_type == 'l2sum':
self.G_lossfn = nn.MSELoss(reduction='sum').to(self.device)
elif G_lossfn_type == 'ssim':
self.G_lossfn = SSIMLoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] is not found.'.format(G_lossfn_type))
self.G_lossfn_weight = self.opt_train['G_lossfn_weight']
else:
print('Do not use pixel loss.')
self.G_lossfn = None
# ------------------------------------
# F_loss
# ------------------------------------
if self.opt_train['F_lossfn_weight'] > 0:
F_lossfn_type = self.opt_train['F_lossfn_type']
if F_lossfn_type == 'l1':
self.F_lossfn = nn.L1Loss().to(self.device)
elif F_lossfn_type == 'l2':
self.F_lossfn = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(F_lossfn_type))
self.F_lossfn_weight = self.opt_train['F_lossfn_weight']
# self.netF = define_F(self.opt, use_bn=False).to(self.device)
else:
print('Do not use feature loss.')
self.F_lossfn = None
# ------------------------------------
# D_loss
# ------------------------------------
self.D_lossfn = GANLoss(self.opt_train['gan_type'], 1.0, 0.0).to(self.device)
self.D_lossfn_weight = self.opt_train['D_lossfn_weight']
self.D_update_ratio = self.opt_train['D_update_ratio'] if self.opt_train['D_update_ratio'] else 1
self.D_init_iters = self.opt_train['D_init_iters'] if self.opt_train['D_init_iters'] else 0
# ----------------------------------------
# define optimizer, G and D
# ----------------------------------------
def define_optimizer(self):
G_optim_params = []
for k, v in self.netG.named_parameters():
if v.requires_grad:
G_optim_params.append(v)
else:
print('Params [{:s}] will not optimize.'.format(k))
self.G_optimizer = Adam(G_optim_params, lr=self.opt_train['G_optimizer_lr'], weight_decay=0)
self.D_optimizer = Adam(self.netD.parameters(), lr=self.opt_train['D_optimizer_lr'], weight_decay=0)
# ----------------------------------------
# define scheduler, only "MultiStepLR"
# ----------------------------------------
def define_scheduler(self):
self.schedulers.append(lr_scheduler.MultiStepLR(self.G_optimizer,
self.opt_train['G_scheduler_milestones'],
self.opt_train['G_scheduler_gamma']
))
self.schedulers.append(lr_scheduler.MultiStepLR(self.D_optimizer,
self.opt_train['D_scheduler_milestones'],
self.opt_train['D_scheduler_gamma']
))
"""
# ----------------------------------------
# Optimization during training with data
# Testing/evaluation
# ----------------------------------------
"""
# ----------------------------------------
# feed L/H data
# ----------------------------------------
def feed_data(self, data, need_H=True):
self.L = data['L'].to(self.device)
if need_H:
self.H = data['H'].to(self.device)
input_ref = data['ref'] if 'ref' in data else data['H']
self.var_ref = input_ref.to(self.device)
# ----------------------------------------
# update parameters and get loss
# ----------------------------------------
def optimize_parameters(self, current_step):
# ------------------------------------
# optimize G
# ------------------------------------
for p in self.netD.parameters():
p.requires_grad = False
self.G_optimizer.zero_grad()
self.E = self.netG(self.L)
loss_G_total = 0
if current_step % self.D_update_ratio == 0 and current_step > self.D_init_iters: # updata D first
if self.opt_train['G_lossfn_weight'] > 0:
G_loss = self.G_lossfn_weight * self.G_lossfn(self.E, self.H)
loss_G_total += G_loss # 1) pixel loss
if self.opt_train['F_lossfn_weight'] > 0:
real_fea = self.netF(self.H).detach()
fake_fea = self.netF(self.E)
F_loss = self.F_lossfn_weight * self.F_lossfn(fake_fea, real_fea)
loss_G_total += F_loss # 2) VGG feature loss
pred_g_fake = self.netD(self.E)
if self.opt['train']['gan_type'] == 'gan':
D_loss = self.D_lossfn_weight * self.D_lossfn(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
pred_d_real = self.netD(self.var_ref).detach()
D_loss = self.D_lossfn_weight * (
self.D_lossfn(pred_d_real - torch.mean(pred_g_fake), False) +
self.D_lossfn(pred_g_fake - torch.mean(pred_d_real), True)) / 2
loss_G_total += D_loss # 3) GAN loss
loss_G_total.backward()
self.G_optimizer.step()
# ------------------------------------
# optimize D
# ------------------------------------
for p in self.netD.parameters():
p.requires_grad = True
self.D_optimizer.zero_grad()
loss_D_total = 0
pred_d_real = self.netD(self.var_ref) # 1) real data
pred_d_fake = self.netD(self.E.detach()) # 2) fake data, detach to avoid BP to G
if self.opt['train']['gan_type'] == 'gan':
l_d_real = self.D_lossfn(pred_d_real, True)
l_d_fake = self.D_lossfn(pred_d_fake, False)
loss_D_total = l_d_real + l_d_fake
elif self.opt['train']['gan_type'] == 'ragan':
l_d_real = self.D_lossfn(pred_d_real - torch.mean(pred_d_fake), True)
l_d_fake = self.D_lossfn(pred_d_fake - torch.mean(pred_d_real), False)
loss_D_total = (l_d_real + l_d_fake) / 2
loss_D_total.backward()
self.D_optimizer.step()
# ------------------------------------
# record log
# ------------------------------------
if current_step % self.D_update_ratio == 0 and current_step > self.D_init_iters:
if self.opt_train['G_lossfn_weight'] > 0:
self.log_dict['G_loss'] = G_loss.item() # /self.E.size()[0]
if self.opt_train['F_lossfn_weight'] > 0:
self.log_dict['F_loss'] = F_loss.item() # /self.E.size()[0]
self.log_dict['D_loss'] = D_loss.item() # /self.E.size()[0]
self.log_dict['l_d_real'] = l_d_real.item() # /self.E.size()[0]
self.log_dict['l_d_fake'] = l_d_fake.item() # /self.E.size()[0]
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
# ----------------------------------------
# test and inference
# ----------------------------------------
def test(self):
self.netG.eval()
with torch.no_grad():
self.E = self.netG(self.L)
self.netG.train()
# ----------------------------------------
# get log_dict
# ----------------------------------------
def current_log(self):
return self.log_dict
# ----------------------------------------
# get L, E, H images
# ----------------------------------------
def current_visuals(self, need_H=True):
out_dict = OrderedDict()
out_dict['L'] = self.L.detach()[0].float().cpu()
out_dict['E'] = self.E.detach()[0].float().cpu()
if need_H:
out_dict['H'] = self.H.detach()[0].float().cpu()
return out_dict
"""
# ----------------------------------------
# Information of netG, netD and netF
# ----------------------------------------
"""
# ----------------------------------------
# print network
# ----------------------------------------
def print_network(self):
msg = self.describe_network(self.netG)
print(msg)
if self.is_train:
msg = self.describe_network(self.netD)
print(msg)
if self.opt_train['F_lossfn_weight'] > 0:
msg = self.describe_network(self.netF)
print(msg)
# ----------------------------------------
# print params
# ----------------------------------------
def print_params(self):
msg = self.describe_params(self.netG)
print(msg)
# ----------------------------------------
# network information
# ----------------------------------------
def info_network(self):
msg = self.describe_network(self.netG)
if self.is_train:
msg += self.describe_network(self.netD)
if self.opt_train['F_lossfn_weight'] > 0:
msg += self.describe_network(self.netF)
return msg
# ----------------------------------------
# params information
# ----------------------------------------
def info_params(self):
msg = self.describe_params(self.netG)
return msg