import logging
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel, DistributedDataParallel
import models.networks as networks
import models.lr_scheduler as lr_scheduler
from .base_model import BaseModel
from models.modules.loss import GANLoss
logger = logging.getLogger('base')
class SRGANModel(BaseModel):
def __init__(self, opt):
super(SRGANModel, self).__init__(opt)
if opt['dist']:
self.rank = torch.distributed.get_rank()
else:
self.rank = -1 # non dist training
train_opt = opt['train']
# define networks and load pretrained models
self.netG = networks.define_G(opt).to(self.device)
if opt['dist']:
self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()])
else:
self.netG = DataParallel(self.netG)
if self.is_train:
self.netD = networks.define_D(opt).to(self.device)
if opt['dist']:
self.netD = DistributedDataParallel(self.netD,
device_ids=[torch.cuda.current_device()])
else:
self.netD = DataParallel(self.netD)
self.netG.train()
self.netD.train()
# define losses, optimizer and scheduler
if self.is_train:
# G pixel loss
if train_opt['pixel_weight'] > 0:
l_pix_type = train_opt['pixel_criterion']
if l_pix_type == 'l1':
self.cri_pix = nn.L1Loss().to(self.device)
elif l_pix_type == 'l2':
self.cri_pix = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
self.l_pix_w = train_opt['pixel_weight']
else:
logger.info('Remove pixel loss.')
self.cri_pix = None
# G feature loss
if train_opt['feature_weight'] > 0:
l_fea_type = train_opt['feature_criterion']
if l_fea_type == 'l1':
self.cri_fea = nn.L1Loss().to(self.device)
elif l_fea_type == 'l2':
self.cri_fea = nn.MSELoss().to(self.device)
else:
raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_fea_type))
self.l_fea_w = train_opt['feature_weight']
else:
logger.info('Remove feature loss.')
self.cri_fea = None
if self.cri_fea: # load VGG perceptual loss
self.netF = networks.define_F(opt, use_bn=False).to(self.device)
if opt['dist']:
self.netF = DistributedDataParallel(self.netF,
device_ids=[torch.cuda.current_device()])
else:
self.netF = DataParallel(self.netF)
# GD gan loss
self.cri_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device)
self.l_gan_w = train_opt['gan_weight']
# D_update_ratio and D_init_iters
self.D_update_ratio = train_opt['D_update_ratio'] if train_opt['D_update_ratio'] else 1
self.D_init_iters = train_opt['D_init_iters'] if train_opt['D_init_iters'] else 0
# optimizers
# G
wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
optim_params = []
for k, v in self.netG.named_parameters(): # can optimize for a part of the model
if v.requires_grad:
optim_params.append(v)
else:
if self.rank <= 0:
logger.warning('Params [{:s}] will not optimize.'.format(k))
self.optimizer_G = torch.optim.Adam(optim_params, lr=train_opt['lr_G'],
weight_decay=wd_G,
betas=(train_opt['beta1_G'], train_opt['beta2_G']))
self.optimizers.append(self.optimizer_G)
# D
wd_D = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0
self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=train_opt['lr_D'],
weight_decay=wd_D,
betas=(train_opt['beta1_D'], train_opt['beta2_D']))
self.optimizers.append(self.optimizer_D)
# schedulers
if train_opt['lr_scheme'] == 'MultiStepLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepLR_Restart(optimizer, train_opt['lr_steps'],
restarts=train_opt['restarts'],
weights=train_opt['restart_weights'],
gamma=train_opt['lr_gamma'],
clear_state=train_opt['clear_state']))
elif train_opt['lr_scheme'] == 'CosineAnnealingLR_Restart':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingLR_Restart(
optimizer, train_opt['T_period'], eta_min=train_opt['eta_min'],
restarts=train_opt['restarts'], weights=train_opt['restart_weights']))
else:
raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
self.log_dict = OrderedDict()
self.print_network() # print network
self.load() # load G and D if needed
def feed_data(self, data, need_GT=True):
self.var_L = data['LQ'].to(self.device) # LQ
if need_GT:
self.var_H = data['GT'].to(self.device) # GT
input_ref = data['ref'] if 'ref' in data else data['GT']
self.var_ref = input_ref.to(self.device)
def optimize_parameters(self, step):
# G
for p in self.netD.parameters():
p.requires_grad = False
self.optimizer_G.zero_grad()
self.fake_H = self.netG(self.var_L)
l_g_total = 0
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix: # pixel loss
l_g_pix = self.l_pix_w * self.cri_pix(self.fake_H, self.var_H)
l_g_total += l_g_pix
if self.cri_fea: # feature loss
real_fea = self.netF(self.var_H).detach()
fake_fea = self.netF(self.fake_H)
l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea)
l_g_total += l_g_fea
pred_g_fake = self.netD(self.fake_H)
if self.opt['train']['gan_type'] == 'gan':
l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
elif self.opt['train']['gan_type'] == 'ragan':
pred_d_real = self.netD(self.var_ref).detach()
l_g_gan = self.l_gan_w * (
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
l_g_total += l_g_gan
l_g_total.backward()
self.optimizer_G.step()
# D
for p in self.netD.parameters():
p.requires_grad = True
self.optimizer_D.zero_grad()
l_d_total = 0
pred_d_real = self.netD(self.var_ref)
pred_d_fake = self.netD(self.fake_H.detach()) # detach to avoid BP to G
if self.opt['train']['gan_type'] == 'gan':
l_d_real = self.cri_gan(pred_d_real, True)
l_d_fake = self.cri_gan(pred_d_fake, False)
l_d_total = l_d_real + l_d_fake
elif self.opt['train']['gan_type'] == 'ragan':
l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True)
l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real), False)
l_d_total = (l_d_real + l_d_fake) / 2
l_d_total.backward()
self.optimizer_D.step()
# set log
if step % self.D_update_ratio == 0 and step > self.D_init_iters:
if self.cri_pix:
self.log_dict['l_g_pix'] = l_g_pix.item()
if self.cri_fea:
self.log_dict['l_g_fea'] = l_g_fea.item()
self.log_dict['l_g_gan'] = l_g_gan.item()
self.log_dict['l_d_real'] = l_d_real.item()
self.log_dict['l_d_fake'] = l_d_fake.item()
self.log_dict['D_real'] = torch.mean(pred_d_real.detach())
self.log_dict['D_fake'] = torch.mean(pred_d_fake.detach())
def test(self):
self.netG.eval()
with torch.no_grad():
self.fake_H = self.netG(self.var_L)
self.netG.train()
def get_current_log(self):
return self.log_dict
def get_current_visuals(self, need_GT=True):
out_dict = OrderedDict()
out_dict['LQ'] = self.var_L.detach()[0].float().cpu()
out_dict['SR'] = self.fake_H.detach()[0].float().cpu()
if need_GT:
out_dict['GT'] = self.var_H.detach()[0].float().cpu()
return out_dict
def print_network(self):
# Generator
s, n = self.get_network_description(self.netG)
if isinstance(self.netG, nn.DataParallel) or isinstance(self.netG, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
self.netG.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netG.__class__.__name__)
if self.rank <= 0:
logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
logger.info(s)
if self.is_train:
# Discriminator
s, n = self.get_network_description(self.netD)
if isinstance(self.netD, nn.DataParallel) or isinstance(self.netD,
DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
self.netD.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netD.__class__.__name__)
if self.rank <= 0:
logger.info('Network D structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
if self.cri_fea: # F, Perceptual Network
s, n = self.get_network_description(self.netF)
if isinstance(self.netF, nn.DataParallel) or isinstance(
self.netF, DistributedDataParallel):
net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
self.netF.module.__class__.__name__)
else:
net_struc_str = '{}'.format(self.netF.__class__.__name__)
if self.rank <= 0:
logger.info('Network F structure: {}, with parameters: {:,d}'.format(
net_struc_str, n))
logger.info(s)
def load(self):
load_path_G = self.opt['path']['pretrain_model_G']
if load_path_G is not None:
logger.info('Loading model for G [{:s}] ...'.format(load_path_G))
self.load_network(load_path_G, self.netG, self.opt['path']['strict_load'])
load_path_D = self.opt['path']['pretrain_model_D']
if self.opt['is_train'] and load_path_D is not None:
logger.info('Loading model for D [{:s}] ...'.format(load_path_D))
self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
def save(self, iter_step):
self.save_network(self.netG, 'G', iter_step)
self.save_network(self.netD, 'D', iter_step)
