text: 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.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']:
                      pass  # do not need to use DistributedDataParallel for netF
                  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
  
              if self.opt['train']['gan_type'] == 'gan':
                  pred_g_fake = self.netD(self.fake_H)
                  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()
                  pred_g_fake = self.netD(self.fake_H)
                  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()
          if self.opt['train']['gan_type'] == 'gan':
              # need to forward and backward separately, since batch norm statistics differ
              # real
              pred_d_real = self.netD(self.var_ref)
              l_d_real = self.cri_gan(pred_d_real, True)
              l_d_real.backward()
              # fake
              pred_d_fake = self.netD(self.fake_H.detach())  # detach to avoid BP to G
              l_d_fake = self.cri_gan(pred_d_fake, False)
              l_d_fake.backward()
          elif self.opt['train']['gan_type'] == 'ragan':
              # pred_d_real = self.netD(self.var_ref)
              # pred_d_fake = self.netD(self.fake_H.detach())  # detach to avoid BP to G
              # 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()
              pred_d_fake = self.netD(self.fake_H.detach()).detach()
              pred_d_real = self.netD(self.var_ref)
              l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5
              l_d_real.backward()
              pred_d_fake = self.netD(self.fake_H.detach())
              l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5
              l_d_fake.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['rlt'] = self.fake_H.detach()[0].float().cpu()
          if need_GT:
              out_dict['GT'] = self.var_H.detach()[0].float().cpu()
          return out_dict
  
      def get_current_visuals_all(self, need_GT=True):
          out_dict = OrderedDict()
          out_dict['LQ'] = self.var_L.detach().float().cpu()
          out_dict['rlt'] = self.fake_H.detach().float().cpu()
          if need_GT:
              out_dict['GT'] = self.var_H.detach().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)
  