text: import logging
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  from torch.optim import lr_scheduler
  
  import models.networks as networks
  from .base_model import BaseModel
  from models.modules.loss import GANLoss, GradientPenaltyLoss
  
  logger = logging.getLogger('base')
  
  
  class SFTGAN_ACD_Model(BaseModel):
      def __init__(self, opt):
          super(SFTGAN_ACD_Model, self).__init__(opt)
          train_opt = opt['train']
  
          # define networks and load pretrained models
          self.netG = networks.define_G(opt).to(self.device)  # G
          if self.is_train:
              self.netD = networks.define_D(opt).to(self.device)  # D
              self.netG.train()
              self.netD.train()
          self.load()  # load G and D if needed
  
          # 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:
                  logging.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:
                  logging.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)
  
              # 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 are for WGAN
              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
  
              if train_opt['gan_type'] == 'wgan-gp':
                  self.random_pt = torch.Tensor(1, 1, 1, 1).to(self.device)
                  # gradient penalty loss
                  self.cri_gp = GradientPenaltyLoss(device=self.device).to(self.device)
                  self.l_gp_w = train_opt['gp_weigth']
  
              # D cls loss
              self.cri_ce = nn.CrossEntropyLoss(ignore_index=0).to(self.device)
              # ignore background, since bg images may conflict with other classes
  
              # optimizers
              # G
              wd_G = train_opt['weight_decay_G'] if train_opt['weight_decay_G'] else 0
              optim_params_SFT = []
              optim_params_other = []
              for k, v in self.netG.named_parameters():  # can optimize for a part of the model
                  if 'SFT' in k or 'Cond' in k:
                      optim_params_SFT.append(v)
                  else:
                      optim_params_other.append(v)
              self.optimizer_G_SFT = torch.optim.Adam(optim_params_SFT, lr=train_opt['lr_G'] * 5,
                                                      weight_decay=wd_G,
                                                      betas=(train_opt['beta1_G'], 0.999))
              self.optimizer_G_other = torch.optim.Adam(optim_params_other, lr=train_opt['lr_G'],
                                                        weight_decay=wd_G,
                                                        betas=(train_opt['beta1_G'], 0.999))
              self.optimizers.append(self.optimizer_G_SFT)
              self.optimizers.append(self.optimizer_G_other)
              # 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'], 0.999))
              self.optimizers.append(self.optimizer_D)
  
              # schedulers
              if train_opt['lr_scheme'] == 'MultiStepLR':
                  for optimizer in self.optimizers:
                      self.schedulers.append(
                          lr_scheduler.MultiStepLR(optimizer, train_opt['lr_steps'],
                                                   train_opt['lr_gamma']))
              else:
                  raise NotImplementedError('MultiStepLR learning rate scheme is enough.')
  
              self.log_dict = OrderedDict()
          # print network
          self.print_network()
  
      def feed_data(self, data, need_GT=True):
          # LR
          self.var_L = data['LR'].to(self.device)
          # seg
          self.var_seg = data['seg'].to(self.device)
          # category
          self.var_cat = data['category'].long().to(self.device)
  
          if need_GT:  # train or val
              self.var_H = data['GT'].to(self.device)
  
      def optimize_parameters(self, step):
          # G
          self.optimizer_G_SFT.zero_grad()
          self.optimizer_G_other.zero_grad()
          self.fake_H = self.netG((self.var_L, self.var_seg))
  
          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 + l_g_total
              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 + l_g_total
              # G gan + cls loss
              pred_g_fake, cls_g_fake = self.netD(self.fake_H)
              l_g_gan = self.l_gan_w * self.cri_gan(pred_g_fake, True)
              l_g_cls = self.l_gan_w * self.cri_ce(cls_g_fake, self.var_cat)
              l_g_total = l_g_gan + l_g_total
              l_g_total = l_g_cls + l_g_total
  
              l_g_total.backward()
              self.optimizer_G_SFT.step()
          if step > 20000:
              self.optimizer_G_other.step()
  
          # D
          self.optimizer_D.zero_grad()
          l_d_total = 0
          # real data
          pred_d_real, cls_d_real = self.netD(self.var_H)
          l_d_real = self.cri_gan(pred_d_real, True)
          l_d_cls_real = self.cri_ce(cls_d_real, self.var_cat)
          # fake data
          pred_d_fake, cls_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_cls_fake = self.cri_ce(cls_d_fake, self.var_cat)
  
          l_d_total = l_d_real + l_d_cls_real + l_d_fake + l_d_cls_fake
  
          if self.opt['train']['gan_type'] == 'wgan-gp':
              batch_size = self.var_H.size(0)
              if self.random_pt.size(0) != batch_size:
                  self.random_pt.resize_(batch_size, 1, 1, 1)
              self.random_pt.uniform_()  # Draw random interpolation points
              interp = self.random_pt * self.fake_H.detach() + (1 - self.random_pt) * self.var_H
              interp.requires_grad = True
              interp_crit, _ = self.netD(interp)
              l_d_gp = self.l_gp_w * self.cri_gp(interp, interp_crit)  # maybe wrong in cls?
              l_d_total = l_d_gp + l_g_total
  
          l_d_total.backward()
          self.optimizer_D.step()
  
          # set log
          if step % self.D_update_ratio == 0 and step > self.D_init_iters:
              # G
              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()
          # D
          self.log_dict['l_d_real'] = l_d_real.item()
          self.log_dict['l_d_fake'] = l_d_fake.item()
          self.log_dict['l_d_cls_real'] = l_d_cls_real.item()
          self.log_dict['l_d_cls_fake'] = l_d_cls_fake.item()
          if self.opt['train']['gan_type'] == 'wgan-gp':
              self.log_dict['l_d_gp'] = l_d_gp.item()
          # D outputs
          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.var_seg))
          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['LR'] = 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):
          # G
          s, n = self.get_network_description(self.netG)
          if isinstance(self.netG, nn.DataParallel):
              net_struc_str = '{} - {}'.format(self.netG.__class__.__name__,
                                               self.netG.module.__class__.__name__)
          else:
              net_struc_str = '{}'.format(self.netG.__class__.__name__)
  
          logger.info('Network G structure: {}, with parameters: {:,d}'.format(net_struc_str, n))
          logger.info(s)
          if self.is_train:
              # D
              s, n = self.get_network_description(self.netD)
              if isinstance(self.netD, nn.DataParallel):
                  net_struc_str = '{} - {}'.format(self.netD.__class__.__name__,
                                                   self.netD.module.__class__.__name__)
              else:
                  net_struc_str = '{}'.format(self.netD.__class__.__name__)
  
              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):
                      net_struc_str = '{} - {}'.format(self.netF.__class__.__name__,
                                                       self.netF.module.__class__.__name__)
                  else:
                      net_struc_str = '{}'.format(self.netF.__class__.__name__)
  
                  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 pretrained 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 pretrained model for D [{:s}] ...'.format(load_path_D))
              self.load_network(load_path_D, self.netD, self.opt['path']['strict_load'])
  
      def save(self, epoch):
          self.save_network(self.netG, 'G', epoch)
          self.save_network(self.netD, 'D', epoch)
  