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 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)