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 models.base_model import BaseModel from models.loss import GANLoss from apex import amp import torch.nn.functional as F import torchvision.utils as utils import os 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 self.is_train: self.netD = networks.define_D(opt).to(self.device) # define losses, optimizer and scheduler if self.is_train: self.mega_batch_factor = train_opt['mega_batch_factor'] if self.mega_batch_factor is None: self.mega_batch_factor = 1 # 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'] self.l_fea_w_decay = train_opt['feature_weight_decay'] self.l_fea_w_decay_steps = train_opt['feature_weight_decay_steps'] self.l_fea_w_minimum = train_opt['feature_weight_minimum'] 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) # AMP [self.netG, self.netD], [self.optimizer_G, self.optimizer_D] = \ amp.initialize([self.netG, self.netD], [self.optimizer_G, self.optimizer_D], opt_level=self.amp_level, num_losses=3) # DataParallel if opt['dist']: self.netG = DistributedDataParallel(self.netG, device_ids=[torch.cuda.current_device()]) else: self.netG = DataParallel(self.netG) if self.is_train: 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() # 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 = torch.chunk(data['LQ'], chunks=self.mega_batch_factor, dim=0) # LQ if need_GT: self.var_H = [t.to(self.device) for t in torch.chunk(data['GT'], chunks=self.mega_batch_factor, dim=0)] input_ref = data['ref'] if 'ref' in data else data['GT'] self.var_ref = [t.to(self.device) for t in torch.chunk(input_ref, chunks=self.mega_batch_factor, dim=0)] self.pix = [t.to(self.device) for t in torch.chunk(data['PIX'], chunks=self.mega_batch_factor, dim=0)] def optimize_parameters(self, step): # G for p in self.netD.parameters(): p.requires_grad = False if step > self.D_init_iters: self.optimizer_G.zero_grad() # Turning off G-grad is required to enable mega-batching and D_update_ratio to work together for some reason. if step % self.D_update_ratio == 0 and step > self.D_init_iters: for p in self.netG.parameters(): p.requires_grad = True else: for p in self.netG.parameters(): p.requires_grad = False self.fake_GenOut = [] for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix): fake_GenOut = self.netG(var_L) # Extract the image output. For generators that output skip-through connections, the master output is always # the first element of the tuple. if isinstance(fake_GenOut, tuple): gen_img = fake_GenOut[0] # TODO: Fix this. self.fake_GenOut.append((fake_GenOut[0].detach(), fake_GenOut[1].detach(), fake_GenOut[2].detach())) else: gen_img = fake_GenOut self.fake_GenOut.append(fake_GenOut.detach()) 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(gen_img, pix) l_g_total += l_g_pix if self.cri_fea: # feature loss real_fea = self.netF(pix).detach() fake_fea = self.netF(gen_img) l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea) l_g_total += l_g_fea # Decay the influence of the feature loss. As the model trains, the GAN will play a stronger role # in the resultant image. if step % self.l_fea_w_decay_steps == 0: self.l_fea_w = max(self.l_fea_w_minimum, self.l_fea_w * self.l_fea_w_decay) if self.opt['train']['gan_type'] == 'gan': pred_g_fake = self.netD(fake_GenOut) 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(var_ref).detach() pred_g_fake = self.netD(fake_GenOut) 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 # Scale the loss down by the batch factor. l_g_total = l_g_total / self.mega_batch_factor with amp.scale_loss(l_g_total, self.optimizer_G, loss_id=0) as l_g_total_scaled: l_g_total_scaled.backward() self.optimizer_G.step() # D for p in self.netD.parameters(): p.requires_grad = True # Convert var_ref to have the same output format as the generator. This generally means interpolating the # HR images to have the same output dimensions as each generator skip connection. if isinstance(self.fake_GenOut[0], tuple): var_ref_skips = [] for ref, hi_res in zip(self.var_ref, self.var_H): var_ref_skips.append((ref,) + self.create_artificial_skips(hi_res)) else: var_ref_skips = self.var_ref self.optimizer_D.zero_grad() for var_L, var_H, var_ref, pix, fake_H in zip(self.var_L, self.var_H, var_ref_skips, self.pix, self.fake_GenOut): if self.opt['train']['gan_type'] == 'gan': # need to forward and backward separately, since batch norm statistics differ # real pred_d_real = self.netD(var_ref) l_d_real = self.cri_gan(pred_d_real, True) / self.mega_batch_factor with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled: l_d_real_scaled.backward() # fake pred_d_fake = self.netD(fake_H) l_d_fake = self.cri_gan(pred_d_fake, False) / self.mega_batch_factor with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled: l_d_fake_scaled.backward() elif self.opt['train']['gan_type'] == 'ragan': # pred_d_real = self.netD(var_ref) # pred_d_fake = self.netD(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(fake_H).detach() pred_d_real = self.netD(var_ref) l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5 / self.mega_batch_factor with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled: l_d_real_scaled.backward() pred_d_fake = self.netD(fake_H) l_d_fake = self.cri_gan(pred_d_fake - torch.mean(pred_d_real.detach()), False) * 0.5 / self.mega_batch_factor with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled: l_d_fake_scaled.backward() self.optimizer_D.step() # Log sample images from first microbatch. if step % 50 == 0: os.makedirs("temp/hr", exist_ok=True) os.makedirs("temp/lr", exist_ok=True) os.makedirs("temp/gen", exist_ok=True) os.makedirs("temp/pix", exist_ok=True) multi_gen = False if isinstance(self.fake_GenOut[0], tuple): os.makedirs("temp/genlr", exist_ok=True) os.makedirs("temp/genmr", exist_ok=True) os.makedirs("temp/ref", exist_ok=True) multi_gen = True for i in range(self.mega_batch_factor): utils.save_image(self.var_H[i].cpu().detach(), os.path.join("temp/hr", "%05i_%02i.png" % (step, i))) utils.save_image(self.var_L[i].cpu().detach(), os.path.join("temp/lr", "%05i_%02i.png" % (step, i))) utils.save_image(self.pix[i].cpu().detach(), os.path.join("temp/pix", "%05i_%02i.png" % (step, i))) if multi_gen: utils.save_image(self.fake_GenOut[i][0].cpu().detach(), os.path.join("temp/gen", "%05i_%02i.png" % (step, i))) utils.save_image(self.fake_GenOut[i][1].cpu().detach(), os.path.join("temp/genmr", "%05i_%02i.png" % (step, i))) utils.save_image(self.fake_GenOut[i][2].cpu().detach(), os.path.join("temp/genlr", "%05i_%02i.png" % (step, i))) utils.save_image(var_ref_skips[i][1].cpu().detach(), os.path.join("temp/ref", "med_%05i_%02i.png" % (step, i))) utils.save_image(var_ref_skips[i][2].cpu().detach(), os.path.join("temp/ref", "low_%05i_%02i.png" % (step, i))) else: utils.save_image(self.fake_GenOut[i].cpu().detach(), os.path.join("temp/gen", "%05i_%02i.png" % (step, i))) # set log TODO(handle mega-batches?) if step % self.D_update_ratio == 0 and step > self.D_init_iters: if self.cri_pix: self.add_log_entry('l_g_pix', l_g_pix.item()) if self.cri_fea: self.add_log_entry('feature_weight', self.l_fea_w) self.add_log_entry('l_g_fea', l_g_fea.item()) self.add_log_entry('l_g_gan', l_g_gan.item()) self.add_log_entry('l_g_total', l_g_total.item() * self.mega_batch_factor) self.add_log_entry('l_d_real', l_d_real.item() * self.mega_batch_factor) self.add_log_entry('l_d_fake', l_d_fake.item() * self.mega_batch_factor) self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach())) # Allows the log to serve as an easy-to-use rotating buffer. def add_log_entry(self, key, value): key_it = "%s_it" % (key,) log_rotating_buffer_size = 50 if key not in self.log_dict.keys(): self.log_dict[key] = [] self.log_dict[key_it] = 0 if len(self.log_dict[key]) < log_rotating_buffer_size: self.log_dict[key].append(value) else: self.log_dict[key][self.log_dict[key_it] % log_rotating_buffer_size] = value self.log_dict[key_it] += 1 def create_artificial_skips(self, truth_img): med_skip = F.interpolate(truth_img, scale_factor=.5) lo_skip = F.interpolate(truth_img, scale_factor=.25) return med_skip, lo_skip def test(self): self.netG.eval() with torch.no_grad(): self.fake_GenOut = [self.netG(self.var_L[0])] self.netG.train() # Fetches a summary of the log. def get_current_log(self): return_log = {} for k in self.log_dict.keys(): if not isinstance(self.log_dict[k], list): continue return_log[k] = sum(self.log_dict[k]) / len(self.log_dict[k]) return return_log def get_current_visuals(self, need_GT=True): out_dict = OrderedDict() out_dict['LQ'] = self.var_L[0].detach()[0].float().cpu() gen_batch = self.fake_GenOut[0] if isinstance(gen_batch, tuple): gen_batch = gen_batch[0] out_dict['rlt'] = gen_batch.detach()[0].float().cpu() if need_GT: out_dict['GT'] = self.var_H[0].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)