import os import logging from collections import OrderedDict import torch import torch.nn as nn from torch.optim import lr_scheduler from apex import amp import models.networks as networks from .base_model import BaseModel from models.loss import GANLoss import torchvision.utils as utils from .archs.SPSR_arch import ImageGradient, ImageGradientNoPadding logger = logging.getLogger('base') class SPSRModel(BaseModel): def __init__(self, opt): super(SPSRModel, 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.netD_grad = networks.define_D(opt).to(self.device) # D_grad self.netG.train() self.netD.train() self.netD_grad.train() self.mega_batch_factor = 1 self.load() # load G and D if needed # define losses, optimizer and scheduler if self.is_train: self.mega_batch_factor = train_opt['mega_batch_factor'] # 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) # 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 # Branch_init_iters self.branch_pretrain = train_opt['branch_pretrain'] if train_opt['branch_pretrain'] else 0 self.branch_init_iters = train_opt['branch_init_iters'] if train_opt['branch_init_iters'] else 1 # gradient_pixel_loss if train_opt['gradient_pixel_weight'] > 0: self.cri_pix_grad = nn.MSELoss().to(self.device) self.l_pix_grad_w = train_opt['gradient_pixel_weight'] else: self.cri_pix_grad = None # gradient_gan_loss if train_opt['gradient_gan_weight'] > 0: self.cri_grad_gan = GANLoss(train_opt['gan_type'], 1.0, 0.0).to(self.device) self.l_gan_grad_w = train_opt['gradient_gan_weight'] else: self.cri_grad_gan = None # G_grad pixel loss if train_opt['pixel_branch_weight'] > 0: l_pix_type = train_opt['pixel_branch_criterion'] if l_pix_type == 'l1': self.cri_pix_branch = nn.L1Loss().to(self.device) elif l_pix_type == 'l2': self.cri_pix_branch = nn.MSELoss().to(self.device) else: raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type)) self.l_pix_branch_w = train_opt['pixel_branch_weight'] else: logger.info('Remove G_grad pixel loss.') self.cri_pix_branch = None # 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(): # optimize part of the model if v.requires_grad: optim_params.append(v) else: 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'], 0.999)) 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'], 0.999)) self.optimizers.append(self.optimizer_D) # D_grad wd_D_grad = train_opt['weight_decay_D'] if train_opt['weight_decay_D'] else 0 self.optimizer_D_grad = torch.optim.Adam(self.netD_grad.parameters(), lr=train_opt['lr_D'], \ weight_decay=wd_D, betas=(train_opt['beta1_D'], 0.999)) self.optimizers.append(self.optimizer_D_grad) # AMP [self.netG, self.netD, self.netD_grad], [self.optimizer_G, self.optimizer_D, self.optimizer_D_grad] = \ amp.initialize([self.netG, self.netD, self.netD_grad], [self.optimizer_G, self.optimizer_D, self.optimizer_D_grad], opt_level=self.amp_level, num_losses=3) # 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() self.get_grad = ImageGradient() self.get_grad_nopadding = ImageGradientNoPadding() def feed_data(self, data, need_HR=True): # LR self.var_L = [t.to(self.device) for t in torch.chunk(data['LQ'], chunks=self.mega_batch_factor, dim=0)] if need_HR: # train or val 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.to(self.device), chunks=self.mega_batch_factor, dim=0)] def optimize_parameters(self, step): # Some generators have variants depending on the current step. if hasattr(self.netG.module, "update_for_step"): self.netG.module.update_for_step(step, os.path.join(self.opt['path']['models'], "..")) if hasattr(self.netD.module, "update_for_step"): self.netD.module.update_for_step(step, os.path.join(self.opt['path']['models'], "..")) # G for p in self.netD.parameters(): p.requires_grad = False for p in self.netD_grad.parameters(): p.requires_grad = False if(self.branch_pretrain): if(step < self.branch_init_iters): for k,v in self.netG.named_parameters(): if 'f_' not in k : v.requires_grad=False else: for k,v in self.netG.named_parameters(): if 'f_' not in k : v.requires_grad=True self.optimizer_G.zero_grad() self.fake_H_branch = [] self.fake_H = [] self.grad_LR = [] for var_L, var_H, var_ref in zip(self.var_L, self.var_H, self.var_ref): fake_H_branch, fake_H, grad_LR = self.netG(var_L) self.fake_H_branch.append(fake_H_branch.detach()) self.fake_H.append(fake_H.detach()) self.grad_LR.append(grad_LR.detach()) fake_H_grad = self.get_grad(fake_H) var_H_grad = self.get_grad(var_H) var_ref_grad = self.get_grad(var_ref) var_H_grad_nopadding = self.get_grad_nopadding(var_H) 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(fake_H, var_H) l_g_total += l_g_pix if self.cri_fea: # feature loss real_fea = self.netF(var_H).detach() fake_fea = self.netF(fake_H) l_g_fea = self.l_fea_w * self.cri_fea(fake_fea, real_fea) l_g_total += l_g_fea if self.cri_pix_grad: #gradient pixel loss l_g_pix_grad = self.l_pix_grad_w * self.cri_pix_grad(fake_H_grad, var_H_grad) l_g_total += l_g_pix_grad if self.cri_pix_branch: #branch pixel loss l_g_pix_grad_branch = self.l_pix_branch_w * self.cri_pix_branch(fake_H_branch, var_H_grad_nopadding) l_g_total += l_g_pix_grad_branch if self.l_gan_w > 0: # G gan + cls loss pred_g_fake = self.netD(fake_H) pred_d_real = self.netD(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 if self.cri_grad_gan: # grad G gan + cls loss pred_g_fake_grad = self.netD_grad(fake_H_grad) pred_d_real_grad = self.netD_grad(var_ref_grad).detach() l_g_gan_grad = self.l_gan_grad_w * (self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_g_fake_grad), False) + self.cri_grad_gan(pred_g_fake_grad - torch.mean(pred_d_real_grad), True)) /2 l_g_total += l_g_gan_grad 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() if step % self.D_update_ratio == 0 and step > self.D_init_iters: self.optimizer_G.step() if self.l_gan_w > 0: # D for p in self.netD.parameters(): p.requires_grad = True self.optimizer_D.zero_grad() for var_ref, fake_H in zip(self.var_ref, self.fake_H): pred_d_real = self.netD(var_ref) pred_d_fake = self.netD(fake_H) # 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 /= self.mega_batch_factor with amp.scale_loss(l_d_total, self.optimizer_D, loss_id=1) as l_d_total_scaled: l_d_total_scaled.backward() self.optimizer_D.step() if self.cri_grad_gan: for p in self.netD_grad.parameters(): p.requires_grad = True self.optimizer_D_grad.zero_grad() for var_ref, fake_H in zip(self.var_ref, self.fake_H): fake_H_grad = self.get_grad(fake_H) var_ref_grad = self.get_grad(var_ref) pred_d_real_grad = self.netD_grad(var_ref_grad) pred_d_fake_grad = self.netD_grad(fake_H_grad.detach()) # detach to avoid BP to G l_d_real_grad = self.cri_grad_gan(pred_d_real_grad - torch.mean(pred_d_fake_grad), True) l_d_fake_grad = self.cri_grad_gan(pred_d_fake_grad - torch.mean(pred_d_real_grad), False) l_d_total_grad = (l_d_real_grad + l_d_fake_grad) / 2 l_d_total_grad /= self.mega_batch_factor with amp.scale_loss(l_d_total_grad, self.optimizer_D_grad, loss_id=2) as l_d_total_grad_scaled: l_d_total_grad_scaled.backward() self.optimizer_D_grad.step() # Log sample images from first microbatch. if step % 50 == 0: sample_save_path = os.path.join(self.opt['path']['models'], "..", "temp") os.makedirs(os.path.join(sample_save_path, "hr"), exist_ok=True) os.makedirs(os.path.join(sample_save_path, "lr"), exist_ok=True) os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True) os.makedirs(os.path.join(sample_save_path, "gen_grad"), exist_ok=True) # fed_LQ is not chunked. utils.save_image(self.var_H[0].cpu(), os.path.join(sample_save_path, "hr", "%05i.png" % (step,))) utils.save_image(self.var_L[0].cpu(), os.path.join(sample_save_path, "lr", "%05i.png" % (step,))) utils.save_image(self.fake_H[0].cpu(), os.path.join(sample_save_path, "gen", "%05i.png" % (step,))) utils.save_image(self.grad_LR[0].cpu(), os.path.join(sample_save_path, "gen_grad", "%05i.png" % (step,))) # set log if step % self.D_update_ratio == 0 and step > self.D_init_iters: # G if self.cri_pix: self.add_log_entry('l_g_pix', l_g_pix.item()) if self.cri_fea: self.add_log_entry('l_g_fea', l_g_fea.item()) if self.l_gan_w > 0: self.add_log_entry('l_g_gan', l_g_gan.item()) if self.cri_pix_branch: #branch pixel loss self.add_log_entry('l_g_pix_grad_branch', l_g_pix_grad_branch.item()) if self.l_gan_w > 0: self.add_log_entry('l_d_real', l_d_real.item()) self.add_log_entry('l_d_fake', l_d_fake.item()) self.add_log_entry('l_d_real_grad', l_d_real_grad.item()) self.add_log_entry('l_d_fake_grad', l_d_fake_grad.item()) self.add_log_entry('D_real', torch.mean(pred_d_real.detach())) self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach())) self.add_log_entry('D_real_grad', torch.mean(pred_d_real_grad.detach())) self.add_log_entry('D_fake_grad', torch.mean(pred_d_fake_grad.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 test(self): self.netG.eval() with torch.no_grad(): self.fake_H_branch = [] self.fake_H = [] self.grad_LR = [] for var_L in self.var_L: fake_H_branch, fake_H, grad_LR = self.netG(var_L) self.fake_H_branch.append(fake_H_branch) self.fake_H.append(fake_H) self.grad_LR.append(grad_LR) self.netG.train() # Fetches a summary of the log. def get_current_log(self, step): 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]) # Some generators can do their own metric logging. if hasattr(self.netG.module, "get_debug_values"): return_log.update(self.netG.module.get_debug_values(step)) if hasattr(self.netD.module, "get_debug_values"): return_log.update(self.netD.module.get_debug_values(step)) return return_log def get_current_visuals(self, need_HR=True): out_dict = OrderedDict() out_dict['LR'] = self.var_L[0].float().cpu() out_dict['rlt'] = self.fake_H[0].float().cpu() out_dict['SR_branch'] = self.fake_H_branch[0].float().cpu() out_dict['LR_grad'] = self.grad_LR[0].float().cpu() if need_HR: out_dict['GT'] = self.var_H[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): 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: # Disriminator 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) 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) load_path_D_grad = self.opt['path']['pretrain_model_D_grad'] if self.opt['is_train'] and load_path_D_grad is not None: logger.info('Loading pretrained model for D_grad [{:s}] ...'.format(load_path_D_grad)) self.load_network(load_path_D_grad, self.netD_grad) def compute_fea_loss(self, real, fake): if self.cri_fea is None: return 0 with torch.no_grad(): real = real.unsqueeze(dim=0).to(self.device) fake = fake.unsqueeze(dim=0).to(self.device) real_fea = self.netF(real).detach() fake_fea = self.netF(fake) return self.cri_fea(fake_fea, real_fea).item() def force_restore_swapout(self): pass def save(self, iter_step): self.save_network(self.netG, 'G', iter_step) self.save_network(self.netD, 'D', iter_step) self.save_network(self.netD_grad, 'D_grad', iter_step) # override of load_network that allows loading partial params (like RRDB_PSNR_x4) def load_network(self, load_path, network, strict=True): if isinstance(network, nn.DataParallel): network = network.module pretrained_dict = torch.load(load_path) model_dict = network.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} model_dict.update(pretrained_dict) network.load_state_dict(model_dict)