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 glob import random 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) if 'network_C' in opt.keys(): self.netC = networks.define_G(opt, net_key='network_C').to(self.device) # The corruptor net is fixed. Lock 'her down. self.netC.eval() for p in self.netC.parameters(): p.requires_grad = True else: self.netC = None # 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 self.G_warmup = train_opt['G_warmup'] if train_opt['G_warmup'] else 0 self.D_noise_theta = train_opt['D_noise_theta_init'] if train_opt['D_noise_theta_init'] else 0 self.D_noise_final = train_opt['D_noise_final_it'] if train_opt['D_noise_final_it'] else 0 self.D_noise_theta_floor = train_opt['D_noise_theta_floor'] if train_opt['D_noise_theta_floor'] else 0 self.corruptor_swapout_steps = train_opt['corruptor_swapout_steps'] if train_opt['corruptor_swapout_steps'] else 500 self.corruptor_usage_prob = train_opt['corruptor_usage_probability'] if train_opt['corruptor_usage_probability'] else .5 # 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'], force_lr=train_opt['force_lr'])) 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() # Swapout params self.swapout_G_freq = train_opt['swapout_G_freq'] if train_opt['swapout_G_freq'] else 0 self.swapout_G_duration = 0 self.swapout_D_freq = train_opt['swapout_D_freq'] if train_opt['swapout_D_freq'] else 0 self.swapout_D_duration = 0 self.swapout_duration = train_opt['swapout_duration'] if train_opt['swapout_duration'] else 0 self.print_network() # print network self.load() # load G and D if needed self.load_random_corruptor() def feed_data(self, data, need_GT=True): _profile = True if _profile: from time import time _t = time() # Corrupt the data with the given corruptor, if specified. self.fed_LQ = data['LQ'].to(self.device) if self.netC and random.random() < self.corruptor_usage_prob: with torch.no_grad(): corrupted_L = self.netC(self.fed_LQ)[0].detach() else: corrupted_L = self.fed_LQ self.var_L = torch.chunk(corrupted_L, chunks=self.mega_batch_factor, dim=0) 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): _profile = False if _profile: from time import time _t = time() # 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'], "..")) # G for p in self.netD.parameters(): p.requires_grad = False if step > self.D_init_iters: self.optimizer_G.zero_grad() self.swapout_D(step) self.swapout_G(step) # 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 # Calculate a standard deviation for the gaussian noise to be applied to the discriminator, termed noise-theta. if self.D_noise_final == 0: noise_theta = 0 else: noise_theta = (self.D_noise_theta - self.D_noise_theta_floor) * (self.D_noise_final - min(step, self.D_noise_final)) / self.D_noise_final + self.D_noise_theta_floor if _profile: print("Misc setup %f" % (time() - _t,)) _t = time() self.fake_GenOut = [] self.fake_H = [] var_ref_skips = [] 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) if _profile: print("Gen forward %f" % (time() - _t,)) _t = time() # 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] # The following line detaches all generator outputs that are not None. self.fake_GenOut.append(tuple([(x.detach() if x is not None else None) for x in list(fake_GenOut)])) var_ref = (var_ref,) # This is a tuple for legacy reasons. 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_pix_log = l_g_pix / self.l_pix_w 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_fea_log = l_g_fea / self.l_fea_w l_g_total += l_g_fea if _profile: print("Fea forward %f" % (time() - _t,)) _t = time() # 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.l_gan_w > 0: if self.opt['train']['gan_type'] == 'gan' or self.opt['train']['gan_type'] == 'pixgan': 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_gan_log = l_g_gan / self.l_gan_w l_g_total += l_g_gan # Scale the loss down by the batch factor. l_g_total_log = l_g_total 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() if _profile: print("Gen backward %f" % (time() - _t,)) _t = time() self.optimizer_G.step() if _profile: print("Gen step %f" % (time() - _t,)) _t = time() # D if self.l_gan_w > 0 and step > self.G_warmup: for p in self.netD.parameters(): p.requires_grad = True noise = torch.randn_like(var_ref[0]) * noise_theta noise.to(self.device) self.optimizer_D.zero_grad() for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix): # Re-compute generator outputs (post-update). with torch.no_grad(): fake_H = self.netG(var_L) # The following line detaches all generator outputs that are not None. fake_H = tuple([(x.detach() if x is not None else None) for x in list(fake_H)]) if _profile: print("Gen forward for disc %f" % (time() - _t,)) _t = time() # Apply noise to the inputs to slow discriminator convergence. var_ref = (var_ref + noise,) fake_H = (fake_H[0] + noise,) + fake_H[1:] 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 l_d_real_log = l_d_real * 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 l_d_fake_log = l_d_fake * 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() if self.opt['train']['gan_type'] == 'pixgan': # We're making some assumptions about the underlying pixel-discriminator here. This is a # necessary evil for now, but if this turns out well we might want to make this configurable. PIXDISC_CHANNELS = 3 PIXDISC_OUTPUT_REDUCTION = 8 PIXDISC_MAX_REDUCTION = 32 disc_output_shape = (var_ref[0].shape[0], PIXDISC_CHANNELS, var_ref[0].shape[2] // PIXDISC_OUTPUT_REDUCTION, var_ref[0].shape[3] // PIXDISC_OUTPUT_REDUCTION) real = torch.ones(disc_output_shape, device=var_ref[0].device) fake = torch.zeros(disc_output_shape, device=var_ref[0].device) # randomly determine portions of the image to swap to keep the discriminator honest. if random.random() > .25: # Make the swap across fake_H and var_ref SWAP_MAX_DIM = var_ref[0].shape[2] // (2 * PIXDISC_MAX_REDUCTION) assert SWAP_MAX_DIM > 0 swap_x, swap_y = random.randint(0, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION, random.randint(0, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION swap_w, swap_h = random.randint(1, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION, random.randint(1, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION t = fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone() fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = t # Swap the expectation matrix too. swap_x, swap_y, swap_w, swap_h = swap_x // PIXDISC_OUTPUT_REDUCTION, swap_y // PIXDISC_OUTPUT_REDUCTION, swap_w // PIXDISC_OUTPUT_REDUCTION, swap_h // PIXDISC_OUTPUT_REDUCTION real[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 0.0 fake[:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = 1.0 # We're also assuming that this is exactly how the flattened discriminator output is generated. real = real.view(-1, 1) fake = fake.view(-1, 1) # real pred_d_real = self.netD(var_ref) l_d_real = self.cri_gan(pred_d_real, real) / self.mega_batch_factor l_d_real_log = l_d_real * 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, fake) / self.mega_batch_factor l_d_fake_log = l_d_fake * 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_fake = self.netD(fake_H).detach() pred_d_real = self.netD(var_ref) if _profile: print("Double disc forward (RAGAN) %f" % (time() - _t,)) _t = time() l_d_real = self.cri_gan(pred_d_real - torch.mean(pred_d_fake), True) * 0.5 / self.mega_batch_factor l_d_real_log = l_d_real * self.mega_batch_factor * 2 with amp.scale_loss(l_d_real, self.optimizer_D, loss_id=2) as l_d_real_scaled: l_d_real_scaled.backward() if _profile: print("Disc backward 1 (RAGAN) %f" % (time() - _t,)) _t = time() 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 l_d_fake_log = l_d_fake * self.mega_batch_factor * 2 with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled: l_d_fake_scaled.backward() if _profile: print("Disc forward/backward 2 (RAGAN) %f" % (time() - _t,)) _t = time() # Append var_ref here, so that we can inspect the alterations the disc made if pixgan var_ref_skips.append(var_ref[0].detach()) self.fake_H.append(fake_H[0].detach()) self.optimizer_D.step() if _profile: print("Disc step %f" % (time() - _t,)) _t = time() # 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, "disc_fake"), exist_ok=True) os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True) multi_gen = False if isinstance(self.fake_GenOut[0], tuple): os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True) multi_gen = True # fed_LQ is not chunked. for i in range(self.mega_batch_factor): utils.save_image(self.var_H[i].cpu(), os.path.join(sample_save_path, "hr", "%05i_%02i.png" % (step, i))) utils.save_image(self.var_L[i].cpu(), os.path.join(sample_save_path, "lr", "%05i_%02i.png" % (step, i))) utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i))) if multi_gen: utils.save_image(self.fake_GenOut[i][0].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i))) if self.l_gan_w > 0 and step > self.G_warmup: utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i))) utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "%05i_%02i.png" % (step, i))) else: utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i))) # Log metrics 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_log.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_log.item()) if self.l_gan_w > 0: self.add_log_entry('l_g_gan', l_g_gan_log.item()) self.add_log_entry('l_g_total', l_g_total_log.item()) if self.l_gan_w > 0 and step > self.G_warmup: self.add_log_entry('l_d_real', l_d_real_log.item()) self.add_log_entry('l_d_fake', l_d_fake_log.item()) self.add_log_entry('D_fake', torch.mean(pred_d_fake.detach())) self.add_log_entry('D_diff', torch.mean(pred_d_fake) - torch.mean(pred_d_real)) if step % self.corruptor_swapout_steps == 0 and step > 0: self.load_random_corruptor() # 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 pick_rand_prev_model(self, model_suffix): previous_models = glob.glob(os.path.join(self.opt['path']['models'], "*_%s.pth" % (model_suffix,))) if len(previous_models) <= 1: return None # Just a note: this intentionally includes the swap model in the list of possibilities. return previous_models[random.randint(0, len(previous_models)-1)] def compute_fea_loss(self, real, fake): with torch.no_grad(): real = real.unsqueeze(dim=0) fake = fake.unsqueeze(dim=0) real_fea = self.netF(real).detach() fake_fea = self.netF(fake) return self.cri_fea(fake_fea, real_fea).item() # Called before verification/checkpoint to ensure we're using the real models and not a swapout variant. def force_restore_swapout(self): if self.swapout_D_duration > 0: logger.info("Swapping back to current D model: %s" % (self.stashed_D,)) self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load']) self.stashed_D = None self.swapout_D_duration = 0 if self.swapout_G_duration > 0: logger.info("Swapping back to current G model: %s" % (self.stashed_G,)) self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load']) self.stashed_G = None self.swapout_G_duration = 0 def swapout_D(self, step): if self.swapout_D_duration > 0: self.swapout_D_duration -= 1 if self.swapout_D_duration == 0: # Swap back. logger.info("Swapping back to current D model: %s" % (self.stashed_D,)) self.load_network(self.stashed_D, self.netD, self.opt['path']['strict_load']) self.stashed_D = None elif self.swapout_D_freq != 0 and step % self.swapout_D_freq == 0: swapped_model = self.pick_rand_prev_model('D') if swapped_model is not None: logger.info("Swapping to previous D model: %s" % (swapped_model,)) self.stashed_D = self.save_network(self.netD, 'D', 'swap_model') self.load_network(swapped_model, self.netD, self.opt['path']['strict_load']) self.swapout_D_duration = self.swapout_duration def swapout_G(self, step): if self.swapout_G_duration > 0: self.swapout_G_duration -= 1 if self.swapout_G_duration == 0: # Swap back. logger.info("Swapping back to current G model: %s" % (self.stashed_G,)) self.load_network(self.stashed_G, self.netG, self.opt['path']['strict_load']) self.stashed_G = None elif self.swapout_G_freq != 0 and step % self.swapout_G_freq == 0: swapped_model = self.pick_rand_prev_model('G') if swapped_model is not None: logger.info("Swapping to previous G model: %s" % (swapped_model,)) self.stashed_G = self.save_network(self.netG, 'G', 'swap_model') self.load_network(swapped_model, self.netG, self.opt['path']['strict_load']) self.swapout_G_duration = self.swapout_duration 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, 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)) return return_log def get_current_visuals(self, need_GT=True): out_dict = OrderedDict() out_dict['LQ'] = self.var_L[0].detach().float().cpu() gen_batch = self.fake_GenOut[0] if isinstance(gen_batch, tuple): gen_batch = gen_batch[0] out_dict['rlt'] = gen_batch.detach().float().cpu() if need_GT: out_dict['GT'] = self.var_H[0].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 load_random_corruptor(self): if self.netC is None: return corruptor_files = glob.glob(os.path.join(self.opt['path']['pretrained_corruptors_dir'], "*.pth")) corruptor_to_load = corruptor_files[random.randint(0, len(corruptor_files)-1)] logger.info('Swapping corruptor to: %s' % (corruptor_to_load,)) self.load_network(corruptor_to_load, self.netC, 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)