import cv2 import math import numpy as np import random import torch from collections import OrderedDict from os import path as osp from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.losses.gan_loss import g_path_regularize, r1_penalty from basicsr.utils import imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY from .base_model import BaseModel @MODEL_REGISTRY.register() class StyleGAN2Model(BaseModel): """StyleGAN2 model.""" def __init__(self, opt): super(StyleGAN2Model, self).__init__(opt) # define network net_g self.net_g = build_network(opt['network_g']) self.net_g = self.model_to_device(self.net_g) self.print_network(self.net_g) # load pretrained model load_path = self.opt['path'].get('pretrain_network_g', None) if load_path is not None: param_key = self.opt['path'].get('param_key_g', 'params') self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) # latent dimension: self.num_style_feat self.num_style_feat = opt['network_g']['num_style_feat'] num_val_samples = self.opt['val'].get('num_val_samples', 16) self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device) if self.is_train: self.init_training_settings() def init_training_settings(self): train_opt = self.opt['train'] # define network net_d self.net_d = build_network(self.opt['network_d']) self.net_d = self.model_to_device(self.net_d) self.print_network(self.net_d) # load pretrained model load_path = self.opt['path'].get('pretrain_network_d', None) if load_path is not None: param_key = self.opt['path'].get('param_key_d', 'params') self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) # define network net_g with Exponential Moving Average (EMA) # net_g_ema only used for testing on one GPU and saving, do not need to # wrap with DistributedDataParallel self.net_g_ema = build_network(self.opt['network_g']).to(self.device) # load pretrained model load_path = self.opt['path'].get('pretrain_network_g', None) if load_path is not None: self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') else: self.model_ema(0) # copy net_g weight self.net_g.train() self.net_d.train() self.net_g_ema.eval() # define losses # gan loss (wgan) self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) # regularization weights self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator self.path_reg_weight = train_opt['path_reg_weight'] # for generator self.net_g_reg_every = train_opt['net_g_reg_every'] self.net_d_reg_every = train_opt['net_d_reg_every'] self.mixing_prob = train_opt['mixing_prob'] self.mean_path_length = 0 # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] # optimizer g net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1) if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC': normal_params = [] style_mlp_params = [] modulation_conv_params = [] for name, param in self.net_g.named_parameters(): if 'modulation' in name: normal_params.append(param) elif 'style_mlp' in name: style_mlp_params.append(param) elif 'modulated_conv' in name: modulation_conv_params.append(param) else: normal_params.append(param) optim_params_g = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }, { 'params': style_mlp_params, 'lr': train_opt['optim_g']['lr'] * 0.01 }, { 'params': modulation_conv_params, 'lr': train_opt['optim_g']['lr'] / 3 } ] else: normal_params = [] for name, param in self.net_g.named_parameters(): normal_params.append(param) optim_params_g = [{ # add normal params first 'params': normal_params, 'lr': train_opt['optim_g']['lr'] }] optim_type = train_opt['optim_g'].pop('type') lr = train_opt['optim_g']['lr'] * net_g_reg_ratio betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) self.optimizers.append(self.optimizer_g) # optimizer d net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC': normal_params = [] linear_params = [] for name, param in self.net_d.named_parameters(): if 'final_linear' in name: linear_params.append(param) else: normal_params.append(param) optim_params_d = [ { # add normal params first 'params': normal_params, 'lr': train_opt['optim_d']['lr'] }, { 'params': linear_params, 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512)) } ] else: normal_params = [] for name, param in self.net_d.named_parameters(): normal_params.append(param) optim_params_d = [{ # add normal params first 'params': normal_params, 'lr': train_opt['optim_d']['lr'] }] optim_type = train_opt['optim_d'].pop('type') lr = train_opt['optim_d']['lr'] * net_d_reg_ratio betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) self.optimizers.append(self.optimizer_d) def feed_data(self, data): self.real_img = data['gt'].to(self.device) def make_noise(self, batch, num_noise): if num_noise == 1: noises = torch.randn(batch, self.num_style_feat, device=self.device) else: noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0) return noises def mixing_noise(self, batch, prob): if random.random() < prob: return self.make_noise(batch, 2) else: return [self.make_noise(batch, 1)] def optimize_parameters(self, current_iter): loss_dict = OrderedDict() # optimize net_d for p in self.net_d.parameters(): p.requires_grad = True self.optimizer_d.zero_grad() batch = self.real_img.size(0) noise = self.mixing_noise(batch, self.mixing_prob) fake_img, _ = self.net_g(noise) fake_pred = self.net_d(fake_img.detach()) real_pred = self.net_d(self.real_img) # wgan loss with softplus (logistic loss) for discriminator l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True) loss_dict['l_d'] = l_d # In wgan, real_score should be positive and fake_score should be # negative loss_dict['real_score'] = real_pred.detach().mean() loss_dict['fake_score'] = fake_pred.detach().mean() l_d.backward() if current_iter % self.net_d_reg_every == 0: self.real_img.requires_grad = True real_pred = self.net_d(self.real_img) l_d_r1 = r1_penalty(real_pred, self.real_img) l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) # TODO: why do we need to add 0 * real_pred, otherwise, a runtime # error will arise: RuntimeError: Expected to have finished # reduction in the prior iteration before starting a new one. # This error indicates that your module has parameters that were # not used in producing loss. loss_dict['l_d_r1'] = l_d_r1.detach().mean() l_d_r1.backward() self.optimizer_d.step() # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() noise = self.mixing_noise(batch, self.mixing_prob) fake_img, _ = self.net_g(noise) fake_pred = self.net_d(fake_img) # wgan loss with softplus (non-saturating loss) for generator l_g = self.cri_gan(fake_pred, True, is_disc=False) loss_dict['l_g'] = l_g l_g.backward() if current_iter % self.net_g_reg_every == 0: path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink']) noise = self.mixing_noise(path_batch_size, self.mixing_prob) fake_img, latents = self.net_g(noise, return_latents=True) l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length) l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0]) # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0] l_g_path.backward() loss_dict['l_g_path'] = l_g_path.detach().mean() loss_dict['path_length'] = path_lengths self.optimizer_g.step() self.log_dict = self.reduce_loss_dict(loss_dict) # EMA self.model_ema(decay=0.5**(32 / (10 * 1000))) def test(self): with torch.no_grad(): self.net_g_ema.eval() self.output, _ = self.net_g_ema([self.fixed_sample]) def dist_validation(self, dataloader, current_iter, tb_logger, save_img): if self.opt['rank'] == 0: self.nondist_validation(dataloader, current_iter, tb_logger, save_img) def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): assert dataloader is None, 'Validation dataloader should be None.' self.test() result = tensor2img(self.output, min_max=(-1, 1)) if self.opt['is_train']: save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png') imwrite(result, save_img_path) # add sample images to tb_logger result = (result / 255.).astype(np.float32) result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if tb_logger is not None: tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC') def save(self, epoch, current_iter): self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) self.save_network(self.net_d, 'net_d', current_iter) self.save_training_state(epoch, current_iter)