import math import os.path as osp import torch from basicsr.archs import build_network from basicsr.losses import build_loss from basicsr.losses.gan_loss import r1_penalty from basicsr.metrics import calculate_metric from basicsr.models.base_model import BaseModel from basicsr.utils import get_root_logger, imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY from collections import OrderedDict from torch.nn import functional as F from torchvision.ops import roi_align from tqdm import tqdm @MODEL_REGISTRY.register() class GFPGANModel(BaseModel): """The GFPGAN model for Towards real-world blind face restoratin with generative facial prior""" def __init__(self, opt): super(GFPGANModel, self).__init__(opt) self.idx = 0 # it is used for saving data for check # define network 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) self.log_size = int(math.log(self.opt['network_g']['out_size'], 2)) if self.is_train: self.init_training_settings() def init_training_settings(self): train_opt = self.opt['train'] # ----------- define 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: self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True)) # ----------- define net_g with Exponential Moving Average (EMA) ----------- # # net_g_ema only used for testing on one GPU and saving. There is no 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() # ----------- facial component networks ----------- # if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt): self.use_facial_disc = True else: self.use_facial_disc = False if self.use_facial_disc: # left eye self.net_d_left_eye = build_network(self.opt['network_d_left_eye']) self.net_d_left_eye = self.model_to_device(self.net_d_left_eye) self.print_network(self.net_d_left_eye) load_path = self.opt['path'].get('pretrain_network_d_left_eye') if load_path is not None: self.load_network(self.net_d_left_eye, load_path, True, 'params') # right eye self.net_d_right_eye = build_network(self.opt['network_d_right_eye']) self.net_d_right_eye = self.model_to_device(self.net_d_right_eye) self.print_network(self.net_d_right_eye) load_path = self.opt['path'].get('pretrain_network_d_right_eye') if load_path is not None: self.load_network(self.net_d_right_eye, load_path, True, 'params') # mouth self.net_d_mouth = build_network(self.opt['network_d_mouth']) self.net_d_mouth = self.model_to_device(self.net_d_mouth) self.print_network(self.net_d_mouth) load_path = self.opt['path'].get('pretrain_network_d_mouth') if load_path is not None: self.load_network(self.net_d_mouth, load_path, True, 'params') self.net_d_left_eye.train() self.net_d_right_eye.train() self.net_d_mouth.train() # ----------- define facial component gan loss ----------- # self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device) # ----------- define losses ----------- # # pixel loss if train_opt.get('pixel_opt'): self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) else: self.cri_pix = None # perceptual loss if train_opt.get('perceptual_opt'): self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) else: self.cri_perceptual = None # L1 loss is used in pyramid loss, component style loss and identity loss self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device) # gan loss (wgan) self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) # ----------- define identity loss ----------- # if 'network_identity' in self.opt: self.use_identity = True else: self.use_identity = False if self.use_identity: # define identity network self.network_identity = build_network(self.opt['network_identity']) self.network_identity = self.model_to_device(self.network_identity) self.print_network(self.network_identity) load_path = self.opt['path'].get('pretrain_network_identity') if load_path is not None: self.load_network(self.network_identity, load_path, True, None) self.network_identity.eval() for param in self.network_identity.parameters(): param.requires_grad = False # regularization weights self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator self.net_d_iters = train_opt.get('net_d_iters', 1) self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) self.net_d_reg_every = train_opt['net_d_reg_every'] # 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 = 1 normal_params = [] for _, 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) normal_params = [] for _, 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) # ----------- optimizers for facial component networks ----------- # if self.use_facial_disc: # setup optimizers for facial component discriminators optim_type = train_opt['optim_component'].pop('type') lr = train_opt['optim_component']['lr'] # left eye self.optimizer_d_left_eye = self.get_optimizer( optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99)) self.optimizers.append(self.optimizer_d_left_eye) # right eye self.optimizer_d_right_eye = self.get_optimizer( optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99)) self.optimizers.append(self.optimizer_d_right_eye) # mouth self.optimizer_d_mouth = self.get_optimizer( optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99)) self.optimizers.append(self.optimizer_d_mouth) def feed_data(self, data): self.lq = data['lq'].to(self.device) if 'gt' in data: self.gt = data['gt'].to(self.device) if 'loc_left_eye' in data: # get facial component locations, shape (batch, 4) self.loc_left_eyes = data['loc_left_eye'] self.loc_right_eyes = data['loc_right_eye'] self.loc_mouths = data['loc_mouth'] # uncomment to check data # import torchvision # if self.opt['rank'] == 0: # import os # os.makedirs('tmp/gt', exist_ok=True) # os.makedirs('tmp/lq', exist_ok=True) # print(self.idx) # torchvision.utils.save_image( # self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) # torchvision.utils.save_image( # self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1)) # self.idx = self.idx + 1 def construct_img_pyramid(self): """Construct image pyramid for intermediate restoration loss""" pyramid_gt = [self.gt] down_img = self.gt for _ in range(0, self.log_size - 3): down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False) pyramid_gt.insert(0, down_img) return pyramid_gt def get_roi_regions(self, eye_out_size=80, mouth_out_size=120): face_ratio = int(self.opt['network_g']['out_size'] / 512) eye_out_size *= face_ratio mouth_out_size *= face_ratio rois_eyes = [] rois_mouths = [] for b in range(self.loc_left_eyes.size(0)): # loop for batch size # left eye and right eye img_inds = self.loc_left_eyes.new_full((2, 1), b) bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4) rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5) rois_eyes.append(rois) # mouse img_inds = self.loc_left_eyes.new_full((1, 1), b) rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5) rois_mouths.append(rois) rois_eyes = torch.cat(rois_eyes, 0).to(self.device) rois_mouths = torch.cat(rois_mouths, 0).to(self.device) # real images all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio self.left_eyes_gt = all_eyes[0::2, :, :, :] self.right_eyes_gt = all_eyes[1::2, :, :, :] self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio # output all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio self.left_eyes = all_eyes[0::2, :, :, :] self.right_eyes = all_eyes[1::2, :, :, :] self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio def _gram_mat(self, x): """Calculate Gram matrix. Args: x (torch.Tensor): Tensor with shape of (n, c, h, w). Returns: torch.Tensor: Gram matrix. """ n, c, h, w = x.size() features = x.view(n, c, w * h) features_t = features.transpose(1, 2) gram = features.bmm(features_t) / (c * h * w) return gram def gray_resize_for_identity(self, out, size=128): out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :]) out_gray = out_gray.unsqueeze(1) out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False) return out_gray def optimize_parameters(self, current_iter): # optimize net_g for p in self.net_d.parameters(): p.requires_grad = False self.optimizer_g.zero_grad() # do not update facial component net_d if self.use_facial_disc: for p in self.net_d_left_eye.parameters(): p.requires_grad = False for p in self.net_d_right_eye.parameters(): p.requires_grad = False for p in self.net_d_mouth.parameters(): p.requires_grad = False # image pyramid loss weight pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 0) if pyramid_loss_weight > 0 and current_iter > self.opt['train'].get('remove_pyramid_loss', float('inf')): pyramid_loss_weight = 1e-12 # very small weight to avoid unused param error if pyramid_loss_weight > 0: self.output, out_rgbs = self.net_g(self.lq, return_rgb=True) pyramid_gt = self.construct_img_pyramid() else: self.output, out_rgbs = self.net_g(self.lq, return_rgb=False) # get roi-align regions if self.use_facial_disc: self.get_roi_regions(eye_out_size=80, mouth_out_size=120) l_g_total = 0 loss_dict = OrderedDict() if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): # pixel loss if self.cri_pix: l_g_pix = self.cri_pix(self.output, self.gt) l_g_total += l_g_pix loss_dict['l_g_pix'] = l_g_pix # image pyramid loss if pyramid_loss_weight > 0: for i in range(0, self.log_size - 2): l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight l_g_total += l_pyramid loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid # perceptual loss if self.cri_perceptual: l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) if l_g_percep is not None: l_g_total += l_g_percep loss_dict['l_g_percep'] = l_g_percep if l_g_style is not None: l_g_total += l_g_style loss_dict['l_g_style'] = l_g_style # gan loss fake_g_pred = self.net_d(self.output) l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) l_g_total += l_g_gan loss_dict['l_g_gan'] = l_g_gan # facial component loss if self.use_facial_disc: # left eye fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True) l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False) l_g_total += l_g_gan loss_dict['l_g_gan_left_eye'] = l_g_gan # right eye fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True) l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False) l_g_total += l_g_gan loss_dict['l_g_gan_right_eye'] = l_g_gan # mouth fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True) l_g_gan = self.cri_component(fake_mouth, True, is_disc=False) l_g_total += l_g_gan loss_dict['l_g_gan_mouth'] = l_g_gan if self.opt['train'].get('comp_style_weight', 0) > 0: # get gt feat _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True) _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True) _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True) def _comp_style(feat, feat_gt, criterion): return criterion(self._gram_mat(feat[0]), self._gram_mat( feat_gt[0].detach())) * 0.5 + criterion( self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach())) # facial component style loss comp_style_loss = 0 comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1) comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1) comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1) comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight'] l_g_total += comp_style_loss loss_dict['l_g_comp_style_loss'] = comp_style_loss # identity loss if self.use_identity: identity_weight = self.opt['train']['identity_weight'] # get gray images and resize out_gray = self.gray_resize_for_identity(self.output) gt_gray = self.gray_resize_for_identity(self.gt) identity_gt = self.network_identity(gt_gray).detach() identity_out = self.network_identity(out_gray) l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight l_g_total += l_identity loss_dict['l_identity'] = l_identity l_g_total.backward() self.optimizer_g.step() # EMA self.model_ema(decay=0.5**(32 / (10 * 1000))) # ----------- optimize net_d ----------- # for p in self.net_d.parameters(): p.requires_grad = True self.optimizer_d.zero_grad() if self.use_facial_disc: for p in self.net_d_left_eye.parameters(): p.requires_grad = True for p in self.net_d_right_eye.parameters(): p.requires_grad = True for p in self.net_d_mouth.parameters(): p.requires_grad = True self.optimizer_d_left_eye.zero_grad() self.optimizer_d_right_eye.zero_grad() self.optimizer_d_mouth.zero_grad() fake_d_pred = self.net_d(self.output.detach()) real_d_pred = self.net_d(self.gt) l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_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_d_pred.detach().mean() loss_dict['fake_score'] = fake_d_pred.detach().mean() l_d.backward() # regularization loss if current_iter % self.net_d_reg_every == 0: self.gt.requires_grad = True real_pred = self.net_d(self.gt) l_d_r1 = r1_penalty(real_pred, self.gt) l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) loss_dict['l_d_r1'] = l_d_r1.detach().mean() l_d_r1.backward() self.optimizer_d.step() # optimize facial component discriminators if self.use_facial_disc: # left eye fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach()) real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt) l_d_left_eye = self.cri_component( real_d_pred, True, is_disc=True) + self.cri_gan( fake_d_pred, False, is_disc=True) loss_dict['l_d_left_eye'] = l_d_left_eye l_d_left_eye.backward() # right eye fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach()) real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt) l_d_right_eye = self.cri_component( real_d_pred, True, is_disc=True) + self.cri_gan( fake_d_pred, False, is_disc=True) loss_dict['l_d_right_eye'] = l_d_right_eye l_d_right_eye.backward() # mouth fake_d_pred, _ = self.net_d_mouth(self.mouths.detach()) real_d_pred, _ = self.net_d_mouth(self.mouths_gt) l_d_mouth = self.cri_component( real_d_pred, True, is_disc=True) + self.cri_gan( fake_d_pred, False, is_disc=True) loss_dict['l_d_mouth'] = l_d_mouth l_d_mouth.backward() self.optimizer_d_left_eye.step() self.optimizer_d_right_eye.step() self.optimizer_d_mouth.step() self.log_dict = self.reduce_loss_dict(loss_dict) def test(self): with torch.no_grad(): if hasattr(self, 'net_g_ema'): self.net_g_ema.eval() self.output, _ = self.net_g_ema(self.lq) else: logger = get_root_logger() logger.warning('Do not have self.net_g_ema, use self.net_g.') self.net_g.eval() self.output, _ = self.net_g(self.lq) self.net_g.train() 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): dataset_name = dataloader.dataset.opt['name'] with_metrics = self.opt['val'].get('metrics') is not None use_pbar = self.opt['val'].get('pbar', False) if with_metrics: if not hasattr(self, 'metric_results'): # only execute in the first run self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} # initialize the best metric results for each dataset_name (supporting multiple validation datasets) self._initialize_best_metric_results(dataset_name) # zero self.metric_results self.metric_results = {metric: 0 for metric in self.metric_results} metric_data = dict() if use_pbar: pbar = tqdm(total=len(dataloader), unit='image') for idx, val_data in enumerate(dataloader): img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] self.feed_data(val_data) self.test() sr_img = tensor2img(self.output.detach().cpu(), min_max=(-1, 1)) metric_data['img'] = sr_img if hasattr(self, 'gt'): gt_img = tensor2img(self.gt.detach().cpu(), min_max=(-1, 1)) metric_data['img2'] = gt_img del self.gt # tentative for out of GPU memory del self.lq del self.output torch.cuda.empty_cache() if save_img: if self.opt['is_train']: save_img_path = osp.join(self.opt['path']['visualization'], img_name, f'{img_name}_{current_iter}.png') else: if self.opt['val']['suffix']: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["val"]["suffix"]}.png') else: save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, f'{img_name}_{self.opt["name"]}.png') imwrite(sr_img, save_img_path) if with_metrics: # calculate metrics for name, opt_ in self.opt['val']['metrics'].items(): self.metric_results[name] += calculate_metric(metric_data, opt_) if use_pbar: pbar.update(1) pbar.set_description(f'Test {img_name}') if use_pbar: pbar.close() if with_metrics: for metric in self.metric_results.keys(): self.metric_results[metric] /= (idx + 1) # update the best metric result self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) self._log_validation_metric_values(current_iter, dataset_name, tb_logger) def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): log_str = f'Validation {dataset_name}\n' for metric, value in self.metric_results.items(): log_str += f'\t # {metric}: {value:.4f}' if hasattr(self, 'best_metric_results'): log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') log_str += '\n' logger = get_root_logger() logger.info(log_str) if tb_logger: for metric, value in self.metric_results.items(): tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) def save(self, epoch, current_iter): # save net_g and net_d 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) # save component discriminators if self.use_facial_disc: self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter) self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter) self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter) # save training state self.save_training_state(epoch, current_iter)