import torch from collections import OrderedDict from os import path as osp from tqdm import tqdm from basicsr.archs import build_network from basicsr.metrics import calculate_metric from basicsr.utils import get_root_logger, imwrite, tensor2img from basicsr.utils.registry import MODEL_REGISTRY import torch.nn.functional as F from .sr_model import SRModel @MODEL_REGISTRY.register() class CodeFormerIdxModel(SRModel): def feed_data(self, data): self.gt = data['gt'].to(self.device) self.input = data['in'].to(self.device) self.b = self.gt.shape[0] if 'latent_gt' in data: self.idx_gt = data['latent_gt'].to(self.device) self.idx_gt = self.idx_gt.view(self.b, -1) else: self.idx_gt = None def init_training_settings(self): logger = get_root_logger() train_opt = self.opt['train'] self.ema_decay = train_opt.get('ema_decay', 0) if self.ema_decay > 0: logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') # define network net_g with Exponential Moving Average (EMA) # net_g_ema is used only 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_ema.eval() if self.opt['datasets']['train'].get('latent_gt_path', None) is not None: self.generate_idx_gt = False elif self.opt.get('network_vqgan', None) is not None: self.hq_vqgan_fix = build_network(self.opt['network_vqgan']).to(self.device) self.hq_vqgan_fix.eval() self.generate_idx_gt = True for param in self.hq_vqgan_fix.parameters(): param.requires_grad = False else: raise NotImplementedError(f'Shoule have network_vqgan config or pre-calculated latent code.') logger.info(f'Need to generate latent GT code: {self.generate_idx_gt}') self.hq_feat_loss = train_opt.get('use_hq_feat_loss', True) self.feat_loss_weight = train_opt.get('feat_loss_weight', 1.0) self.cross_entropy_loss = train_opt.get('cross_entropy_loss', True) self.entropy_loss_weight = train_opt.get('entropy_loss_weight', 0.5) self.net_g.train() # set up optimizers and schedulers self.setup_optimizers() self.setup_schedulers() def setup_optimizers(self): train_opt = self.opt['train'] # optimizer g optim_params_g = [] for k, v in self.net_g.named_parameters(): if v.requires_grad: optim_params_g.append(v) else: logger = get_root_logger() logger.warning(f'Params {k} will not be optimized.') optim_type = train_opt['optim_g'].pop('type') self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, **train_opt['optim_g']) self.optimizers.append(self.optimizer_g) def optimize_parameters(self, current_iter): logger = get_root_logger() # optimize net_g self.optimizer_g.zero_grad() if self.generate_idx_gt: x = self.hq_vqgan_fix.encoder(self.gt) _, _, quant_stats = self.hq_vqgan_fix.quantize(x) min_encoding_indices = quant_stats['min_encoding_indices'] self.idx_gt = min_encoding_indices.view(self.b, -1) if self.hq_feat_loss: # quant_feats quant_feat_gt = self.net_g.module.quantize.get_codebook_feat(self.idx_gt, shape=[self.b,16,16,256]) logits, lq_feat = self.net_g(self.input, w=0, code_only=True) l_g_total = 0 loss_dict = OrderedDict() # hq_feat_loss if self.hq_feat_loss: # codebook loss l_feat_encoder = torch.mean((quant_feat_gt.detach()-lq_feat)**2) * self.feat_loss_weight l_g_total += l_feat_encoder loss_dict['l_feat_encoder'] = l_feat_encoder # cross_entropy_loss if self.cross_entropy_loss: # b(hw)n -> bn(hw) cross_entropy_loss = F.cross_entropy(logits.permute(0, 2, 1), self.idx_gt) * self.entropy_loss_weight l_g_total += cross_entropy_loss loss_dict['cross_entropy_loss'] = cross_entropy_loss l_g_total.backward() self.optimizer_g.step() if self.ema_decay > 0: self.model_ema(decay=self.ema_decay) 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.input, w=0) 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.input, w=0) 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 if with_metrics: self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} 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() visuals = self.get_current_visuals() sr_img = tensor2img([visuals['result']]) if 'gt' in visuals: gt_img = tensor2img([visuals['gt']]) 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(): metric_data = dict(img1=sr_img, img2=gt_img) self.metric_results[name] += calculate_metric(metric_data, opt_) pbar.update(1) pbar.set_description(f'Test {img_name}') pbar.close() if with_metrics: for metric in self.metric_results.keys(): self.metric_results[metric] /= (idx + 1) 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}\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/{metric}', value, current_iter) def get_current_visuals(self): out_dict = OrderedDict() out_dict['gt'] = self.gt.detach().cpu() out_dict['result'] = self.output.detach().cpu() return out_dict def save(self, epoch, current_iter): if self.ema_decay > 0: self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) else: self.save_network(self.net_g, 'net_g', current_iter) self.save_training_state(epoch, current_iter)