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| import torch | |
| from collections import OrderedDict | |
| from os import path as osp | |
| from tqdm import tqdm | |
| from basicsr.archs import build_network | |
| from basicsr.losses import build_loss | |
| from basicsr.metrics import calculate_metric | |
| from basicsr.utils import get_root_logger, imwrite, tensor2img | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| from .base_model import BaseModel | |
| class SRModel(BaseModel): | |
| """Base SR model for single image super-resolution.""" | |
| def __init__(self, opt): | |
| super(SRModel, self).__init__(opt) | |
| # 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 models | |
| 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) | |
| if self.is_train: | |
| self.init_training_settings() | |
| def init_training_settings(self): | |
| self.net_g.train() | |
| train_opt = self.opt['train'] | |
| self.ema_decay = train_opt.get('ema_decay', 0) | |
| if self.ema_decay > 0: | |
| logger = get_root_logger() | |
| 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() | |
| # define losses | |
| if train_opt.get('pixel_opt'): | |
| self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) | |
| else: | |
| self.cri_pix = None | |
| if train_opt.get('perceptual_opt'): | |
| self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) | |
| else: | |
| self.cri_perceptual = None | |
| if self.cri_pix is None and self.cri_perceptual is None: | |
| raise ValueError('Both pixel and perceptual losses are None.') | |
| # set up optimizers and schedulers | |
| self.setup_optimizers() | |
| self.setup_schedulers() | |
| def setup_optimizers(self): | |
| train_opt = self.opt['train'] | |
| optim_params = [] | |
| for k, v in self.net_g.named_parameters(): | |
| if v.requires_grad: | |
| optim_params.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, **train_opt['optim_g']) | |
| self.optimizers.append(self.optimizer_g) | |
| def feed_data(self, data): | |
| self.lq = data['lq'].to(self.device) | |
| if 'gt' in data: | |
| self.gt = data['gt'].to(self.device) | |
| def optimize_parameters(self, current_iter): | |
| self.optimizer_g.zero_grad() | |
| self.output = self.net_g(self.lq) | |
| l_total = 0 | |
| loss_dict = OrderedDict() | |
| # pixel loss | |
| if self.cri_pix: | |
| l_pix = self.cri_pix(self.output, self.gt) | |
| l_total += l_pix | |
| loss_dict['l_pix'] = l_pix | |
| # perceptual loss | |
| if self.cri_perceptual: | |
| l_percep, l_style = self.cri_perceptual(self.output, self.gt) | |
| if l_percep is not None: | |
| l_total += l_percep | |
| loss_dict['l_percep'] = l_percep | |
| if l_style is not None: | |
| l_total += l_style | |
| loss_dict['l_style'] = l_style | |
| l_total.backward() | |
| self.optimizer_g.step() | |
| self.log_dict = self.reduce_loss_dict(loss_dict) | |
| if self.ema_decay > 0: | |
| self.model_ema(decay=self.ema_decay) | |
| def test(self): | |
| if hasattr(self, 'net_g_ema'): | |
| self.net_g_ema.eval() | |
| with torch.no_grad(): | |
| self.output = self.net_g_ema(self.lq) | |
| else: | |
| self.net_g.eval() | |
| with torch.no_grad(): | |
| self.output = self.net_g(self.lq) | |
| self.net_g.train() | |
| def test_selfensemble(self): | |
| # TODO: to be tested | |
| # 8 augmentations | |
| # modified from https://github.com/thstkdgus35/EDSR-PyTorch | |
| def _transform(v, op): | |
| # if self.precision != 'single': v = v.float() | |
| v2np = v.data.cpu().numpy() | |
| if op == 'v': | |
| tfnp = v2np[:, :, :, ::-1].copy() | |
| elif op == 'h': | |
| tfnp = v2np[:, :, ::-1, :].copy() | |
| elif op == 't': | |
| tfnp = v2np.transpose((0, 1, 3, 2)).copy() | |
| ret = torch.Tensor(tfnp).to(self.device) | |
| # if self.precision == 'half': ret = ret.half() | |
| return ret | |
| # prepare augmented data | |
| lq_list = [self.lq] | |
| for tf in 'v', 'h', 't': | |
| lq_list.extend([_transform(t, tf) for t in lq_list]) | |
| # inference | |
| if hasattr(self, 'net_g_ema'): | |
| self.net_g_ema.eval() | |
| with torch.no_grad(): | |
| out_list = [self.net_g_ema(aug) for aug in lq_list] | |
| else: | |
| self.net_g.eval() | |
| with torch.no_grad(): | |
| out_list = [self.net_g_ema(aug) for aug in lq_list] | |
| self.net_g.train() | |
| # merge results | |
| for i in range(len(out_list)): | |
| if i > 3: | |
| out_list[i] = _transform(out_list[i], 't') | |
| if i % 4 > 1: | |
| out_list[i] = _transform(out_list[i], 'h') | |
| if (i % 4) % 2 == 1: | |
| out_list[i] = _transform(out_list[i], 'v') | |
| output = torch.cat(out_list, dim=0) | |
| self.output = output.mean(dim=0, keepdim=True) | |
| 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 | |
| if with_metrics: | |
| 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() | |
| visuals = self.get_current_visuals() | |
| sr_img = tensor2img([visuals['result']]) | |
| metric_data['img'] = sr_img | |
| if 'gt' in visuals: | |
| gt_img = tensor2img([visuals['gt']]) | |
| 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 get_current_visuals(self): | |
| out_dict = OrderedDict() | |
| out_dict['lq'] = self.lq.detach().cpu() | |
| out_dict['result'] = self.output.detach().cpu() | |
| if hasattr(self, 'gt'): | |
| out_dict['gt'] = self.gt.detach().cpu() | |
| return out_dict | |
| def save(self, epoch, current_iter): | |
| if hasattr(self, 'net_g_ema'): | |
| 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) | |