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| import torch | |
| from collections import Counter | |
| from os import path as osp | |
| from torch import distributed as dist | |
| from tqdm import tqdm | |
| from basicsr.metrics import calculate_metric | |
| from basicsr.utils import get_root_logger, imwrite, tensor2img | |
| from basicsr.utils.dist_util import get_dist_info | |
| from basicsr.utils.registry import MODEL_REGISTRY | |
| from .sr_model import SRModel | |
| class VideoBaseModel(SRModel): | |
| """Base video SR model.""" | |
| def dist_validation(self, dataloader, current_iter, tb_logger, save_img): | |
| dataset = dataloader.dataset | |
| dataset_name = dataset.opt['name'] | |
| with_metrics = self.opt['val']['metrics'] is not None | |
| # initialize self.metric_results | |
| # It is a dict: { | |
| # 'folder1': tensor (num_frame x len(metrics)), | |
| # 'folder2': tensor (num_frame x len(metrics)) | |
| # } | |
| if with_metrics: | |
| if not hasattr(self, 'metric_results'): # only execute in the first run | |
| self.metric_results = {} | |
| num_frame_each_folder = Counter(dataset.data_info['folder']) | |
| for folder, num_frame in num_frame_each_folder.items(): | |
| self.metric_results[folder] = torch.zeros( | |
| num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') | |
| # initialize the best metric results | |
| self._initialize_best_metric_results(dataset_name) | |
| # zero self.metric_results | |
| rank, world_size = get_dist_info() | |
| if with_metrics: | |
| for _, tensor in self.metric_results.items(): | |
| tensor.zero_() | |
| metric_data = dict() | |
| # record all frames (border and center frames) | |
| if rank == 0: | |
| pbar = tqdm(total=len(dataset), unit='frame') | |
| for idx in range(rank, len(dataset), world_size): | |
| val_data = dataset[idx] | |
| val_data['lq'].unsqueeze_(0) | |
| val_data['gt'].unsqueeze_(0) | |
| folder = val_data['folder'] | |
| frame_idx, max_idx = val_data['idx'].split('/') | |
| lq_path = val_data['lq_path'] | |
| self.feed_data(val_data) | |
| self.test() | |
| visuals = self.get_current_visuals() | |
| result_img = tensor2img([visuals['result']]) | |
| metric_data['img'] = result_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']: | |
| raise NotImplementedError('saving image is not supported during training.') | |
| else: | |
| if 'vimeo' in dataset_name.lower(): # vimeo90k dataset | |
| split_result = lq_path.split('/') | |
| img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}' | |
| else: # other datasets, e.g., REDS, Vid4 | |
| img_name = osp.splitext(osp.basename(lq_path))[0] | |
| if self.opt['val']['suffix']: | |
| save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, | |
| f'{img_name}_{self.opt["val"]["suffix"]}.png') | |
| else: | |
| save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, | |
| f'{img_name}_{self.opt["name"]}.png') | |
| imwrite(result_img, save_img_path) | |
| if with_metrics: | |
| # calculate metrics | |
| for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): | |
| result = calculate_metric(metric_data, opt_) | |
| self.metric_results[folder][int(frame_idx), metric_idx] += result | |
| # progress bar | |
| if rank == 0: | |
| for _ in range(world_size): | |
| pbar.update(1) | |
| pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}') | |
| if rank == 0: | |
| pbar.close() | |
| if with_metrics: | |
| if self.opt['dist']: | |
| # collect data among GPUs | |
| for _, tensor in self.metric_results.items(): | |
| dist.reduce(tensor, 0) | |
| dist.barrier() | |
| else: | |
| pass # assume use one gpu in non-dist testing | |
| if rank == 0: | |
| self._log_validation_metric_values(current_iter, dataset_name, tb_logger) | |
| def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): | |
| logger = get_root_logger() | |
| logger.warning('nondist_validation is not implemented. Run dist_validation.') | |
| self.dist_validation(dataloader, current_iter, tb_logger, save_img) | |
| def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): | |
| # ----------------- calculate the average values for each folder, and for each metric ----------------- # | |
| # average all frames for each sub-folder | |
| # metric_results_avg is a dict:{ | |
| # 'folder1': tensor (len(metrics)), | |
| # 'folder2': tensor (len(metrics)) | |
| # } | |
| metric_results_avg = { | |
| folder: torch.mean(tensor, dim=0).cpu() | |
| for (folder, tensor) in self.metric_results.items() | |
| } | |
| # total_avg_results is a dict: { | |
| # 'metric1': float, | |
| # 'metric2': float | |
| # } | |
| total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} | |
| for folder, tensor in metric_results_avg.items(): | |
| for idx, metric in enumerate(total_avg_results.keys()): | |
| total_avg_results[metric] += metric_results_avg[folder][idx].item() | |
| # average among folders | |
| for metric in total_avg_results.keys(): | |
| total_avg_results[metric] /= len(metric_results_avg) | |
| # update the best metric result | |
| self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter) | |
| # ------------------------------------------ log the metric ------------------------------------------ # | |
| log_str = f'Validation {dataset_name}\n' | |
| for metric_idx, (metric, value) in enumerate(total_avg_results.items()): | |
| log_str += f'\t # {metric}: {value:.4f}' | |
| for folder, tensor in metric_results_avg.items(): | |
| log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}' | |
| if hasattr(self, 'best_metric_results'): | |
| log_str += (f'\n\t Best: {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_idx, (metric, value) in enumerate(total_avg_results.items()): | |
| tb_logger.add_scalar(f'metrics/{metric}', value, current_iter) | |
| for folder, tensor in metric_results_avg.items(): | |
| tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter) | |