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| import logging | |
| import torch | |
| from os import path as osp | |
| import sys | |
| from basicsr.data import create_dataloader, create_dataset | |
| from basicsr.models import create_model | |
| from basicsr.train import parse_options | |
| from basicsr.utils import (get_env_info, get_root_logger, get_time_str, | |
| make_exp_dirs) | |
| from basicsr.utils.options import dict2str | |
| def main(): | |
| # parse options, set distributed setting, set ramdom seed | |
| opt = parse_options(is_train=False) | |
| torch.backends.cudnn.benchmark = True | |
| # torch.backends.cudnn.deterministic = True | |
| # mkdir and initialize loggers | |
| make_exp_dirs(opt) | |
| log_file = osp.join(opt['path']['log'], | |
| f"test_{opt['name']}_{get_time_str()}.log") | |
| logger = get_root_logger( | |
| logger_name='basicsr', log_level=logging.INFO, log_file=log_file) | |
| logger.info(get_env_info()) | |
| logger.info(dict2str(opt)) | |
| # create test dataset and dataloader | |
| test_loaders = [] | |
| for phase, dataset_opt in sorted(opt['datasets'].items()): | |
| test_set = create_dataset(dataset_opt) | |
| test_loader = create_dataloader( | |
| test_set, | |
| dataset_opt, | |
| num_gpu=opt['num_gpu'], | |
| dist=opt['dist'], | |
| sampler=None, | |
| seed=opt['manual_seed']) | |
| logger.info( | |
| f"Number of test images in {dataset_opt['name']}: {len(test_set)}") | |
| test_loaders.append(test_loader) | |
| # create model | |
| model = create_model(opt) | |
| for test_loader in test_loaders: | |
| test_set_name = test_loader.dataset.opt['name'] | |
| logger.info(f'Testing {test_set_name}...') | |
| rgb2bgr = opt['val'].get('rgb2bgr', True) | |
| # wheather use uint8 image to compute metrics | |
| use_image = opt['val'].get('use_image', True) | |
| model.validation( | |
| test_loader, | |
| current_iter=opt['name'], | |
| tb_logger=None, | |
| save_img=opt['val']['save_img'], | |
| rgb2bgr=rgb2bgr, use_image=use_image) | |
| if __name__ == '__main__': | |
| main() | |