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| import logging | |
| import torch | |
| from os import path as osp | |
| from basicsr.data import build_dataloader, build_dataset | |
| from basicsr.models import build_model | |
| from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs | |
| from basicsr.utils.options import dict2str, parse_options | |
| def test_pipeline(root_path): | |
| # parse options, set distributed setting, set ramdom seed | |
| opt, _ = parse_options(root_path, 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 _, dataset_opt in sorted(opt['datasets'].items()): | |
| test_set = build_dataset(dataset_opt) | |
| test_loader = build_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 = build_model(opt) | |
| for test_loader in test_loaders: | |
| test_set_name = test_loader.dataset.opt['name'] | |
| logger.info(f'Testing {test_set_name}...') | |
| model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img']) | |
| if __name__ == '__main__': | |
| root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) | |
| test_pipeline(root_path) | |