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.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 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 phase, 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)