Source code for pyiqa.test

import logging
import torch
from os import path as osp

from pyiqa.data import build_dataloader, build_dataset
from pyiqa.models import build_model
from pyiqa.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs
from pyiqa.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='pyiqa', 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)