import os.path as osp import logging import time import argparse from collections import OrderedDict import options.options as option import utils.util as util from data.util import bgr2ycbcr from data import create_dataset, create_dataloader from models import create_model from tqdm import tqdm import torch if __name__ == "__main__": #### options torch.backends.cudnn.benchmark = True want_just_images = True parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='../options/test_resgen_upsample.yml') opt = option.parse(parser.parse_args().opt, is_train=False) opt = option.dict_to_nonedict(opt) util.mkdirs( (path for key, path in opt['path'].items() if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key)) util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO, screen=True, tofile=True) logger = logging.getLogger('base') logger.info(option.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) logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set))) test_loaders.append(test_loader) model = create_model(opt) for test_loader in test_loaders: test_set_name = test_loader.dataset.opt['name'] logger.info('\nTesting [{:s}]...'.format(test_set_name)) test_start_time = time.time() dataset_dir = osp.join(opt['path']['results_root'], test_set_name) util.mkdir(dataset_dir) test_results = OrderedDict() test_results['psnr'] = [] test_results['ssim'] = [] test_results['psnr_y'] = [] test_results['ssim_y'] = [] tq = tqdm(test_loader) for data in tq: need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True model.feed_data(data, need_GT=need_GT) model.test() if isinstance(model.fake_H, tuple): visuals = model.fake_H[0].detach().float().cpu() else: visuals = model.fake_H.detach().float().cpu() for i in range(visuals.shape[0]): img_path = data['GT_path'][i] if need_GT else data['LQ_path'][i] img_name = osp.splitext(osp.basename(img_path))[0] sr_img = util.tensor2img(visuals[i]) # uint8 # save images suffix = opt['suffix'] if suffix: save_img_path = osp.join(dataset_dir, img_name + suffix + '.png') else: save_img_path = osp.join(dataset_dir, img_name + '.png') util.save_img(sr_img, save_img_path) if want_just_images: continue if not want_just_images and need_GT: # metrics # Average PSNR/SSIM results ave_psnr = sum(test_results['psnr']) / len(test_results['psnr']) ave_ssim = sum(test_results['ssim']) / len(test_results['ssim']) logger.info( '----Average PSNR/SSIM results for {}----\n\tPSNR: {:.6f} dB; SSIM: {:.6f}\n'.format( test_set_name, ave_psnr, ave_ssim)) if test_results['psnr_y'] and test_results['ssim_y']: ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y']) ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y']) logger.info( '----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'. format(ave_psnr_y, ave_ssim_y))