# modified from https://github.com/Andrew0613/X-Restormer/blob/master/xrestormer/test.py # flake8: noqa import os.path as osp import xrestormer.archs import xrestormer.data import xrestormer.models from basicsr.test import test_pipeline 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, ordered_yaml, _postprocess_yml_value from basicsr.models.sr_model import SRModel import argparse import random import torch import yaml from collections import OrderedDict from os import path as osp from basicsr.utils import set_random_seed # from basicsr.utils.dist_util import get_dist_info, init_dist, master_only config = { "xrestormer.denoise_50":{ "yaml": "/hdd/Restoration/Inference/X-Restormer/options/test/002_xrestormer_denoise.yml" }, "xrestormer.derain":{ "yaml": "/hdd/Restoration/Inference/X-Restormer/options/test/004_xrestormer_derain.yml" }, "xrestormer.dehaze":{ "yaml": "/hdd/Restoration/Inference/X-Restormer/options/test/005_xrestormer_dehaze.yml" }, "xrestormer.deblur":{ "yaml": "/hdd/Restoration/Inference/X-Restormer/options/test/003_xrestormer_deblur.yml" }, "xrestormer.super_resolution":{ "yaml": "/hdd/Restoration/Inference/X-Restormer/options/test/001_xrestormer_sr.yml" } } def custom_parse_options(root_path, is_train=True): parser = argparse.ArgumentParser() parser = argparse.ArgumentParser(description="Single image restoration using Restormer") parser.add_argument("--input", required=True, type=str, help="Path to input image") parser.add_argument("--output", required=True, type=str, help="Path to save output image") parser.add_argument("--model", required=True, choices=['xrestormer.denoise_50', 'xrestormer.derain', 'xrestormer.dehaze', 'xrestormer.deblur', 'xrestormer.super_resolution'], help="Model type to use") args = parser.parse_args() # parser.add_argument('--opt', type=str, required=True, help='Path to option YAML file.') # parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') # parser.add_argument('--auto_resume', action='store_true') # parser.add_argument('--debug', action='store_true') # parser.add_argument('--local_rank', type=int, default=0) parser.add_argument( '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999') args = parser.parse_args() opt = config[args.model]['yaml'] # parse yml to dict with open(opt, mode='r') as f: opt = yaml.load(f, Loader=ordered_yaml()[0]) # init_dist('pytorch') # opt['rank'], opt['world_size'] = get_dist_info() # random seed seed = opt.get('manual_seed') if seed is None: seed = random.randint(1, 10000) opt['manual_seed'] = seed # set_random_seed(seed + opt['rank']) # force to update yml options if args.force_yml is not None: for entry in args.force_yml: # now do not support creating new keys keys, value = entry.split('=') keys, value = keys.strip(), value.strip() value = _postprocess_yml_value(value) eval_str = 'opt' for key in keys.split(':'): eval_str += f'["{key}"]' eval_str += '=value' # using exec function exec(eval_str) opt['auto_resume'] = False opt['is_train'] = False # debug setting # if args.debug and not opt['name'].startswith('debug'): # opt['name'] = 'debug_' + opt['name'] if opt['num_gpu'] == 'auto': opt['num_gpu'] = torch.cuda.device_count() # datasets for phase, dataset in opt['datasets'].items(): # for multiple datasets, e.g., val_1, val_2; test_1, test_2 phase = phase.split('_')[0] dataset['phase'] = phase if 'scale' in opt: dataset['scale'] = opt['scale'] dataset['dataroot_lq'] = args.input # if dataset.get('dataroot_gt') is not None: # dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt']) # if dataset.get('dataroot_lq') is not None: # dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq']) # paths for key, val in opt['path'].items(): if (val is not None) and ('resume_state' in key or 'pretrain_network' in key): opt['path'][key] = osp.expanduser(val) # opt['path']['results'] = args.output ################### All for this!!!!!!!!!!!!!!! ################### # results_root = osp.join(root_path, 'results', opt['name']) results_root = args.output #osp.join(opt['path']['results'], opt['name']) ################### All for this!!!!!!!!!!!!!!! ################### opt['path']['results_root'] = results_root opt['path']['log'] = results_root opt['path']['visualization'] = osp.join(results_root, 'visualization') return opt, args def custom_test_pipeline(root_path): # parse options, set distributed setting, set ramdom seed # opt, _ = parse_options(root_path, is_train=False) opt, _ = custom_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 opt['dist'] = False 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: SRModel = 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) custom_test_pipeline(root_path)