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# 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) |