import os import os.path as osp import logging import yaml from utils.util import OrderedYaml Loader, Dumper = OrderedYaml() def parse(opt_path, is_train=True): with open(opt_path, mode='r') as f: opt = yaml.load(f, Loader=Loader) # export CUDA_VISIBLE_DEVICES if 'gpu_ids' in opt.keys(): gpu_list = ','.join(str(x) for x in opt['gpu_ids']) os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list print('export CUDA_VISIBLE_DEVICES=' + gpu_list) opt['is_train'] = is_train if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample': scale = opt['scale'] # datasets if 'datasets' in opt.keys(): for phase, dataset in opt['datasets'].items(): phase = phase.split('_')[0] dataset['phase'] = phase if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample': dataset['scale'] = scale is_lmdb = False ''' LMDB is not supported at this point with the mods I've been making. if dataset.get('dataroot_GT', None) is not None: dataset['dataroot_GT'] = osp.expanduser(dataset['dataroot_GT']) if dataset['dataroot_GT'].endswith('lmdb'): is_lmdb = True if dataset.get('dataroot_LQ', None) is not None: dataset['dataroot_LQ'] = osp.expanduser(dataset['dataroot_LQ']) if dataset['dataroot_LQ'].endswith('lmdb'): is_lmdb = True ''' dataset['data_type'] = 'lmdb' if is_lmdb else 'img' if dataset['mode'].endswith('mc'): # for memcached dataset['data_type'] = 'mc' dataset['mode'] = dataset['mode'].replace('_mc', '') # path for key, path in opt['path'].items(): if path and key in opt['path'] and key != 'strict_load': opt['path'][key] = osp.expanduser(path) opt['path']['root'] = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir, osp.pardir)) if is_train: experiments_root = osp.join(opt['path']['root'], 'experiments', opt['name']) opt['path']['experiments_root'] = experiments_root opt['path']['models'] = osp.join(experiments_root, 'models') opt['path']['training_state'] = osp.join(experiments_root, 'training_state') opt['path']['log'] = experiments_root opt['path']['val_images'] = osp.join(experiments_root, 'val_images') # change some options for debug mode if 'debug' in opt['name']: opt['train']['val_freq'] = 8 opt['logger']['print_freq'] = 1 opt['logger']['save_checkpoint_freq'] = 8 else: # test results_root = osp.join(opt['path']['root'], 'results', opt['name']) opt['path']['results_root'] = results_root opt['path']['log'] = results_root # network if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample': if 'network_G' in opt.keys(): opt['network_G']['scale'] = scale return opt def dict2str(opt, indent_l=1): '''dict to string for logger''' msg = '' for k, v in opt.items(): if isinstance(v, dict): msg += ' ' * (indent_l * 2) + k + ':[\n' msg += dict2str(v, indent_l + 1) msg += ' ' * (indent_l * 2) + ']\n' else: msg += ' ' * (indent_l * 2) + k + ': ' + str(v) + '\n' return msg class NoneDict(dict): def __missing__(self, key): return None # convert to NoneDict, which return None for missing key. def dict_to_nonedict(opt): if isinstance(opt, dict): new_opt = dict() for key, sub_opt in opt.items(): new_opt[key] = dict_to_nonedict(sub_opt) return NoneDict(**new_opt) elif isinstance(opt, list): return [dict_to_nonedict(sub_opt) for sub_opt in opt] else: return opt def check_resume(opt, resume_iter): '''Check resume states and pretrain_model paths''' logger = logging.getLogger('base') if opt['path']['resume_state']: if opt['path'].get('pretrain_model_G', None) is not None or opt['path'].get( 'pretrain_model_D', None) is not None: logger.warning('pretrain_model path will be ignored when resuming training.') opt['path']['pretrain_model_G'] = osp.join(opt['path']['models'], '{}_G.pth'.format(resume_iter)) logger.info('Set [pretrain_model_G] to ' + opt['path']['pretrain_model_G']) if 'gan' in opt['model'] or 'spsr' in opt['model']: opt['path']['pretrain_model_D'] = osp.join(opt['path']['models'], '{}_D.pth'.format(resume_iter)) logger.info('Set [pretrain_model_D] to ' + opt['path']['pretrain_model_D']) if 'spsr' in opt['model']: opt['path']['pretrain_model_D_grad'] = osp.join(opt['path']['models'], '{}_D_grad.pth'.format(resume_iter)) logger.info('Set [pretrain_model_D_grad] to ' + opt['path']['pretrain_model_D_grad'])