DL-Art-School/codes/options/options.py

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
    if 'path' in opt.keys():
        for key, path in opt['path'].items():
            if path and key in opt['path'] and key != 'strict_load':
                opt['path'][key] = osp.expanduser(path)
    else:
        opt['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.')

        if opt['model'] == 'extensibletrainer':
            for k in opt['networks'].keys():
                pt_key = 'pretrain_model_%s' % (k,)
                opt['path'][pt_key] = osp.join(opt['path']['models'],
                                          '{}_{}.pth'.format(resume_iter, k))
                logger.info('Set model [%s] to %s' % (k, opt['path'][pt_key]))
        else:
            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'])