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| import os | |
| import os.path as osp | |
| from collections import OrderedDict | |
| import yaml | |
| def ordered_yaml(): | |
| """Support OrderedDict for yaml. | |
| Returns: | |
| yaml Loader and Dumper. | |
| """ | |
| try: | |
| from yaml import CDumper as Dumper | |
| from yaml import CLoader as Loader | |
| except ImportError: | |
| from yaml import Dumper, Loader | |
| _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG | |
| def dict_representer(dumper, data): | |
| return dumper.represent_dict(data.items()) | |
| def dict_constructor(loader, node): | |
| return OrderedDict(loader.construct_pairs(node)) | |
| Dumper.add_representer(OrderedDict, dict_representer) | |
| Loader.add_constructor(_mapping_tag, dict_constructor) | |
| return Loader, Dumper | |
| def parse(opt_path, is_train=True): | |
| """Parse option file. | |
| Args: | |
| opt_path (str): Option file path. | |
| is_train (str): Indicate whether in training or not. Default: True. | |
| Returns: | |
| (dict): Options. | |
| """ | |
| with open(opt_path, mode='r') as f: | |
| Loader, _ = ordered_yaml() | |
| opt = yaml.load(f, Loader=Loader) | |
| gpu_list = ','.join(str(x) for x in opt['gpu_ids']) | |
| if opt.get('set_CUDA_VISIBLE_DEVICES', None): | |
| os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list | |
| print('export CUDA_VISIBLE_DEVICES=' + gpu_list, flush=True) | |
| else: | |
| print('gpu_list: ', gpu_list, flush=True) | |
| opt['is_train'] = is_train | |
| # paths | |
| opt['path'] = {} | |
| opt['path']['root'] = osp.abspath( | |
| osp.join(__file__, 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']['log'] = experiments_root | |
| opt['path']['visualization'] = osp.join(experiments_root, | |
| 'visualization') | |
| # change some options for debug mode | |
| if 'debug' in opt['name']: | |
| opt['debug'] = True | |
| opt['val_freq'] = 1 | |
| opt['print_freq'] = 1 | |
| opt['save_checkpoint_freq'] = 1 | |
| else: # test | |
| results_root = osp.join(opt['path']['root'], 'results', opt['name']) | |
| opt['path']['results_root'] = results_root | |
| opt['path']['log'] = results_root | |
| opt['path']['visualization'] = osp.join(results_root, 'visualization') | |
| return opt | |
| def dict2str(opt, indent_level=1): | |
| """dict to string for printing options. | |
| Args: | |
| opt (dict): Option dict. | |
| indent_level (int): Indent level. Default: 1. | |
| Return: | |
| (str): Option string for printing. | |
| """ | |
| msg = '' | |
| for k, v in opt.items(): | |
| if isinstance(v, dict): | |
| msg += ' ' * (indent_level * 2) + k + ':[\n' | |
| msg += dict2str(v, indent_level + 1) | |
| msg += ' ' * (indent_level * 2) + ']\n' | |
| else: | |
| msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n' | |
| return msg | |
| class NoneDict(dict): | |
| """None dict. It will return none if key is not in the dict.""" | |
| def __missing__(self, key): | |
| return None | |
| def dict_to_nonedict(opt): | |
| """Convert to NoneDict, which returns None for missing keys. | |
| Args: | |
| opt (dict): Option dict. | |
| Returns: | |
| (dict): NoneDict for options. | |
| """ | |
| 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 | |