File size: 4,166 Bytes
06f2b0d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | import yaml
from collections import OrderedDict
from os import path as osp
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)
opt['is_train'] = is_train
# datasets
for phase, dataset in opt['datasets'].items():
# for several datasets, e.g., test_1, test_2
phase = phase.split('_')[0]
dataset['phase'] = phase
if 'scale' in opt:
dataset['scale'] = opt['scale']
if dataset.get('dataroot_gt') is not None:
dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt'])
if dataset.get('dataroot_lq') is not None:
if isinstance(dataset['dataroot_lq'], list):
for i in range(len(dataset['dataroot_lq'])):
dataset['dataroot_lq'][i] = osp.expanduser(dataset['dataroot_lq'][i])
else:
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):
if isinstance(opt['path'][key], list):
# for pretrain_network_g, pretrain_network_d
for i in range(len(opt['path'][key])):
opt['path'][key][i] = osp.expanduser(opt['path'][key][i])
else:
opt['path'][key] = osp.expanduser(val)
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_states'] = osp.join(experiments_root,
'training_states')
opt['path']['log'] = experiments_root
opt['path']['visualization'] = osp.join(experiments_root,
'visualization')
# change some options for debug mode
if 'debug' in opt['name']:
if 'val' in opt:
opt['val']['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
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 = '\n'
for k, v in opt.items():
if isinstance(v, dict):
msg += ' ' * (indent_level * 2) + k + ':['
msg += dict2str(v, indent_level + 1)
msg += ' ' * (indent_level * 2) + ']\n'
else:
msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n'
return msg
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