forked from mrq/DL-Art-School
117 lines
4.3 KiB
Python
117 lines
4.3 KiB
Python
|
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
|
||
|
|
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':
|
||
|
|
scale = opt['scale']
|
||
|
|
|
||
|
|
# datasets
|
||
|
|
for phase, dataset in opt['datasets'].items():
|
||
|
|
phase = phase.split('_')[0]
|
||
|
|
dataset['phase'] = phase
|
||
|
|
if opt['distortion'] == 'sr':
|
||
|
|
dataset['scale'] = scale
|
||
|
|
is_lmdb = False
|
||
|
|
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':
|
||
|
|
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']:
|
||
|
|
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'])
|