44b89330c2
This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways:
1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped.
2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but
the model appears unable to fight the discriminator, so the G-loss steadily increases.
Going forwards, I want to try some bigger models. In particular, I want to change the generator
to increase complexity and capacity. I also want to add skip connections between the
disc and generator.
118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
import os
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import os.path as osp
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import logging
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import yaml
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from utils.util import OrderedYaml
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Loader, Dumper = OrderedYaml()
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def parse(opt_path, is_train=True):
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with open(opt_path, mode='r') as f:
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opt = yaml.load(f, Loader=Loader)
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# export CUDA_VISIBLE_DEVICES
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if 'gpu_ids' in opt.keys():
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gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
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os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
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print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
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opt['is_train'] = is_train
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if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
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scale = opt['scale']
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# datasets
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for phase, dataset in opt['datasets'].items():
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phase = phase.split('_')[0]
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dataset['phase'] = phase
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if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
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dataset['scale'] = scale
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is_lmdb = False
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if dataset.get('dataroot_GT', None) is not None:
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dataset['dataroot_GT'] = osp.expanduser(dataset['dataroot_GT'])
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if dataset['dataroot_GT'].endswith('lmdb'):
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is_lmdb = True
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if dataset.get('dataroot_LQ', None) is not None:
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dataset['dataroot_LQ'] = osp.expanduser(dataset['dataroot_LQ'])
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if dataset['dataroot_LQ'].endswith('lmdb'):
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is_lmdb = True
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dataset['data_type'] = 'lmdb' if is_lmdb else 'img'
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if dataset['mode'].endswith('mc'): # for memcached
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dataset['data_type'] = 'mc'
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dataset['mode'] = dataset['mode'].replace('_mc', '')
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# path
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for key, path in opt['path'].items():
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if path and key in opt['path'] and key != 'strict_load':
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opt['path'][key] = osp.expanduser(path)
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opt['path']['root'] = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
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if is_train:
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experiments_root = osp.join(opt['path']['root'], 'experiments', opt['name'])
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opt['path']['experiments_root'] = experiments_root
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opt['path']['models'] = osp.join(experiments_root, 'models')
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opt['path']['training_state'] = osp.join(experiments_root, 'training_state')
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opt['path']['log'] = experiments_root
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opt['path']['val_images'] = osp.join(experiments_root, 'val_images')
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# change some options for debug mode
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if 'debug' in opt['name']:
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opt['train']['val_freq'] = 8
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opt['logger']['print_freq'] = 1
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opt['logger']['save_checkpoint_freq'] = 8
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else: # test
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results_root = osp.join(opt['path']['root'], 'results', opt['name'])
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opt['path']['results_root'] = results_root
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opt['path']['log'] = results_root
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# network
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if opt['distortion'] == 'sr' or opt['distortion'] == 'downsample':
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opt['network_G']['scale'] = scale
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return opt
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def dict2str(opt, indent_l=1):
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'''dict to string for logger'''
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msg = ''
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for k, v in opt.items():
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if isinstance(v, dict):
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msg += ' ' * (indent_l * 2) + k + ':[\n'
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msg += dict2str(v, indent_l + 1)
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msg += ' ' * (indent_l * 2) + ']\n'
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else:
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msg += ' ' * (indent_l * 2) + k + ': ' + str(v) + '\n'
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return msg
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class NoneDict(dict):
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def __missing__(self, key):
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return None
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# convert to NoneDict, which return None for missing key.
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def dict_to_nonedict(opt):
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if isinstance(opt, dict):
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new_opt = dict()
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for key, sub_opt in opt.items():
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new_opt[key] = dict_to_nonedict(sub_opt)
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return NoneDict(**new_opt)
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elif isinstance(opt, list):
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return [dict_to_nonedict(sub_opt) for sub_opt in opt]
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else:
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return opt
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def check_resume(opt, resume_iter):
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'''Check resume states and pretrain_model paths'''
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logger = logging.getLogger('base')
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if opt['path']['resume_state']:
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if opt['path'].get('pretrain_model_G', None) is not None or opt['path'].get(
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'pretrain_model_D', None) is not None:
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logger.warning('pretrain_model path will be ignored when resuming training.')
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opt['path']['pretrain_model_G'] = osp.join(opt['path']['models'],
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'{}_G.pth'.format(resume_iter))
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logger.info('Set [pretrain_model_G] to ' + opt['path']['pretrain_model_G'])
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if 'gan' in opt['model']:
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opt['path']['pretrain_model_D'] = osp.join(opt['path']['models'],
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'{}_D.pth'.format(resume_iter))
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logger.info('Set [pretrain_model_D] to ' + opt['path']['pretrain_model_D'])
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