def _get_child_configer_transform(self, child_config_file):
dataset_configer = Configer(configs=child_config_file)
child_configer = self.configer.clone()
child_configer.params_root['data'].update(dataset_configer.get('data'))
if self.configer.exists(
'use_adaptive_transform') or self.dataset == 'val':
child_configer.params_root.update({
'train_trans':
dataset_configer.params_root['train_trans'],
'val_trans':
dataset_configer.params_root['val_trans'],
})
return child_configer, CV2AugCompose(split=self.dataset,
configer=child_configer)
args_parser = parser.parse_args()
from lib.utils.distributed import handle_distributed
handle_distributed(args_parser,
os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
data_dir = configer.get('data', 'data_dir')
if isinstance(data_dir, str):
data_dir = [data_dir]
abs_data_dir = [os.path.expanduser(x) for x in data_dir]
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
args_parser = parser.parse_args()
from lib.utils.distributed import handle_distributed
handle_distributed(args_parser,
os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
abs_data_dir = os.path.expanduser(configer.get('data', 'data_dir'))
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
default=False,
dest='distributed',
help='Use CUDNN.')
args_parser = parser.parse_args()
if args_parser.seed is not None:
random.seed(args_parser.seed + args_parser.local_rank)
torch.manual_seed(args_parser.seed + args_parser.local_rank)
if args_parser.gpu is not None:
torch.cuda.manual_seed_all(args_parser.seed +
args_parser.local_rank)
configer = Configer(args_parser=args_parser)
cudnn.enabled = True
if configer.get('data', 'multiscale') is None:
cudnn.benchmark = args_parser.cudnn
else:
cudnn.benchmark = False
if configer.get('gpu') is not None and not configer.get('distributed',
default=False):
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(gpu_id) for gpu_id in configer.get('gpu'))
if configer.get('network', 'norm_type') is None:
configer.update('network.norm_type', 'batchnorm')
if torch.cuda.device_count() <= 1 or configer.get('distributed',
default=False):
configer.update('network.gather', True)
args_parser = parser.parse_args()
from lib.utils.distributed import handle_distributed
handle_distributed(args_parser,
os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
print(args_parser)
configer = Configer(args_parser=args_parser)
data_dir = configer.get('data', 'data_dir')
if isinstance(data_dir, str):
data_dir = [data_dir]
abs_data_dir = [os.path.expanduser(x) for x in data_dir]
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(
log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
# *********** Params for env. **********
parser.add_argument('--seed', default=None, type=int, help='manual seed')
parser.add_argument('--cudnn', type=str2bool, nargs='?', default=True, help='Use CUDNN.')
args_parser = parser.parse_args()
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
abs_data_dir = os.path.expanduser(configer.get('data', 'data_dir'))
configer.update(['data', 'data_dir'], abs_data_dir)
if configer.get('gpu') is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(gpu_id) for gpu_id in configer.get('gpu'))
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
