default=True,
help='Use CUDNN.')
parser.add_argument("--local_rank", default=0, type=int)
args = parser.parse_args()
configer = Configer(args_parser=args)
if args.seed is not None:
random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
cudnn.enabled = True
cudnn.benchmark = args.cudnn
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 and not configer.get(
'network.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('network.distributed',
default=False):
configer.update('network.gather', True)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add('project_dir', project_dir)
default=True,
help='Use CUDNN.')
parser.add_argument("--local_rank", default=0, type=int)
args = parser.parse_args()
configer = Configer(args_parser=args)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.enabled = True
cudnn.benchmark = args.cudnn
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 and not configer.get(
'network.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('network.distributed',
default=False):
configer.update('network.gather', True)
if configer.get('phase') == 'train':
assert len(configer.get('gpu')) > 1 or 'sync' not in configer.get(
