def main():
args = get_parser()
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(x) for x in args.train_gpu)
if args.manual_seed is not None:
random.seed(args.manual_seed)
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed(args.manual_seed)
torch.cuda.manual_seed_all(args.manual_seed)
cudnn.benchmark = False
cudnn.deterministic = True
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
args.ngpus_per_node = len(args.train_gpu)
if len(args.train_gpu) == 1:
args.sync_bn = False
args.distributed = False
args.multiprocessing_distributed = False
if args.multiprocessing_distributed:
port = find_free_port()
args.dist_url = f"tcp://127.0.0.1:{port}"
args.world_size = args.ngpus_per_node * args.world_size
mp.spawn(main_worker,
nprocs=args.ngpus_per_node,
args=(args.ngpus_per_node, args))
else:
main_worker(args.train_gpu, args.ngpus_per_node, args)
def main():
args = get_parser()
if args.test_in_nyu_label_space:
args.colors_path = 'nyu/nyu_colors.txt'
args.names_path = 'nyu/nyu_names.txt'
args.classes = 41
if args.if_cluster:
args.data_root = args.data_root_cluster
args.project_path = args.project_path_cluster
args.data_config_path = 'data'
for key in [
'train_list', 'val_list', 'test_list', 'colors_path', 'names_path'
]:
args[key] = os.path.join(args.data_config_path, args[key])
for key in ['save_path', 'model_path', 'save_folder']:
args[key] = os.path.join(args.project_path, args[key])
for key in ['save_path']:
args[key] = args[key] % args.exp_name
check(args)
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(
str(x) for x in args.train_gpu)
if args.manual_seed is not None:
random.seed(args.manual_seed)
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed(args.manual_seed)
torch.cuda.manual_seed_all(args.manual_seed)
cudnn.benchmark = False
cudnn.deterministic = True
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
args.ngpus_per_node = len(args.train_gpu)
if len(args.train_gpu) == 1:
args.sync_bn = False
args.distributed = False
args.multiprocessing_distributed = False
if args.multiprocessing_distributed:
port = find_free_port()
args.dist_url = f"tcp://127.0.0.1:{port}"
args.world_size = args.ngpus_per_node * args.world_size
mp.spawn(main_worker,
nprocs=args.ngpus_per_node,
args=(args.ngpus_per_node, args))
else:
main_worker(args.train_gpu, args.ngpus_per_node, args)
if __name__ == "__main__":
args = get_parser()
args.gpu = [int(i) for i in os.environ['CUDA_VISIBLE_DEVICES'].split(',')]
if args.manual_seed is not None:
random.seed(args.manual_seed)
np.random.seed(args.manual_seed)
torch.manual_seed(args.manual_seed)
torch.cuda.manual_seed(args.manual_seed)
torch.cuda.manual_seed_all(args.manual_seed)
cudnn.benchmark = False
cudnn.deterministic = True
if args.dist_url == "env://" and args.world_size == -1:
args.world_size = int(os.environ["WORLD_SIZE"])
args.distributed = args.world_size > 1 or args.multiprocessing_distributed
args.ngpus_per_node = len(args.gpu)
if len(args.gpu) == 1:
args.sync_bn = False
args.distributed = False
args.multiprocessing_distributed = False
if args.multiprocessing_distributed:
port = find_free_port()
args.dist_url = f"tcp://127.0.0.1:{port}"
args.world_size = args.ngpus_per_node * args.world_size
mp.spawn(main_worker,
nprocs=args.ngpus_per_node,
args=(args.ngpus_per_node, args))
else:
main_worker(args.gpu, args.ngpus_per_node, args)
