def main()

in main_fixmatch.py [0:0]

• 21 lines of code
• 7 McCabe index (conditional complexity)

def main():
    args = parser.parse_args()
    assert args.warmup_epoch < args.schedule[0]
    print(args)

    if args.seed is not None:
        seed = args.seed + dist_utils.get_rank()
        torch.manual_seed(seed)
        np.random.seed(seed)
        # random.seed(seed)

    assert 0 < args.anno_percent < 1

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    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

    ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)