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file_dir = os.path.join(args.experiment_dir, '%s_SemSeg-' %
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(args.model_name) +
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str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M')))
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args.file_dir = file_dir
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code_dir = os.path.join(args.file_dir, 'code_log')
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if args.local_rank == 0:
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if not os.path.exists(args.experiment_dir):
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os.makedirs(args.experiment_dir)
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if not os.path.exists(file_dir):
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os.makedirs(file_dir)
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if not os.path.exists(code_dir):
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os.makedirs(code_dir)
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os.system('cp %s %s' % (args.config, code_dir))
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os.system('cp %s %s' % (os.path.basename(__file__), code_dir))
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os.system('cp %s %s' % ('scannet_data_loader_color_DDP.py', code_dir))
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os.system('cp %s %s' % ('model_architecture.py', code_dir))
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print("ignore label: ", args.ignore_label)
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if 'manual_seed' in args.keys():
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init_seeds(args.manual_seed)
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args.num_gpus = torch.cuda.device_count()
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main_worker(args)
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def main_worker(config):
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global args, best_iou
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args, best_iou = config, 0
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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args.DDP = True
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args.rank = int(os.environ["RANK"])
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local_rank = args.local_rank
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print("local_rank : ", local_rank)
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print("rank: ", args.rank)
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torch.cuda.set_device(local_rank)
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dist.init_process_group(
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backend='nccl',
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world_size=args.num_gpus,
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rank=local_rank)
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dist.barrier()
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init_seeds(args.manual_seed + torch.distributed.get_rank())
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else:
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args.DDP = False
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init_seeds(args.manual_seed)
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local_rank = 0
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if main_process():
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global logger, writer
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logger = get_logger(args.file_dir)
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logger.info(args)
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logger.info("=> Creating model ...")
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logger.info("Classes: {}".format(args.num_classes))
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writer = SummaryWriter(args.file_dir)
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train_data_loader, val_data_loader = scannet_data_loader.getdataLoadersDDP(
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args)
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# get model
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model = VI_PointConv(args).to(local_rank)
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if main_process():
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logger.info(model)
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logger.info("#Model parameters: {}".format(
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sum([x.nelement() for x in model.parameters()])))
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# make model to DDP
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if args.DDP:
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if args.sync_bn:
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model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).cuda()
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model = torch.nn.parallel.DistributedDataParallel(
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model, device_ids=[local_rank])
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# get loss func
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if args.USE_WEIGHT:
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if main_process():
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logger.info("use weight!")
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# Using pyTorch label smoothing
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if args.label_smoothing:
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criterion = nn.CrossEntropyLoss(
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weight=torch.tensor(
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args.weights).float(),
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ignore_index=args.ignore_label,
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label_smoothing=args.label_smoothing).cuda()
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else:
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criterion = nn.CrossEntropyLoss(
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weight=torch.tensor(
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args.weights).float(),
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ignore_index=args.ignore_label).cuda()
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else:
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if args.label_smoothing:
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criterion = nn.CrossEntropyLoss(
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ignore_index=args.ignore_label,
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label_smoothing=args.label_smoothing).cuda()
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