tools/handobj/train_net.py [262:417]:
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            preds = preds[0]

            if cfg.DATA.MULTI_LABEL:
                if cfg.NUM_GPUS > 1:
                    preds, labels = du.all_gather([preds, labels])
            else:
                # Compute the errors.
                ks = (1, 5) if cfg.MODEL.NUM_CLASSES >= 5 else (1, 1)
                num_topks_correct = metrics.topks_correct(preds, labels, ks)

                # Combine the errors across the GPUs.
                top1_err, top5_err = [
                    (1.0 - x / preds.size(0)) * 100.0 for x in num_topks_correct
                ]
                if cfg.NUM_GPUS > 1:
                    top1_err, top5_err = du.all_reduce([top1_err, top5_err])

                # Copy the errors from GPU to CPU (sync point).
                top1_err, top5_err = top1_err.item(), top5_err.item()

                val_meter.iter_toc()
                # Update and log stats.
                val_meter.update_stats(
                    top1_err, top5_err, inputs[0].size(0) * cfg.NUM_GPUS
                )
                # write to tensorboard format if available.
                if writer is not None:
                    writer.add_scalars(
                        {"Val/Top1_err": top1_err, "Val/Top5_err": top5_err},
                        global_step=len(val_loader) * cur_epoch + cur_iter,
                    )

            val_meter.update_predictions(preds, labels)

        val_meter.log_iter_stats(cur_epoch, cur_iter)
        val_meter.iter_tic()

    # Log epoch stats.
    val_meter.log_epoch_stats(cur_epoch)
    # write to tensorboard format if available.
    if writer is not None:
        if cfg.DETECTION.ENABLE:
            writer.add_scalars(
                {"Val/mAP": val_meter.full_map}, global_step=cur_epoch
            )
        all_preds_cpu = [
            pred.clone().detach().cpu() for pred in val_meter.all_preds
        ]
        all_labels_cpu = [
            label.clone().detach().cpu() for label in val_meter.all_labels
        ]
        writer.plot_eval(
            preds=all_preds_cpu, labels=all_labels_cpu, global_step=cur_epoch
        )

    val_meter.reset()


def calculate_and_update_precise_bn(loader, model, num_iters=200):
    """
    Update the stats in bn layers by calculate the precise stats.
    Args:
        loader (loader): data loader to provide training data.
        model (model): model to update the bn stats.
        num_iters (int): number of iterations to compute and update the bn stats.
    """

    def _gen_loader():
        for inputs, _, _, _ in loader:
            if isinstance(inputs, (list,)):
                for i in range(len(inputs)):
                    inputs[i] = inputs[i].cuda(non_blocking=True)
            else:
                inputs = inputs.cuda(non_blocking=True)
            yield inputs

    # Update the bn stats.
    update_bn_stats(model, _gen_loader(), num_iters)


def build_trainer(cfg):
    """
    Build training model and its associated tools, including optimizer,
    dataloaders and meters.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    Returns:
        model (nn.Module): training model.
        optimizer (Optimizer): optimizer.
        train_loader (DataLoader): training data loader.
        val_loader (DataLoader): validatoin data loader.
        precise_bn_loader (DataLoader): training data loader for computing
            precise BN.
        train_meter (TrainMeter): tool for measuring training stats.
        val_meter (ValMeter): tool for measuring validation stats.
    """
    # Build the video model and print model statistics.
    model = build_model(cfg)
    if du.is_master_proc() and cfg.LOG_MODEL_INFO:
        misc.log_model_info(model, cfg, use_train_input=True)

    # Construct the optimizer.
    optimizer = optim.construct_optimizer(model, cfg)

    # Create the video train and val loaders.
    train_loader = loader.construct_loader(cfg, "train")
    val_loader = loader.construct_loader(cfg, "val")
    precise_bn_loader = loader.construct_loader(
        cfg, "train", is_precise_bn=True
    )
    # Create meters.
    train_meter = TrainMeter(len(train_loader), cfg)
    val_meter = ValMeter(len(val_loader), cfg)

    return (
        model,
        optimizer,
        train_loader,
        val_loader,
        precise_bn_loader,
        train_meter,
        val_meter,
    )


def train(cfg):
    """
    Train a video model for many epochs on train set and evaluate it on val set.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    # Set up environment.
    du.init_distributed_training(cfg)
    # Set random seed from configs.
    np.random.seed(cfg.RNG_SEED)
    torch.manual_seed(cfg.RNG_SEED)

    # Setup logging format.
    logging.setup_logging(cfg.OUTPUT_DIR)

    # Init multigrid.
    multigrid = None
    if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE:
        multigrid = MultigridSchedule()
        cfg = multigrid.init_multigrid(cfg)
        if cfg.MULTIGRID.LONG_CYCLE:
            cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0)
    # Print config.
    logger.info("Train with config:")
    logger.info(pprint.pformat(cfg))

    # Build the video model and print model statistics.
    model = build_model(cfg)
    if du.is_master_proc() and cfg.LOG_MODEL_INFO:
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tools/kd/train_net.py [256:411]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            preds = preds[0]

            if cfg.DATA.MULTI_LABEL:
                if cfg.NUM_GPUS > 1:
                    preds, labels = du.all_gather([preds, labels])
            else:
                # Compute the errors.
                ks = (1, 5) if cfg.MODEL.NUM_CLASSES >= 5 else (1, 1)
                num_topks_correct = metrics.topks_correct(preds, labels, ks)

                # Combine the errors across the GPUs.
                top1_err, top5_err = [
                    (1.0 - x / preds.size(0)) * 100.0 for x in num_topks_correct
                ]
                if cfg.NUM_GPUS > 1:
                    top1_err, top5_err = du.all_reduce([top1_err, top5_err])

                # Copy the errors from GPU to CPU (sync point).
                top1_err, top5_err = top1_err.item(), top5_err.item()

                val_meter.iter_toc()
                # Update and log stats.
                val_meter.update_stats(
                    top1_err, top5_err, inputs[0].size(0) * cfg.NUM_GPUS
                )
                # write to tensorboard format if available.
                if writer is not None:
                    writer.add_scalars(
                        {"Val/Top1_err": top1_err, "Val/Top5_err": top5_err},
                        global_step=len(val_loader) * cur_epoch + cur_iter,
                    )

            val_meter.update_predictions(preds, labels)

        val_meter.log_iter_stats(cur_epoch, cur_iter)
        val_meter.iter_tic()

    # Log epoch stats.
    val_meter.log_epoch_stats(cur_epoch)
    # write to tensorboard format if available.
    if writer is not None:
        if cfg.DETECTION.ENABLE:
            writer.add_scalars(
                {"Val/mAP": val_meter.full_map}, global_step=cur_epoch
            )
        all_preds_cpu = [
            pred.clone().detach().cpu() for pred in val_meter.all_preds
        ]
        all_labels_cpu = [
            label.clone().detach().cpu() for label in val_meter.all_labels
        ]
        writer.plot_eval(
            preds=all_preds_cpu, labels=all_labels_cpu, global_step=cur_epoch
        )

    val_meter.reset()


def calculate_and_update_precise_bn(loader, model, num_iters=200):
    """
    Update the stats in bn layers by calculate the precise stats.
    Args:
        loader (loader): data loader to provide training data.
        model (model): model to update the bn stats.
        num_iters (int): number of iterations to compute and update the bn stats.
    """

    def _gen_loader():
        for inputs, _, _, _ in loader:
            if isinstance(inputs, (list,)):
                for i in range(len(inputs)):
                    inputs[i] = inputs[i].cuda(non_blocking=True)
            else:
                inputs = inputs.cuda(non_blocking=True)
            yield inputs

    # Update the bn stats.
    update_bn_stats(model, _gen_loader(), num_iters)


def build_trainer(cfg):
    """
    Build training model and its associated tools, including optimizer,
    dataloaders and meters.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    Returns:
        model (nn.Module): training model.
        optimizer (Optimizer): optimizer.
        train_loader (DataLoader): training data loader.
        val_loader (DataLoader): validatoin data loader.
        precise_bn_loader (DataLoader): training data loader for computing
            precise BN.
        train_meter (TrainMeter): tool for measuring training stats.
        val_meter (ValMeter): tool for measuring validation stats.
    """
    # Build the video model and print model statistics.
    model = build_model(cfg)
    if du.is_master_proc() and cfg.LOG_MODEL_INFO:
        misc.log_model_info(model, cfg, use_train_input=True)

    # Construct the optimizer.
    optimizer = optim.construct_optimizer(model, cfg)

    # Create the video train and val loaders.
    train_loader = loader.construct_loader(cfg, "train")
    val_loader = loader.construct_loader(cfg, "val")
    precise_bn_loader = loader.construct_loader(
        cfg, "train", is_precise_bn=True
    )
    # Create meters.
    train_meter = TrainMeter(len(train_loader), cfg)
    val_meter = ValMeter(len(val_loader), cfg)

    return (
        model,
        optimizer,
        train_loader,
        val_loader,
        precise_bn_loader,
        train_meter,
        val_meter,
    )


def train(cfg):
    """
    Train a video model for many epochs on train set and evaluate it on val set.
    Args:
        cfg (CfgNode): configs. Details can be found in
            slowfast/config/defaults.py
    """
    # Set up environment.
    du.init_distributed_training(cfg)
    # Set random seed from configs.
    np.random.seed(cfg.RNG_SEED)
    torch.manual_seed(cfg.RNG_SEED)

    # Setup logging format.
    logging.setup_logging(cfg.OUTPUT_DIR)

    # Init multigrid.
    multigrid = None
    if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE:
        multigrid = MultigridSchedule()
        cfg = multigrid.init_multigrid(cfg)
        if cfg.MULTIGRID.LONG_CYCLE:
            cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0)
    # Print config.
    logger.info("Train with config:")
    logger.info(pprint.pformat(cfg))

    # Build the video model and print model statistics.
    model = build_model(cfg)
    if du.is_master_proc() and cfg.LOG_MODEL_INFO:
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