def single_gpu_test(model,
                    data_loader,
                    show=False,
                    out_dir=None,
                    efficient_test=False,
                    opacity=0.5):
    """Test with single GPU.

    Args:
        model (nn.Module): Model to be tested.
        data_loader (utils.data.Dataloader): Pytorch data loader.
        show (bool): Whether show results during inference. Default: False.
        out_dir (str, optional): If specified, the results will be dumped into
            the directory to save output results.
        efficient_test (bool): Whether save the results as local numpy files to
            save CPU memory during evaluation. Default: False.
        opacity(float): Opacity of painted segmentation map.
            Default 0.5.
            Must be in (0, 1] range.
    Returns:
        list: The prediction results.
    """

    model.eval()
    results = []
    dataset = data_loader.dataset
    prog_bar = mmcv.ProgressBar(len(dataset))
    for i, data in enumerate(data_loader):
        with torch.no_grad():
            result = model(return_loss=False, **data)

        if show or out_dir:
            img_tensor = data['img'][0]
            img_metas = data['img_metas'][0].data[0]
            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
            assert len(imgs) == len(img_metas)

            for img, img_meta in zip(imgs, img_metas):
                h, w, _ = img_meta['img_shape']
                img_show = img[:h, :w, :]

                ori_h, ori_w = img_meta['ori_shape'][:-1]
                img_show = mmcv.imresize(img_show, (ori_w, ori_h))

                if out_dir:
                    out_file = osp.join(out_dir, img_meta['ori_filename'])
                else:
                    out_file = None

                model.module.show_result(
                    img_show,
                    result,
                    palette=dataset.PALETTE,
                    show=show,
                    out_file=out_file,
                    opacity=opacity)

        if isinstance(result, list):
            if efficient_test:
                result = [np2tmp(_) for _ in result]
            results.extend(result)
        else:
            if efficient_test:
                result = np2tmp(result)
            results.append(result)

        batch_size = len(result)
        for _ in range(batch_size):
            prog_bar.update()
    return results
Test with single GPU.

    Args:
        model (nn.Module): Model to be tested.
        data_loader (utils.data.Dataloader): Pytorch data loader.
        show (bool): Whether show results during inference. Default: False.
        out_dir (str, optional): If specified, the results will be dumped into
            the directory to save output results.
        efficient_test (bool): Whether save the results as local numpy files to
            save CPU memory during evaluation. Default: False.
        opacity(float): Opacity of painted segmentation map.
            Default 0.5.
            Must be in (0, 1] range.
    Returns:
        list: The prediction results.
    
ControlNet-v1-1-nightly/annotator/uniformer/mmseg/apis/test.py