def METHOD_NAME(self):
    s = 256
    img_metas = [{
        'img_shape': (s, s, 3),
        'scale_factor': (1.0, 1.0),
    }]
    test_cfg = Config(
        dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
    head = YOLOXHead(
        num_classes=4,
        in_channels=1,
        stacked_convs=1,
        use_depthwise=False,
        test_cfg=test_cfg)
    feat = [
        torch.rand(1, 1, s // feat_size, s // feat_size)
        for feat_size in [4, 8, 16]
    ]
    cls_scores, bbox_preds, objectnesses = head.forward(feat)
    head.predict_by_feat(
        cls_scores,
        bbox_preds,
        objectnesses,
        img_metas,
        cfg=test_cfg,
        rescale=True,
        with_nms=True)
    head.predict_by_feat(
        cls_scores,
        bbox_preds,
        objectnesses,
        img_metas,
        cfg=test_cfg,
        rescale=False,
        with_nms=False)