def METHOD_NAME(self):
    """Tests vfnet head loss when truth is empty and non-empty."""
    s = 256
    img_metas = [{
        'img_shape': (s, s, 3),
        'scale_factor': 1,
        'pad_shape': (s, s, 3)
    }]
    train_cfg = Config(
        dict(
            assigner=dict(type='ATSSAssigner', topk=9),
            allowed_border=-1,
            pos_weight=-1,
            debug=False))
    # since VarFocal Loss is not supported on CPU
    vfnet_head = VFNetHead(
        num_classes=4,
        in_channels=1,
        train_cfg=train_cfg,
        loss_cls=dict(
            type='VarifocalLoss', use_sigmoid=True, loss_weight=1.0))
    feat = [
        torch.rand(1, 1, s // feat_size, s // feat_size)
        for feat_size in [4, 8, 16, 32, 64]
    ]
    cls_scores, bbox_preds, bbox_preds_refine = vfnet_head.forward(feat)
    # Test that empty ground truth encourages the network to predict
    # background
    gt_instances = InstanceData()
    gt_instances.bboxes = torch.empty((0, 4))
    gt_instances.labels = torch.LongTensor([])
    empty_gt_losses = vfnet_head.loss_by_feat(cls_scores, bbox_preds,
                                              bbox_preds_refine,
                                              [gt_instances], img_metas)
    # When there is no truth, the cls loss should be nonzero but there
    # should be no box loss.
    empty_cls_loss = empty_gt_losses['loss_cls']
    empty_box_loss = empty_gt_losses['loss_bbox']
    self.assertGreater(empty_cls_loss.item(), 0,
                       'cls loss should be non-zero')
    self.assertEqual(
        empty_box_loss.item(), 0,
        'there should be no box loss when there are no true boxes')
    # When truth is non-empty then both cls and box loss should be nonzero
    # for random inputs
    gt_instances = InstanceData()
    gt_instances.bboxes = torch.Tensor(
        [[23.6667, 23.8757, 238.6326, 151.8874]])
    gt_instances.labels = torch.LongTensor([2])
    one_gt_losses = vfnet_head.loss_by_feat(cls_scores, bbox_preds,
                                            bbox_preds_refine,
                                            [gt_instances], img_metas)
    onegt_cls_loss = one_gt_losses['loss_cls']
    onegt_box_loss = one_gt_losses['loss_bbox']
    self.assertGreater(onegt_cls_loss.item(), 0,
                       'cls loss should be non-zero')
    self.assertGreater(onegt_box_loss.item(), 0,
                       'box loss should be non-zero')