def METHOD_NAME(self):
    """Tests guided anchor loss when truth is empty and non-empty."""
    s = 256
    img_metas = [{
        'img_shape': (s, s),
        'pad_shape': (s, s),
        'scale_factor': (1, 1)
    }]
    guided_anchor_head = GuidedAnchorHead(**guided_anchor_head_config)
    feats = (
        torch.rand(1, 4, s // stride[1], s // stride[0])
        for stride in guided_anchor_head.square_anchor_generator.strides)
    outs = guided_anchor_head(feats)
    # 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 = guided_anchor_head.loss_by_feat(
        *outs, [gt_instances], img_metas)
    # When there is no truth, the cls loss should be nonzero but
    # box shape and location loss should be zero
    empty_cls_loss = sum(empty_gt_losses['loss_cls']).item()
    empty_box_loss = sum(empty_gt_losses['loss_bbox']).item()
    empty_shape_loss = sum(empty_gt_losses['loss_shape']).item()
    empty_loc_loss = sum(empty_gt_losses['loss_loc']).item()
    self.assertGreater(empty_cls_loss, 0, 'cls loss should be non-zero')
    self.assertGreater(empty_loc_loss, 0,
                       'location loss should be non-zero')
    self.assertEqual(
        empty_box_loss, 0,
        'there should be no box loss when there are no true boxes')
    self.assertEqual(
        empty_shape_loss, 0,
        'there should be no shape loss when there are no true boxes')
    # When truth is non-empty then all cls, box loss and centerness 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 = guided_anchor_head.loss_by_feat(
        *outs, [gt_instances], img_metas)
    onegt_cls_loss = sum(one_gt_losses['loss_cls']).item()
    onegt_box_loss = sum(one_gt_losses['loss_bbox']).item()
    onegt_shape_loss = sum(one_gt_losses['loss_shape']).item()
    onegt_loc_loss = sum(one_gt_losses['loss_loc']).item()
    self.assertGreater(onegt_cls_loss, 0, 'cls loss should be non-zero')
    self.assertGreater(onegt_box_loss, 0, 'box loss should be non-zero')
    self.assertGreater(onegt_shape_loss, 0,
                       'shape loss should be non-zero')
    self.assertGreater(onegt_loc_loss, 0,
                       'location loss should be non-zero')