diff --git a/tests/test_models/test_dense_heads/test_lad_head.py b/tests/test_models/test_dense_heads/test_lad_head.py
index 14af87461e7..2223917fdca 100644
--- a/tests/test_models/test_dense_heads/test_lad_head.py
+++ b/tests/test_models/test_dense_heads/test_lad_head.py
@@ -45,7 +45,7 @@ def score_samples(self, loss):
             allowed_border=-1,
             pos_weight=-1,
             debug=False))
-
+    # since Focal Loss is not supported on CPU
     self = LADHead(
         num_classes=4,
         in_channels=1,
@@ -55,7 +55,6 @@ def score_samples(self, loss):
         loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
         loss_centerness=dict(
             type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
-
     teacher_model = LADHead(
         num_classes=4,
         in_channels=1,
@@ -65,7 +64,6 @@ def score_samples(self, loss):
         loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
         loss_centerness=dict(
             type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
-
     feat = [
         torch.rand(1, 1, s // feat_size, s // feat_size)
         for feat_size in [4, 8, 16, 32, 64]
@@ -120,12 +118,25 @@ def score_samples(self, loss):
     assert len(results) == n
     assert results[0].size() == (h * w * 5, c)
     assert self.with_score_voting
+
+    self = LADHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
     cls_scores = [torch.ones(2, 4, 5, 5)]
     bbox_preds = [torch.ones(2, 4, 5, 5)]
     iou_preds = [torch.ones(2, 1, 5, 5)]
-    mlvl_anchors = [torch.ones(2, 5 * 5, 4)]
-    img_shape = None
-    scale_factor = [0.5, 0.5]
     cfg = mmcv.Config(
         dict(
             nms_pre=1000,
@@ -134,12 +145,12 @@ def score_samples(self, loss):
             nms=dict(type='nms', iou_threshold=0.6),
             max_per_img=100))
     rescale = False
-    self._get_bboxes(
-        cls_scores,
-        bbox_preds,
-        iou_preds,
-        mlvl_anchors,
-        img_shape,
-        scale_factor,
-        cfg,
-        rescale=rescale)
+    self.get_bboxes(
+        cls_scores, bbox_preds, iou_preds, img_metas, cfg, rescale=rescale)
+
+
+# ------------------------------------------------------------------------------
+#  Main execution
+# ------------------------------------------------------------------------------
+if __name__ == '__main__':
+    test_lad_head_loss()