diff --git a/visualDet3D/networks/pipelines/testers.py b/visualDet3D/networks/pipelines/testers.py
index efa3925..f0e9482 100644
--- a/visualDet3D/networks/pipelines/testers.py
+++ b/visualDet3D/networks/pipelines/testers.py
@@ -39,4 +39,52 @@ def test_stereo_detection(data, module:nn.Module,
     scores, bbox, obj_index = module([left_images.cuda().float().contiguous(), right_images.cuda().float().contiguous(), torch.tensor(P2).cuda().float(), torch.tensor(P3).cuda().float()])
     obj_types = [cfg.obj_types[i.item()] for i in obj_index]
 
-    return scores, bbox, obj_types
\ No newline at end of file
+    return scores, bbox, obj_types
+
+@PIPELINE_DICT.register_module
+@torch.no_grad()
+def test_mono_detection_diggingmono3d(data, module:nn.Module,
+                     writer:SummaryWriter, 
+                     loss_logger:LossLogger=None, 
+                     global_step:int=None, 
+                     cfg:EasyDict=EasyDict())-> Tuple[torch.Tensor, torch.Tensor, List[str]]:
+    image, P2 = data[0], data[1]
+
+    scores, bbox, obj_index = module(
+        [image.cuda().float().contiguous(), torch.tensor(P2).cuda().float()])
+    
+    if len(scores) == 0:
+        return scores, bbox, obj_index
+    
+    expanded_scores = []
+    expanded_bboxes = []
+    expanded_obj_index = []
+    
+    for i in range(len(scores)):
+        expanded_scores.append(scores[i])
+        expanded_bboxes.append(bbox[i])
+        expanded_obj_index.append(obj_index[i])
+        depth = bbox[i, 6]
+        if depth < 10:
+            # no augmentation for these measurements
+            continue
+        
+        lamda = 80
+        for depth_shift in [-2, -1, -0.5, 0.5, 1, 2]:
+            new_depth = depth + depth_shift
+            new_bbox = bbox[i].clone()
+            new_bbox[6] = new_depth
+            sigma = torch.exp(depth / lamda)
+            t_s = torch.exp( - depth_shift**2 / sigma**2)
+            new_score = scores[i] * t_s
+            expanded_scores.append(new_score)
+            expanded_bboxes.append(new_bbox)
+            expanded_obj_index.append(obj_index[i])
+        
+    expanded_scores = torch.stack(expanded_scores)
+    expanded_bboxes = torch.stack(expanded_bboxes, dim=0)
+        
+    
+    obj_types = [cfg.obj_types[i.item()] for i in expanded_obj_index]
+
+    return expanded_scores, expanded_bboxes, obj_types