diff --git a/ppdet/modeling/assigners/simota_assigner.py b/ppdet/modeling/assigners/simota_assigner.py
index abc055d29e3405174753e03501b40817accb583b..4ec87cbcf9527aa08c0ca3fd856b0a9a1af7170d 100644
--- a/ppdet/modeling/assigners/simota_assigner.py
+++ b/ppdet/modeling/assigners/simota_assigner.py
@@ -241,8 +241,8 @@ class SimOTAAssigner(object):
         pos_inds, neg_inds, pos_gt_bboxes, pos_assigned_gt_inds \
             = self.get_sample(assigned_gt_inds, gt_bboxes.numpy())
 
-        bbox_target = np.zeros_like(flatten_bboxes)
-        bbox_weight = np.zeros_like(flatten_bboxes)
+        bbox_target = np.zeros(flatten_bboxes.shape, paddle.common_ops_import.convert_dtype(flatten_bboxes.dtype))
+        bbox_weight = np.zeros_like(bbox_target)
         label = np.ones([num_bboxes], dtype=np.int64) * self.num_classes
         label_weight = np.zeros([num_bboxes], dtype=np.float32)
 
diff --git a/ppdet/modeling/transformers/matchers.py b/ppdet/modeling/transformers/matchers.py
index 72459a3f909806f212a4b204a50a494875589e51..8082c0e625563b9d4b80e21d8fdbe8c61763630a 100644
--- a/ppdet/modeling/transformers/matchers.py
+++ b/ppdet/modeling/transformers/matchers.py
@@ -175,10 +175,16 @@ class HungarianMatcher(nn.Layer):
         C = C.reshape([bs, num_queries, -1])
         C = [a.squeeze(0) for a in C.chunk(bs)]
         sizes = [a.shape[0] for a in gt_bbox]
-        indices = [
-            linear_sum_assignment(c.split(sizes, -1)[i].numpy())
-            for i, c in enumerate(C)
-        ]
+        if hasattr(paddle.Tensor, "contiguous"):
+            indices = [
+                linear_sum_assignment(c.split(sizes, -1)[i].contiguous().numpy())
+                for i, c in enumerate(C)
+            ]
+        else:
+            indices = [
+                linear_sum_assignment(c.split(sizes, -1)[i].numpy())
+                for i, c in enumerate(C)
+            ]
         return [(paddle.to_tensor(
             i, dtype=paddle.int64), paddle.to_tensor(
                 j, dtype=paddle.int64)) for i, j in indices]