diff --git a/ppdet/modeling/assigners/atss_assigner.py b/ppdet/modeling/assigners/atss_assigner.py
index 550962d8493a6e29de1b0601b994e64d4cd99cc2..2a641d4cc41928671d13dd14cddb73295d0f8386 100644
--- a/ppdet/modeling/assigners/atss_assigner.py
+++ b/ppdet/modeling/assigners/atss_assigner.py
@@ -51,7 +51,6 @@ class ATSSAssigner(nn.Layer):
 
     def _gather_topk_pyramid(self, gt2anchor_distances, num_anchors_list,
                              pad_gt_mask):
-        pad_gt_mask = pad_gt_mask.tile([1, 1, self.topk]).astype(paddle.bool)
         gt2anchor_distances_list = paddle.split(
             gt2anchor_distances, num_anchors_list, axis=-1)
         num_anchors_index = np.cumsum(num_anchors_list).tolist()
@@ -61,15 +60,12 @@ class ATSSAssigner(nn.Layer):
         for distances, anchors_index in zip(gt2anchor_distances_list,
                                             num_anchors_index):
             num_anchors = distances.shape[-1]
-            topk_metrics, topk_idxs = paddle.topk(
+            _, topk_idxs = paddle.topk(
                 distances, self.topk, axis=-1, largest=False)
             topk_idxs_list.append(topk_idxs + anchors_index)
-            topk_idxs = paddle.where(pad_gt_mask, topk_idxs,
-                                     paddle.zeros_like(topk_idxs))
-            is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(axis=-2)
-            is_in_topk = paddle.where(is_in_topk > 1,
-                                      paddle.zeros_like(is_in_topk), is_in_topk)
-            is_in_topk_list.append(is_in_topk.astype(gt2anchor_distances.dtype))
+            is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(
+                axis=-2).astype(gt2anchor_distances.dtype)
+            is_in_topk_list.append(is_in_topk * pad_gt_mask)
         is_in_topk_list = paddle.concat(is_in_topk_list, axis=-1)
         topk_idxs_list = paddle.concat(topk_idxs_list, axis=-1)
         return is_in_topk_list, topk_idxs_list
@@ -155,9 +151,8 @@ class ATSSAssigner(nn.Layer):
         iou_threshold = iou_threshold.reshape([batch_size, num_max_boxes, -1])
         iou_threshold = iou_threshold.mean(axis=-1, keepdim=True) + \
                         iou_threshold.std(axis=-1, keepdim=True)
-        is_in_topk = paddle.where(
-            iou_candidates > iou_threshold.tile([1, 1, num_anchors]),
-            is_in_topk, paddle.zeros_like(is_in_topk))
+        is_in_topk = paddle.where(iou_candidates > iou_threshold, is_in_topk,
+                                  paddle.zeros_like(is_in_topk))
 
         # 6. check the positive sample's center in gt, [B, n, L]
         is_in_gts = check_points_inside_bboxes(anchor_centers, gt_bboxes)
diff --git a/ppdet/modeling/assigners/task_aligned_assigner.py b/ppdet/modeling/assigners/task_aligned_assigner.py
index b97923f6c7b3bbf87a83e0eab91ce3358c316069..1a82c15237a07d3993460629ccb8317466da87fb 100644
--- a/ppdet/modeling/assigners/task_aligned_assigner.py
+++ b/ppdet/modeling/assigners/task_aligned_assigner.py
@@ -112,9 +112,7 @@ class TaskAlignedAssigner(nn.Layer):
         # select topk largest alignment metrics pred bbox as candidates
         # for each gt, [B, n, L]
         is_in_topk = gather_topk_anchors(
-            alignment_metrics * is_in_gts,
-            self.topk,
-            topk_mask=pad_gt_mask.tile([1, 1, self.topk]).astype(paddle.bool))
+            alignment_metrics * is_in_gts, self.topk, topk_mask=pad_gt_mask)
 
         # select positive sample, [B, n, L]
         mask_positive = is_in_topk * is_in_gts * pad_gt_mask
diff --git a/ppdet/modeling/assigners/utils.py b/ppdet/modeling/assigners/utils.py
index f2d9be99509b62d585a9f4db999818b6d25a2d94..6a89593a316da8cc9221fee872f34a8452200751 100644
--- a/ppdet/modeling/assigners/utils.py
+++ b/ppdet/modeling/assigners/utils.py
@@ -88,7 +88,7 @@ def gather_topk_anchors(metrics, topk, largest=True, topk_mask=None, eps=1e-9):
         largest (bool) : largest is a flag, if set to true,
             algorithm will sort by descending order, otherwise sort by
             ascending order. Default: True
-        topk_mask (Tensor, bool|None): shape[B, n, topk], ignore bbox mask,
+        topk_mask (Tensor, float32): shape[B, n, 1], ignore bbox mask,
             Default: None
         eps (float): Default: 1e-9
     Returns:
@@ -98,13 +98,11 @@ def gather_topk_anchors(metrics, topk, largest=True, topk_mask=None, eps=1e-9):
     topk_metrics, topk_idxs = paddle.topk(
         metrics, topk, axis=-1, largest=largest)
     if topk_mask is None:
-        topk_mask = (topk_metrics.max(axis=-1, keepdim=True) > eps).tile(
-            [1, 1, topk])
-    topk_idxs = paddle.where(topk_mask, topk_idxs, paddle.zeros_like(topk_idxs))
-    is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(axis=-2)
-    is_in_topk = paddle.where(is_in_topk > 1,
-                              paddle.zeros_like(is_in_topk), is_in_topk)
-    return is_in_topk.astype(metrics.dtype)
+        topk_mask = (
+            topk_metrics.max(axis=-1, keepdim=True) > eps).astype(metrics.dtype)
+    is_in_topk = F.one_hot(topk_idxs, num_anchors).sum(
+        axis=-2).astype(metrics.dtype)
+    return is_in_topk * topk_mask
 
 
 def check_points_inside_bboxes(points,