diff --git a/ppdet/modeling/losses/detr_loss.py b/ppdet/modeling/losses/detr_loss.py
index 8e9b1294f68e6104b5176d562c502c9566f2c003..f4291a7a74b5cb979d746bbdf8178f8116c3403f 100644
--- a/ppdet/modeling/losses/detr_loss.py
+++ b/ppdet/modeling/losses/detr_loss.py
@@ -163,9 +163,9 @@ class DETRLoss(nn.Layer):
                       gt_class,
                       bg_index,
                       num_gts,
-                      match_indices=None,
+                      dn_match_indices=None,
                       postfix=""):
-        if boxes is None and logits is None:
+        if boxes is None or logits is None:
             return {
                 "loss_class_aux" + postfix: paddle.paddle.zeros([1]),
                 "loss_bbox_aux" + postfix: paddle.paddle.zeros([1]),
@@ -175,9 +175,11 @@ class DETRLoss(nn.Layer):
         loss_bbox = []
         loss_giou = []
         for aux_boxes, aux_logits in zip(boxes, logits):
-            if match_indices is None:
+            if dn_match_indices is None:
                 match_indices = self.matcher(aux_boxes, aux_logits, gt_bbox,
                                              gt_class)
+            else:
+                match_indices = dn_match_indices
             loss_class.append(
                 self._get_loss_class(aux_logits, gt_class, match_indices,
                                      bg_index, num_gts, postfix)['loss_class' +
@@ -237,11 +239,13 @@ class DETRLoss(nn.Layer):
             gt_mask (List(Tensor), optional): list[[n, H, W]]
             postfix (str): postfix of loss name
         """
-        if "match_indices" in kwargs:
-            match_indices = kwargs["match_indices"]
-        else:
+        dn_match_indices = kwargs.get("dn_match_indices", None)
+        if dn_match_indices is None and (boxes is not None and
+                                         logits is not None):
             match_indices = self.matcher(boxes[-1].detach(),
                                          logits[-1].detach(), gt_bbox, gt_class)
+        else:
+            match_indices = dn_match_indices
 
         num_gts = sum(len(a) for a in gt_bbox)
         num_gts = paddle.to_tensor([num_gts], dtype="float32")
@@ -264,13 +268,11 @@ class DETRLoss(nn.Layer):
                                     gt_mask, match_indices, num_gts, postfix))
 
         if self.aux_loss:
-            if "match_indices" not in kwargs:
-                match_indices = None
             total_loss.update(
                 self._get_loss_aux(
                     boxes[:-1] if boxes is not None else None, logits[:-1]
                     if logits is not None else None, gt_bbox, gt_class,
-                    self.num_classes, num_gts, match_indices, postfix))
+                    self.num_classes, num_gts, dn_match_indices, postfix))
 
         return total_loss
 
@@ -292,7 +294,6 @@ class DINOLoss(DETRLoss):
         total_loss = super(DINOLoss, self).forward(boxes, logits, gt_bbox,
                                                    gt_class)
 
-        # denoising training loss
         if dn_meta is not None:
             dn_positive_idx, dn_num_group = \
                 dn_meta["dn_positive_idx"], dn_meta["dn_num_group"]
@@ -315,13 +316,14 @@ class DINOLoss(DETRLoss):
         else:
             dn_match_indices, dn_num_group = None, 1.
 
+        # compute denoising training loss
         dn_loss = super(DINOLoss, self).forward(
             dn_out_bboxes,
             dn_out_logits,
             gt_bbox,
             gt_class,
             postfix="_dn",
-            match_indices=dn_match_indices,
+            dn_match_indices=dn_match_indices,
             dn_num_group=dn_num_group)
         total_loss.update(dn_loss)