diff --git a/ppdet/modeling/post_process.py b/ppdet/modeling/post_process.py
index af222444ee6caa8f2dfe89207dee525b85fd6020..65e8d0b8bcffee8180633b5c417c4b74b5197a18 100644
--- a/ppdet/modeling/post_process.py
+++ b/ppdet/modeling/post_process.py
@@ -455,8 +455,11 @@ class DETRPostProcess(object):
                  use_focal_loss=False,
                  with_mask=False,
                  mask_threshold=0.5,
-                 use_avg_mask_score=False):
+                 use_avg_mask_score=False,
+                 bbox_decode_type='origin'):
         super(DETRPostProcess, self).__init__()
+        assert bbox_decode_type in ['origin', 'pad']
+
         self.num_classes = num_classes
         self.num_top_queries = num_top_queries
         self.dual_queries = dual_queries
@@ -465,6 +468,7 @@ class DETRPostProcess(object):
         self.with_mask = with_mask
         self.mask_threshold = mask_threshold
         self.use_avg_mask_score = use_avg_mask_score
+        self.bbox_decode_type = bbox_decode_type
 
     def _mask_postprocess(self, mask_pred, score_pred, index):
         mask_score = F.sigmoid(paddle.gather_nd(mask_pred, index))
@@ -478,7 +482,7 @@ class DETRPostProcess(object):
 
     def __call__(self, head_out, im_shape, scale_factor, pad_shape):
         """
-        Decode the bbox.
+        Decode the bbox and mask.
 
         Args:
             head_out (tuple): bbox_pred, cls_logit and masks of bbox_head output.
@@ -502,9 +506,15 @@ class DETRPostProcess(object):
         # calculate the original shape of the image
         origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
         img_h, img_w = paddle.split(origin_shape, 2, axis=-1)
-        # calculate the shape of the image with padding
-        out_shape = pad_shape / im_shape * origin_shape
-        out_shape = out_shape.flip(1).tile([1, 2]).unsqueeze(1)
+        if self.bbox_decode_type == 'pad':
+            # calculate the shape of the image with padding
+            out_shape = pad_shape / im_shape * origin_shape
+            out_shape = out_shape.flip(1).tile([1, 2]).unsqueeze(1)
+        elif self.bbox_decode_type == 'origin':
+            out_shape = origin_shape.flip(1).tile([1, 2]).unsqueeze(1)
+        else:
+            raise Exception(
+                f'Wrong `bbox_decode_type`: {self.bbox_decode_type}.')
         bbox_pred *= out_shape
 
         scores = F.sigmoid(logits) if self.use_focal_loss else F.softmax(