diff --git a/groundingdino/models/GroundingDINO/groundingdino.py b/groundingdino/models/GroundingDINO/groundingdino.py
index 078b539..6f3a18a 100644
--- a/groundingdino/models/GroundingDINO/groundingdino.py
+++ b/groundingdino/models/GroundingDINO/groundingdino.py
@@ -230,6 +230,8 @@ class GroundingDINO(nn.Module):
                 input_ids: Tensor, 
                 attention_mask: Tensor, 
                 token_type_ids: Tensor, 
+                position_ids: Tensor, 
+                text_self_attention_masks: Tensor, 
                 **kw):
         """The forward expects a NestedTensor, which consists of:
            - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
@@ -262,22 +264,22 @@ class GroundingDINO(nn.Module):
             "token_type_ids": token_type_ids,
         }
 
-        (
-            text_self_attention_masks,
-            position_ids,
-            cate_to_token_mask_list,
-        ) = generate_masks_with_special_tokens_and_transfer_map(
-            tokenized, self.specical_tokens, self.tokenizer
-        )
+#        (
+#            text_self_attention_masks,
+#            position_ids,
+#            cate_to_token_mask_list,
+#        ) = generate_masks_with_special_tokens_and_transfer_map(
+#            tokenized, self.specical_tokens, self.tokenizer
+#        )
 
-        if text_self_attention_masks.shape[1] > self.max_text_len:
-            text_self_attention_masks = text_self_attention_masks[
-                :, : self.max_text_len, : self.max_text_len
-            ]
-            position_ids = position_ids[:, : self.max_text_len]
-            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
-            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
-            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+#        if text_self_attention_masks.shape[1] > self.max_text_len:
+#            text_self_attention_masks = text_self_attention_masks[
+#                :, : self.max_text_len, : self.max_text_len
+#            ]
+#            position_ids = position_ids[:, : self.max_text_len]
+#            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
+#            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
+#            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
 
         # extract text embeddings
         if self.sub_sentence_present:
@@ -292,7 +294,7 @@ class GroundingDINO(nn.Module):
 
         encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
         text_token_mask = tokenized["attention_mask"].bool()  # bs, 195
-#        text_token_mask = tokenizedattention_mask.bool()  # bs, 195
+#        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
         # text_token_mask: True for nomask, False for mask
         # text_self_attention_masks: True for nomask, False for mask