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groundingdino/util/inference.py

@@ -89,11 +89,17 @@ def predict(
         tokenized["attention_mask"] = tokenized["attention_mask"][:, : max_text_len]
         tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : max_text_len]
 
+    tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+    tokenized_for_encoder["attention_mask"] = text_self_attention_masks
+    tokenized_for_encoder["position_ids"] = position_ids
+
+    bert = model.bert
+    bert_output = bert(**tokenized_for_encoder)  # bs, 195, 768
+
     with torch.no_grad():
         outputs = model(image.unsqueeze(0), 
-                        input_ids=tokenized["input_ids"], 
+                        last_hidden_state=bert_output["last_hidden_state"], 
                         attention_mask=tokenized["attention_mask"], 
-                        token_type_ids=tokenized["token_type_ids"], 
                         position_ids = position_ids, 
                         text_self_attention_masks = text_self_attention_masks)
 #        outputs = model(image[None], captions=[caption])