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modeling_sa2va_chat.py

@@ -689,6 +689,7 @@ class Sa2VAChatModel(PreTrainedModel):
                 input=text, round=1, bot_name=self.bot_name)
         input_text = past_text + input_text
         ids = self.tokenizer.encode(input_text)
+        ret_past_text = self.tokenizer.decode(ids)
         ids = torch.tensor(ids).cuda().unsqueeze(0)
 
         attention_mask = torch.ones_like(ids, dtype=torch.bool)
@@ -715,7 +716,8 @@ class Sa2VAChatModel(PreTrainedModel):
         )
         predict = self.tokenizer.decode(
             generate_output.sequences[0], skip_special_tokens=False).strip()
-
+        ret_past_text = ret_past_text + self.tokenizer.decode(
+            generate_output.sequences[0], skip_special_tokens=False)
         # if have seg result, find the seg hidden states
         hidden_states = generate_output.hidden_states
         last_hidden_states = [item[-1][0] for item in hidden_states]
@@ -737,8 +739,7 @@ class Sa2VAChatModel(PreTrainedModel):
             masks = masks.sigmoid() > 0.5
             masks = masks.cpu().numpy()
             ret_masks.append(masks)
-
-        return {'prediction': predict, 'prediction_masks': ret_masks,}
+        return {'prediction': predict, 'prediction_masks': ret_masks, "past_text": ret_past_text}
 
 def get_seg_hidden_states(hidden_states, output_ids, seg_id):
     seg_mask = output_ids == seg_id