Update conversation(fixed the strikethrough issue).py

minigpt4/conversation/conversation.py

@@ -85,123 +85,4 @@ def dict(self):
             # "system_img": self.system_img,
             "roles": self.roles,
             "messages": self.messages,
-            "offset": self.offset,
-            "sep": self.sep,
-            "sep2": self.sep2,
-            "conv_id": self.conv_id,
-        }
-
-
-class StoppingCriteriaSub(StoppingCriteria):
-
-    def __init__(self, stops=[], encounters=1):
-        super().__init__()
-        self.stops = stops
-
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
-        for stop in self.stops:
-            if torch.all((stop == input_ids[0][-len(stop):])).item():
-                return True
-
-        return False
-
-
-CONV_VISION = Conversation(
-    system="Give the following image: <Img>ImageContent</Img>. "
-           "You will be able to see the image once I provide it to you. Please answer my questions.",
-    roles=("Human", "Assistant"),
-    messages=[],
-    offset=2,
-    sep_style=SeparatorStyle.SINGLE,
-    sep="###",
-)
-
-
-
-class Chat:
-    def __init__(self, model, vis_processor, device='cuda:0'):
-        self.device = device
-        self.model = model
-        self.vis_processor = vis_processor
-        stop_words_ids = [torch.tensor([835]).to(self.device),
-                          torch.tensor([2277, 29937]).to(self.device)]  # '###' can be encoded in two different ways.
-        self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
-
-    def ask(self, text, conv):
-        if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \
-                and conv.messages[-1][1][-6:] == '</Img>':  # last message is image.
-            conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])
-        else:
-            conv.append_message(conv.roles[0], text)
-
-    def answer(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
-               repetition_penalty=1.0, length_penalty=1, temperature=1.0, max_length=2000):
-        conv.append_message(conv.roles[1], None)
-        embs = self.get_context_emb(conv, img_list)
-
-        current_max_len = embs.shape[1] + max_new_tokens
-        if current_max_len - max_length > 0:
-            print('Warning: The number of tokens in current conversation exceeds the max length. '
-                  'The model will not see the contexts outside the range.')
-        begin_idx = max(0, current_max_len - max_length)
-
-        embs = embs[:, begin_idx:]
-
-        outputs = self.model.llama_model.generate(
-            inputs_embeds=embs,
-            max_new_tokens=max_new_tokens,
-            stopping_criteria=self.stopping_criteria,
-            num_beams=num_beams,
-            do_sample=True,
-            min_length=min_length,
-            top_p=top_p,
-            repetition_penalty=repetition_penalty,
-            length_penalty=length_penalty,
-            temperature=temperature,
-        )
-        output_token = outputs[0]
-        if output_token[0] == 0:  # the model might output a unknow token <unk> at the beginning. remove it
-            output_token = output_token[1:]
-        if output_token[0] == 1:  # some users find that there is a start token <s> at the beginning. remove it
-            output_token = output_token[1:]
-        output_text = self.model.llama_tokenizer.decode(output_token, add_special_tokens=False)
-        output_text = output_text.split('###')[0]  # remove the stop sign '###'
-        output_text = output_text.split('Assistant:')[-1].strip()
-        conv.messages[-1][1] = output_text
-        return output_text, output_token.cpu().numpy()
-
-    def upload_img(self, image, conv, img_list):
-        if isinstance(image, str):  # is a image path
-            raw_image = Image.open(image).convert('RGB')
-            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
-        elif isinstance(image, Image.Image):
-            raw_image = image
-            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
-        elif isinstance(image, torch.Tensor):
-            if len(image.shape) == 3:
-                image = image.unsqueeze(0)
-            image = image.to(self.device)
-
-        image_emb, _ = self.model.encode_img(image)
-        img_list.append(image_emb)
-        conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
-        msg = "Received."
-        # self.conv.append_message(self.conv.roles[1], msg)
-        return msg
-
-    def get_context_emb(self, conv, img_list):
-        prompt = conv.get_prompt()
-        prompt_segs = prompt.split('<ImageHere>')
-        assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."
-        seg_tokens = [
-            self.model.llama_tokenizer(
-                seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids
-            # only add bos to the first seg
-            for i, seg in enumerate(prompt_segs)
-        ]
-        seg_embs = [self.model.llama_model.model.embed_tokens(seg_t) for seg_t in seg_tokens]
-        mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]
-        mixed_embs = torch.cat(mixed_embs, dim=1)
-        return mixed_embs
-
-
+            "offset": self.offset