conversation.py

import argparse
import time
from PIL import Image
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from transformers import StoppingCriteria, StoppingCriteriaList
from tqdm import tqdm
import dataclasses
from enum import auto, Enum
from typing import List, Tuple, Any
import json
from tqdm import tqdm
from minigpt4.common.registry import registry
import random

class SeparatorStyle(Enum):
    """Different separator style."""
    SINGLE = auto()
    TWO = auto()


@dataclasses.dataclass
class Conversation:
    """A class that keeps all conversation history."""
    system: str
    roles: List[str]
    messages: List[List[str]]
    offset: int
    # system_img: List[Image.Image] = []
    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
    sep: str = "###"
    sep2: str = None

    skip_next: bool = False
    conv_id: Any = None

    def get_prompt(self):
        if self.sep_style == SeparatorStyle.SINGLE:
            ret = self.system + self.sep
            for role, message in self.messages:
                if message:
                    ret += role + ": " + message + self.sep
                else:
                    ret += role + ":"
            return ret
        elif self.sep_style == SeparatorStyle.TWO:
            seps = [self.sep, self.sep2]
            ret = self.system + seps[0]
            for i, (role, message) in enumerate(self.messages):
                if message:
                    ret += role + ": " + message + seps[i % 2]
                else:
                    ret += role + ":"
            return ret
        else:
            raise ValueError(f"Invalid style: {self.sep_style}")

    def append_message(self, role, message):
        self.messages.append([role, message])

    def to_gradio_chatbot(self):
        ret = []
        for i, (role, msg) in enumerate(self.messages[self.offset:]):
            if i % 2 == 0:
                ret.append([msg, None])
            else:
                ret[-1][-1] = msg
        return ret

    def copy(self):
        return Conversation(
            system=self.system,
            # system_img=self.system_img,
            roles=self.roles,
            messages=[[x, y] for x, y in self.messages],
            offset=self.offset,
            sep_style=self.sep_style,
            sep=self.sep,
            sep2=self.sep2,
            conv_id=self.conv_id)

    def dict(self):
        return {
            "system": self.system,
            # "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)
        # print('con1:', conv)
        # print('img_list:', img_list)
        # print('img_list1:', img_list,size())
        embs = self.get_context_emb(conv, img_list)

        # print('!!!!!!!!!!!!!!!!!!!!!!!!????????????????????')
        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
        print('output_text:', output_text)
        return output_text, output_token.cpu().numpy()

    def upload_img(self, image, conv, img_list):
        print('image:', image)

        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>')
        # print('!!!:', prompt_segs)
        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)
        ]
        # print('!!!:', seg_tokens)
        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


class Chat1:
    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 answer1(self, 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):
        prompt1 = '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.###Human: <Img><ImageHere></Img> '
        prompt2 = '###Assistant:'

        #image folder
        image_folder = '/path/to/image_folder'
                    
        #instruction file
        input_file = '/path/to/evaluation_data.json'
        with open(input_file, 'r') as f:
            datas = json.load(f)
        result = []
        correct = 0
        num = 0
        for imgs in tqdm(datas):
            instruction = imgs['instruction']
            prompt = prompt1 + instruction + prompt2
            image_path = imgs['image_id']
            img_list = []
            image_path = image_folder + image_path + '.jpg'
            raw_image = Image.open(image_path).convert('RGB')
            image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
            image_emb, _ = self.model.encode_img(image)
            img_list.append(image_emb)
            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)
            ]
            # print('!!!:', seg_tokens)
            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)
            print('====')

            embs = mixed_embs
            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()
            #print('output_text:',  output_text)
            result.append(
                {'image': imgs['image_id'], 'instruction': imgs['instruction'], 'answer': output_text, 'gt': imgs['answer'], 'source': imgs['source']})

            #output file
            with open('/path/to/output.json', 'w') as f:
                json.dump(result, f)