from vlmeval.smp import * import os import sys from vlmeval.api.base import BaseAPI import math from vlmeval.dataset import DATASET_TYPE from vlmeval.dataset import img_root_map from io import BytesIO import pandas as pd import requests import json import base64 import time from openai import OpenAI class DoubaoVLWrapper(BaseAPI): is_api: bool = True def __init__(self, model: str = '', retry: int = 5, wait: int = 5, verbose: bool = True, system_prompt: str = None, temperature: float = 0, timeout: int = 60, max_tokens: int = 4096, api_base: str = 'https://ark.cn-beijing.volces.com/api/v3',#使用系统推荐的服务区域地址 **kwargs): self.model = model# This variable is unused self.cur_idx = 0 self.fail_msg = 'Failed to obtain answer via API. ' self.temperature = temperature self.max_tokens = max_tokens warnings.warn('You may need to set the env variable DOUBAO_VL_KEY& DOUBAO_VL_ENDPOINT to use DOUBAO_VL.') key = os.environ.get('DOUBAO_VL_KEY', None) assert key is not None, 'Please set the environment variable DOUBAO_VL_KEY. ' self.key = key endpoint = os.getenv('DOUBAO_VL_ENDPOINT', None) assert endpoint is not None, 'Please set the environment variable DOUBAO_VL_ENDPOINT. ' self.endpoint = endpoint assert api_base is not None, 'Please set the variable API_BASE. ' self.api_base = api_base self.timeout = timeout super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs) self.client = OpenAI( api_key = self.key, base_url = self.api_base, ) self.logger.info(f'Using API Base: {self.api_base}; End Point: {self.endpoint}; API Key: {self.key}') def dump_image(self, line, dataset): """Dump the image(s) of the input line to the corresponding dataset folder. Args: line (line of pd.DataFrame): The raw input line. dataset (str): The name of the dataset. Returns: str | list[str]: The paths of the dumped images. """ ROOT = LMUDataRoot() assert isinstance(dataset, str) img_root = os.path.join(ROOT, 'images', img_root_map(dataset) if dataset in img_root_map(dataset) else dataset) os.makedirs(img_root, exist_ok=True) if 'image' in line: if isinstance(line['image'], list): tgt_path = [] assert 'image_path' in line for img, im_name in zip(line['image'], line['image_path']): path = osp.join(img_root, im_name) if not read_ok(path): decode_base64_to_image_file(img, path) tgt_path.append(path) else: tgt_path = osp.join(img_root, f"{line['index']}.jpg") if not read_ok(tgt_path): decode_base64_to_image_file(line['image'], tgt_path) tgt_path = [tgt_path] else: assert 'image_path' in line tgt_path = toliststr(line['image_path']) return tgt_path def use_custom_prompt(self, dataset_name): if dataset_name == 'MathVerse_MINI_Vision_Only': return True else: return False def build_prompt(self, line, dataset: str) -> list[dict[str, str]]: if dataset in {'MathVerse_MINI_Vision_Only'}: return self. _build_mathVerse_mini_vision_only_prompt(line, dataset) raise ValueError(f'Unsupported dataset: {dataset}') def _build_mathVerse_mini_vision_only_prompt(self, line, dataset=None): assert self.use_custom_prompt(dataset) assert dataset is None or isinstance(dataset, str) tgt_path = self.dump_image(line, dataset) question = line['question'] ###remove 'directly' from the prompt, so the model will answer the question in Chain-of-Thought (CoT) manner prompt = question.replace('directly','',1) msgs = [] if isinstance(tgt_path, list): msgs.extend([dict(type='image', value=p) for p in tgt_path]) else: msgs = [dict(type='image', value=tgt_path)] msgs.append(dict(type='text', value=prompt)) return msgs # inputs can be a lvl-2 nested list: [content1, content2, content3, ...] # content can be a string or a list of image & text def prepare_itlist(self, inputs): assert np.all([isinstance(x, dict) for x in inputs]) has_images = np.sum([x['type'] == 'image' for x in inputs]) if has_images: content_list = [] for msg in inputs: if msg['type'] == 'text': content_list.append(dict(type='text', text=msg['value'])) elif msg['type'] == 'image': from PIL import Image img = Image.open(msg['value']) b64 = encode_image_to_base64(img) img_struct = dict(url=f'data:image/jpeg;base64,{b64}') content_list.append(dict(type='image_url', image_url=img_struct)) else: assert all([x['type'] == 'text' for x in inputs]) text = '\n'.join([x['value'] for x in inputs]) content_list = [dict(type='text', text=text)] return content_list def prepare_inputs(self, inputs): input_msgs = [] if self.system_prompt is not None: input_msgs.append(dict(role='system', content=self.system_prompt)) assert isinstance(inputs, list) and isinstance(inputs[0], dict) assert np.all(['type' in x for x in inputs]) or np.all(['role' in x for x in inputs]), inputs if 'role' in inputs[0]: assert inputs[-1]['role'] == 'user', inputs[-1] for item in inputs: input_msgs.append(dict(role=item['role'], content=self.prepare_itlist(item['content']))) else: input_msgs.append(dict(role='user', content=self.prepare_itlist(inputs))) return input_msgs def generate_inner(self, inputs, **kwargs) -> str: input_msgs = self.prepare_inputs(inputs) temperature = kwargs.pop('temperature', self.temperature) max_tokens = kwargs.pop('max_tokens', self.max_tokens) ret_code = -1 answer = self.fail_msg response = None try: response = self.client.chat.completions.create( model=self.endpoint, messages=input_msgs, max_tokens=max_tokens, temperature=temperature ) answer = response.choices[0].message.content.strip() ret_code = 0 except Exception as err: if self.verbose: self.logger.error(f'{type(err)}: {err}') self.logger.error(response.text if hasattr(response, 'text') else response) return ret_code, answer, response class DoubaoVL(DoubaoVLWrapper): def generate(self, message, dataset=None): return super(DoubaoVL, self).generate(message) if __name__ == '__main__': #export DOUBAO_VL_KEY='' #export DOUBAO_VL_ENDPOINT='' model = DoubaoVLWrapper( verbose=True) inputs = [ {'type': 'image', 'value': './assets/apple.jpg'}, {'type': 'text', 'value': '请详细描述一下这张图片。'}, ] code, answer, resp = model.generate_inner(inputs) print(code, answer, resp)