File size: 7,726 Bytes
b5beb60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | 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)
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