File size: 2,837 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 |
import requests
requests.packages.urllib3.disable_warnings()
from vlmeval.smp import *
from vlmeval.api.base import BaseAPI
from vlmeval.dataset import DATASET_TYPE
from vlmeval.smp.vlm import encode_image_file_to_base64
class GLMVisionWrapper(BaseAPI):
is_api: bool = True
def __init__(self,
model: str,
retry: int = 5,
wait: int = 5,
key: str = None,
verbose: bool = True,
system_prompt: str = None,
max_tokens: int = 4096,
proxy: str = None,
**kwargs):
from zhipuai import ZhipuAI
self.model = model
self.fail_msg = 'Failed to obtain answer via API. '
if key is None:
key = os.environ.get('GLMV_API_KEY', None)
assert key is not None, (
'Please set the API Key (obtain it here: '
'https://bigmodel.cn)'
)
self.client = ZhipuAI(api_key=key)
super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
def build_msgs(self, msgs_raw, system_prompt=None, dataset=None):
msgs = cp.deepcopy(msgs_raw)
content = []
for i, msg in enumerate(msgs):
if msg['type'] == 'text':
content.append(dict(type='text', text=msg['value']))
elif msg['type'] == 'image':
content.append(dict(type='image_url', image_url=dict(url=encode_image_file_to_base64(msg['value']))))
if dataset in {'HallusionBench', 'POPE'}:
content.append(dict(type="text", text="Please answer yes or no."))
ret = [dict(role='user', content=content)]
return ret
def generate_inner(self, inputs, **kwargs) -> str:
assert isinstance(inputs, str) or isinstance(inputs, list)
inputs = [inputs] if isinstance(inputs, str) else inputs
messages = self.build_msgs(msgs_raw=inputs, dataset=kwargs.get('dataset', None))
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
do_sample=False,
max_tokens=2048
)
try:
answer = response.choices[0].message.content.strip()
if self.verbose:
self.logger.info(f'inputs: {inputs}\nanswer: {answer}')
return 0, answer, 'Succeeded!'
except Exception as err:
if self.verbose:
self.logger.error(f'{type(err)}: {err}')
self.logger.error(f'The input messages are {inputs}.')
return -1, self.fail_msg, ''
class GLMVisionAPI(GLMVisionWrapper):
def generate(self, message, dataset=None):
return super(GLMVisionAPI, self).generate(message, dataset=dataset)
|