| import re |
| 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, |
| 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__(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)) |
|
|
| try: |
| response = self.client.chat.completions.create( |
| model=self.model, |
| messages=messages, |
| do_sample=False, |
| max_tokens=2048 |
| ) |
| 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) |
|
|
