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Add eval_mm dir
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trainfanlhy
178
eval_mm/vlmevalkit/vlmeval/api/gpt.py
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178
eval_mm/vlmevalkit/vlmeval/api/gpt.py
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from ..smp import *
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import os
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import sys
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from .base import BaseAPI
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APIBASES = {
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'OFFICIAL': 'https://api.openai.com/v1/chat/completions',
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}
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def GPT_context_window(model):
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length_map = {
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'gpt-4-1106-preview': 128000,
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'gpt-4-vision-preview': 128000,
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'gpt-4': 8192,
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'gpt-4-32k': 32768,
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'gpt-4-0613': 8192,
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'gpt-4-32k-0613': 32768,
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'gpt-3.5-turbo-1106': 16385,
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'gpt-3.5-turbo': 4096,
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'gpt-3.5-turbo-16k': 16385,
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'gpt-3.5-turbo-instruct': 4096,
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'gpt-3.5-turbo-0613': 4096,
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'gpt-3.5-turbo-16k-0613': 16385,
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}
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if model in length_map:
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return length_map[model]
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else:
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return 128000
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class OpenAIWrapper(BaseAPI):
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is_api: bool = True
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def __init__(self,
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model: str = 'gpt-3.5-turbo-0613',
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retry: int = 5,
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wait: int = 5,
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key: str = None,
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verbose: bool = True,
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system_prompt: str = None,
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temperature: float = 0,
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timeout: int = 60,
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api_base: str = None,
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max_tokens: int = 1024,
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img_size: int = 512,
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img_detail: str = 'low',
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**kwargs):
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self.model = model
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self.cur_idx = 0
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self.fail_msg = 'Failed to obtain answer via API. '
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self.max_tokens = max_tokens
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self.temperature = temperature
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if 'step-1v' in model:
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env_key = os.environ.get('STEPAI_API_KEY', '')
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if key is None:
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key = env_key
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else:
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env_key = os.environ.get('OPENAI_API_KEY', '')
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if key is None:
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key = env_key
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assert isinstance(key, str) and key.startswith('sk-'), (
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f'Illegal openai_key {key}. '
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'Please set the environment variable OPENAI_API_KEY to your openai key. '
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)
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self.key = key
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assert img_size > 0 or img_size == -1
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self.img_size = img_size
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assert img_detail in ['high', 'low']
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self.img_detail = img_detail
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self.timeout = timeout
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super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
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if api_base is None:
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if 'OPENAI_API_BASE' in os.environ and os.environ['OPENAI_API_BASE'] != '':
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self.logger.error('Environment variable OPENAI_API_BASE is set. Will use it as api_base. ')
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api_base = os.environ['OPENAI_API_BASE']
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else:
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api_base = 'OFFICIAL'
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assert api_base is not None
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if api_base in APIBASES:
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self.api_base = APIBASES[api_base]
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elif api_base.startswith('http'):
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self.api_base = api_base
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else:
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self.logger.error('Unknown API Base. ')
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sys.exit(-1)
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self.logger.info(f'Using API Base: {self.api_base}; API Key: {self.key}')
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# inputs can be a lvl-2 nested list: [content1, content2, content3, ...]
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# content can be a string or a list of image & text
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def prepare_inputs(self, inputs):
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input_msgs = []
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if self.system_prompt is not None:
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input_msgs.append(dict(role='system', content=self.system_prompt))
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has_images = np.sum([x['type'] == 'image' for x in inputs])
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if has_images:
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content_list = []
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for msg in inputs:
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if msg['type'] == 'text':
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content_list.append(dict(type='text', text=msg['value']))
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elif msg['type'] == 'image':
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from PIL import Image
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img = Image.open(msg['value'])
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b64 = encode_image_to_base64(img, target_size=self.img_size)
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img_struct = dict(url=f'data:image/jpeg;base64,{b64}', detail=self.img_detail)
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content_list.append(dict(type='image_url', image_url=img_struct))
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input_msgs.append(dict(role='user', content=content_list))
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else:
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assert all([x['type'] == 'text' for x in inputs])
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text = '\n'.join([x['value'] for x in inputs])
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input_msgs.append(dict(role='user', content=text))
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return input_msgs
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def generate_inner(self, inputs, **kwargs) -> str:
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input_msgs = self.prepare_inputs(inputs)
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temperature = kwargs.pop('temperature', self.temperature)
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max_tokens = kwargs.pop('max_tokens', self.max_tokens)
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context_window = GPT_context_window(self.model)
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max_tokens = min(max_tokens, context_window - self.get_token_len(inputs))
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if 0 < max_tokens <= 100:
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self.logger.warning(
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'Less than 100 tokens left, '
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'may exceed the context window with some additional meta symbols. '
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)
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if max_tokens <= 0:
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return 0, self.fail_msg + 'Input string longer than context window. ', 'Length Exceeded. '
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headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.key}'}
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payload = dict(
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model=self.model,
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messages=input_msgs,
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max_tokens=max_tokens,
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n=1,
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temperature=temperature,
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**kwargs)
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response = requests.post(self.api_base, headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1)
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ret_code = response.status_code
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ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code
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answer = self.fail_msg
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try:
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resp_struct = json.loads(response.text)
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answer = resp_struct['choices'][0]['message']['content'].strip()
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except:
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pass
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return ret_code, answer, response
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def get_token_len(self, inputs) -> int:
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import tiktoken
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try:
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enc = tiktoken.encoding_for_model(self.model)
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except:
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enc = tiktoken.encoding_for_model('gpt-4')
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assert isinstance(inputs, list)
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tot = 0
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for item in inputs:
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if item['type'] == 'text':
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tot += len(enc.encode(item['value']))
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elif item['type'] == 'image':
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tot += 85
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if self.img_detail == 'high':
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img = Image.open(item['value'])
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npatch = np.ceil(img.size[0] / 512) * np.ceil(img.size[1] / 512)
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tot += npatch * 170
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return tot
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class GPT4V(OpenAIWrapper):
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def generate(self, message, dataset=None):
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return super(GPT4V, self).generate(message)
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