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# from http import HTTPStatus
import os
import requests
from ..dataset import DATASET_TYPE, DATASET_MODALITY
from vlmeval.api.base import BaseAPI
from vlmeval.smp import *
class MUGUWrapper(BaseAPI):
is_api: bool = True
def __init__(self,
model: str,
retry: int = 5,
wait: int = 5,
key: str = None,
verbose: bool = True,
temperature: float = 0.0,
timeout: int = 60,
api_base: str = None,
system_prompt: str = None,
max_tokens: int = 4096,
use_mpo_prompt: bool = False,
**kwargs):
self.fail_msg = 'Failed to obtain answer via API. '
self.max_tokens = max_tokens
self.timeout = timeout
api_base = 'https://shopee.sg/api/v1/compassllvm/v1/chat/completions'
assert api_base is not None, 'Please set the environment variable LMDEPLOY_API_BASE.'
self.api_base = api_base
super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
model_url = ''.join([api_base.split('v1')[0], 'v1/models'])
resp = requests.get(model_url)
self.model = model
if hasattr(self, 'custom_prompt'):
self.logger.info(f'using custom prompt {self.custom_prompt}')
self.temperature = temperature
self.logger.info(f'Init temperature: {self.temperature}')
self.use_mpo_prompt = use_mpo_prompt
self.temperature = 0.0
def dump_image(self, line, dataset):
return self.dump_image_func(line)
def set_dump_image(self, dump_image_func):
self.dump_image_func = dump_image_func
def use_custom_prompt(self, dataset):
assert dataset is not None
assert DATASET_MODALITY(dataset) != 'VIDEO', 'not supported'
if listinstr(['MMDU', 'MME-RealWorld', 'MME-RealWorld-CN'], dataset):
# For Multi-Turn we don't have custom prompt
return False
if DATASET_MODALITY(dataset) == 'VIDEO':
# For Video benchmarks we don't have custom prompt at here
return False
else:
return True
def get_max_num(self, dataset):
assert dataset is not None
res_1_datasets = ['MMBench-Video', 'Video-MME', 'MVBench', 'Video', 'WorldSense']
res_12_datasets = ['ChartQA_TEST', 'MMMU_DEV_VAL', 'MMMU_TEST', 'MME-RealWorld',
'VCR_EN', 'VCR_ZH', 'OCRVQA']
res_18_datasets = ['DocVQA_VAL', 'DocVQA_TEST', 'DUDE', 'MMLongBench_DOC', 'SLIDEVQA']
res_24_datasets = ['InfoVQA_VAL', 'InfoVQA_TEST', 'OCRBench', 'HRBench4K', 'HRBench8K']
if listinstr(res_1_datasets, dataset):
return 1
elif listinstr(res_12_datasets, dataset):
return 12
elif listinstr(res_18_datasets, dataset):
return 18
elif listinstr(res_24_datasets, dataset):
return 24
else:
return 6
def build_prompt(self, line, dataset=None):
assert self.use_custom_prompt(dataset)
assert dataset is None or isinstance(dataset, str)
from ..vlm.internvl.utils import (build_multi_choice_prompt,
build_mcq_cot_prompt,
build_qa_cot_prompt,
build_mpo_prompt,
reorganize_prompt)
tgt_path = self.dump_image(line, dataset)
max_num = self.get_max_num(dataset)
if dataset is not None and DATASET_TYPE(dataset) == 'Y/N':
question = line['question']
if listinstr(['MME'], dataset):
prompt = question + ' Answer the question using a single word or phrase.'
elif listinstr(['HallusionBench', 'AMBER'], dataset):
prompt = question + ' Please answer yes or no. Answer the question using a single word or phrase.'
else:
prompt = question
elif dataset is not None and DATASET_TYPE(dataset) == 'MCQ':
prompt = build_multi_choice_prompt(line, dataset)
if os.getenv('USE_COT') == '1':
prompt = build_mcq_cot_prompt(line, prompt)
elif dataset is not None and DATASET_TYPE(dataset) == 'VQA':
question = line['question']
if listinstr(['LLaVABench', 'WildVision'], dataset):
prompt = question + '\nAnswer this question in detail.'
elif listinstr(['OCRVQA', 'TextVQA', 'ChartQA', 'DocVQA', 'InfoVQA', 'OCRBench',
'DUDE', 'SLIDEVQA', 'GQA', 'MMLongBench_DOC'], dataset):
prompt = question + '\nAnswer the question using a single word or phrase.'
elif listinstr(['MathVista', 'MathVision', 'VCR', 'MTVQA', 'MMVet', 'MathVerse',
'MMDU', 'CRPE', 'MIA-Bench', 'MM-Math', 'DynaMath', 'QSpatial', 'WeMath', 'LogicVista'], dataset):
prompt = question
if os.getenv('USE_COT') == '1':
prompt = build_qa_cot_prompt(line, prompt)
else:
prompt = question + '\nAnswer the question using a single word or phrase.'
else:
# VQA_ex_prompt: OlympiadBench, VizWiz
prompt = line['question']
if os.getenv('USE_COT') == '1':
prompt = build_qa_cot_prompt(line, prompt)
message = [dict(type='text', value=prompt)]
image_num = len(tgt_path)
max_num = max(1, min(max_num, 64 // image_num))
# TODO：support upscale_flag
message.extend([dict(type='image', value=s, max_dynamic_patch=max_num) for s in tgt_path])
if self.use_mpo_prompt:
message = build_mpo_prompt(message, line, dataset)
# reorganize_prompt
prompt = reorganize_prompt(message, image_num, dataset=dataset)
prompt.replace('<image>', '<IMAGE_TOKEN>')
message[0] = dict(type='text', value=prompt)
return message
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)
extra_args = msg.copy()
extra_args.pop('type')
extra_args.pop('value')
img_struct = dict(url=f'data:image/jpeg;base64,{b64}', **extra_args)
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)
self.logger.info(f'Generate temperature: {temperature}')
max_tokens = kwargs.pop('max_tokens', self.max_tokens)
headers = {'Content-Type': 'application/json'}
payload = dict(
model=self.model,
messages=input_msgs,
max_tokens=max_tokens,
n=1,
top_k=1,
temperature=temperature,
stream=False,
**kwargs)
response = requests.post(
self.api_base,
headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1)
ret_code = response.status_code
ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code
answer = self.fail_msg
try:
resp_struct = json.loads(response.text)
answer = resp_struct['choices'][0]['message']['content'].strip()
# for internvl2-8b-mpo-cot
if getattr(self, 'use_mpo_prompt', False):
from ..vlm.internvl.utils import mpo_post_processing
answer = mpo_post_processing(answer, kwargs.get('dataset'))
except:
pass
return ret_code, answer, response
class MUGUAPI(MUGUWrapper):
def generate(self, message, dataset=None):
return super(MUGUAPI, self).generate(message, dataset=dataset) |