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import os
import warnings
from vlmeval.smp import *
from vlmeval.api.base import BaseAPI
from vlmeval.vlm.qwen2_vl.prompt import Qwen2VLPromptMixin
def ensure_image_url(image: str) -> str:
prefixes = ['http://', 'https://', 'file://', 'data:image;']
if any(image.startswith(prefix) for prefix in prefixes):
return image
if os.path.exists(image):
return 'file://' + image
raise ValueError(f'Invalid image: {image}')
class Qwen2VLAPI(Qwen2VLPromptMixin, BaseAPI):
is_api: bool = True
def __init__(
self,
model: str = 'qwen-vl-max-0809',
key: str | None = None,
min_pixels: int | None = None,
max_pixels: int | None = None,
max_length=1024,
top_p=0.001,
top_k=1,
temperature=0.01,
repetition_penalty=1.0,
presence_penalty=0.0,
seed=3407,
use_custom_prompt: bool = True,
**kwargs,
):
import dashscope
self.model = model
self.min_pixels = min_pixels
self.max_pixels = max_pixels
self.generate_kwargs = dict(
max_length=max_length,
top_p=top_p,
top_k=top_k,
temperature=temperature,
repetition_penalty=repetition_penalty,
presence_penalty=presence_penalty,
seed=seed,
)
key = os.environ.get('DASHSCOPE_API_KEY', None) if key is None else key
assert key is not None, (
'Please set the API Key (obtain it here: '
'https://help.aliyun.com/zh/dashscope/developer-reference/vl-plus-quick-start)'
)
dashscope.api_key = key
super().__init__(use_custom_prompt=use_custom_prompt, **kwargs)
def _prepare_content(self, inputs: list[dict[str, str]], dataset: str | None = None) -> list[dict[str, str]]:
"""
inputs list[dict[str, str]], each dict has keys: ['type', 'value']
"""
content = []
for s in inputs:
if s['type'] == 'image':
item = {'type': 'image', 'image': ensure_image_url(s['value'])}
if dataset == 'OCRBench':
item['min_pixels'] = 10 * 10 * 28 * 28
warnings.warn(f"OCRBench dataset uses custom min_pixels={item['min_pixels']}")
if self.max_pixels is not None:
item['max_pixels'] = self.max_pixels
else:
if self.min_pixels is not None:
item['min_pixels'] = self.min_pixels
if self.max_pixels is not None:
item['max_pixels'] = self.max_pixels
elif s['type'] == 'text':
item = {'type': 'text', 'text': s['value']}
else:
raise ValueError(f"Invalid message type: {s['type']}, {s}")
content.append(item)
return content
def generate_inner(self, inputs, **kwargs) -> str:
import dashscope
messages = []
if self.system_prompt is not None:
messages.append({'role': 'system', 'content': self.system_prompt})
messages.append(
{'role': 'user', 'content': self._prepare_content(inputs, dataset=kwargs.get('dataset', None))}
)
if self.verbose:
print(f'\033[31m{messages}\033[0m')
# generate
generation_kwargs = self.generate_kwargs.copy()
kwargs.pop('dataset', None)
generation_kwargs.update(kwargs)
try:
response = dashscope.MultiModalConversation.call(
model=self.model,
messages=messages,
**generation_kwargs,
)
if self.verbose:
print(response)
answer = response.output.choices[0]['message']['content'][0]['text']
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, '', ''
class QwenVLWrapper(BaseAPI):
is_api: bool = True
def __init__(self,
model: str = 'qwen-vl-plus',
retry: int = 5,
wait: int = 5,
key: str = None,
verbose: bool = True,
temperature: float = 0.0,
system_prompt: str = None,
max_tokens: int = 2048,
proxy: str = None,
**kwargs):
assert model in ['qwen-vl-plus', 'qwen-vl-max']
self.model = model
import dashscope
self.fail_msg = 'Failed to obtain answer via API. '
self.max_tokens = max_tokens
self.temperature = temperature
if key is None:
key = os.environ.get('DASHSCOPE_API_KEY', None)
assert key is not None, (
'Please set the API Key (obtain it here: '
'https://help.aliyun.com/zh/dashscope/developer-reference/vl-plus-quick-start)'
)
dashscope.api_key = key
if proxy is not None:
proxy_set(proxy)
super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
# 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(text=msg['value']))
elif msg['type'] == 'image':
content_list.append(dict(image='file://' + msg['value']))
else:
assert all([x['type'] == 'text' for x in inputs])
text = '\n'.join([x['value'] for x in inputs])
content_list = [dict(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:
from dashscope import MultiModalConversation
assert isinstance(inputs, str) or isinstance(inputs, list)
if 'type' in inputs[0]:
pure_text = np.all([x['type'] == 'text' for x in inputs])
else:
pure_text = True
for inp in inputs:
if not np.all([x['type'] == 'text' for x in inp['content']]):
pure_text = False
break
assert not pure_text
messages = self.prepare_inputs(inputs)
gen_config = dict(max_output_tokens=self.max_tokens, temperature=self.temperature)
gen_config.update(kwargs)
try:
response = MultiModalConversation.call(model=self.model, messages=messages)
if self.verbose:
print(response)
answer = response.output.choices[0]['message']['content'][0]['text']
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, '', ''
class QwenVLAPI(QwenVLWrapper):
def generate(self, message, dataset=None):
return super(QwenVLAPI, self).generate(message)
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