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import math
from vlmeval.smp import *
from vlmeval.api.base import BaseAPI
from vlmeval.dataset import img_root_map
API_BASE = "https://api.siliconflow.cn/v1/chat/completions"
def resize_image(image: Image.Image, max_height: int, max_width: int) -> Image.Image:
width, height = image.size
if min(width, height) < 50:
scale = 50 / min(width, height)
image = image.resize((int(width * scale), int(height * scale)))
current_pixels = width * height
if current_pixels <= max_height * max_width:
return image
scale = math.sqrt(max_height * max_width / current_pixels)
new_width = int(width * scale)
new_height = int(height * scale)
return image.resize((new_width, new_height), Image.Resampling.LANCZOS)
def encode_image(path: str, max_height: int = 1024, max_width: int = 1024) -> str:
image = Image.open(path).convert("RGB")
image = resize_image(image, max_height, max_width)
width, height = image.size
if min(height, width) < 50:
scale = 50 / min(width, height)
image = image.resize((int(width * scale), int(height * scale)))
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_bytes = buffered.getvalue()
img_base64 = base64.b64encode(img_bytes).decode("utf-8")
return img_base64
class SiliconFlowAPI(BaseAPI):
is_api: bool = True
def __init__(
self,
model: str = "deepseek-ai/DeepSeek-V2.5",
retry: int = 5,
wait: int = 5,
key: str = None,
api_base: str = API_BASE,
verbose: bool = True,
system_prompt: str = None,
timeout: int = 60,
reasoning: bool = False, # If set, will return results in the format of {'content': '...', 'reasoning': '...'}
**kwargs,
):
self.model = model
self.api_base = api_base
self.reasoning = reasoning
self.timeout = timeout
default_kwargs = {
"stream": False,
"temperature": 0,
"n": 1,
"max_tokens": 1280,
}
for k, v in default_kwargs.items():
if k not in kwargs:
kwargs[k] = default_kwargs[k]
if key is not None:
self.key = key
else:
self.key = os.environ.get("SiliconFlow_API_KEY", "")
headers = {"Authorization": "Bearer {}", "Content-Type": "application/json"}
headers["Authorization"] = headers["Authorization"].format(self.key)
self.headers = headers
super().__init__(
wait=wait,
retry=retry,
system_prompt=system_prompt,
verbose=verbose,
**kwargs,
)
@staticmethod
def build_msgs(msgs_raw):
messages = []
message = {"role": "user", "content": []}
image_b64 = None
for msg in msgs_raw:
if msg["type"] == "image" and not image_b64:
image_b64 = encode_image(msg["value"])
message["content"].append(
{"image_url": {"url": image_b64}, "type": "image_url"}
)
elif msg["type"] == "text":
message["content"].append({"text": msg["value"], "type": "text"})
messages.append(message)
return messages
def generate_inner(self, inputs, **kwargs) -> str:
default_kwargs = self.default_kwargs
default_kwargs.update(kwargs)
payload = dict(
model=self.model,
messages=self.build_msgs(msgs_raw=inputs),
**default_kwargs,
)
response = requests.post(
self.api_base, headers=self.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)
msg = resp_struct["choices"][0]["message"]
if self.reasoning and 'reasoning_content' in msg:
answer = {'content': msg['content'], 'reasoning': msg['reasoning_content']}
else:
answer = resp_struct["choices"][0]["message"]["content"].strip()
except:
pass
return ret_code, answer, response
class TeleMMAPI(SiliconFlowAPI):
is_api: bool = True
def __init__(
self,
model: str = "TeleAI/TeleMM",
key: str = None,
max_height: int = 1280,
max_width: int = 784,
**kwargs,
):
super().__init__(model=model, key=key, **kwargs)
self.max_height = max_height
self.max_width = max_width
def dump_image(self, line, dataset):
"""Dump the image(s) of the input line to the corresponding dataset folder.
Args:
line (line of pd.DataFrame): The raw input line.
dataset (str): The name of the dataset.
Returns:
str | list[str]: The paths of the dumped images.
"""
ROOT = LMUDataRoot()
assert isinstance(dataset, str)
# img_root = osp.join(ROOT, 'images', img_root_map[dataset] if dataset in img_root_map else dataset)
img_root = osp.join(ROOT, "images", img_root_map(dataset))
os.makedirs(img_root, exist_ok=True)
if "image" in line:
if isinstance(line["image"], list):
tgt_path = []
assert "image_path" in line
for img, im_name in zip(line["image"], line["image_path"]):
path = osp.join(img_root, im_name)
if not read_ok(path):
decode_base64_to_image_file(img, path)
tgt_path.append(path)
else:
tgt_path = osp.join(img_root, f"{line['index']}.jpg")
if not read_ok(tgt_path):
decode_base64_to_image_file(line["image"], tgt_path)
tgt_path = [tgt_path]
else:
assert "image_path" in line
tgt_path = toliststr(line["image_path"])
return tgt_path
def _prepare_content(
self, inputs: list[dict[str, str]], dataset: str = None
) -> list[dict[str, str]]:
"""
inputs list[dict[str, str]], each dict has keys: ['type', 'value']
"""
content = []
has_image = False
for s in inputs:
if s["type"] == "image":
if not has_image:
item = {
"type": "image_url",
"image_url": {
"url": encode_image(
s["value"],
max_height=self.max_height,
max_width=self.max_width,
)
},
}
has_image = True
else:
continue
elif s["type"] == "text":
prompt = s["value"]
if len(prompt) == 0:
continue
if dataset == "HallusionBench":
prompt += " Please answer yes or no directly, without any unnecessary explanation."
elif dataset == "OCRBench":
prompt = (
prompt + "\nExtract the text from the image intactly and "
+ "answer the question concisely and clearly if possible."
)
elif (
dataset == "AI2D_TEST"
or dataset == "MMStar"
or dataset == "MMBench_TEST_EN_V11"
or dataset == "MMVet"
):
prompt = prompt.replace(
"Please select the correct answer from the options above. \n",
"Please select the correct option from the above choices based on the "
+ "input image and question. The final output should only be one option, such as 'A'",
)
elif dataset == "MMBench_TEST_CN_V11":
prompt = prompt.replace(
"Please select the correct answer from the options above. \n",
"请根据输入图像和问题从上述选项中选择正确选项，最终的输出只有一个选项，例如'A'",
)
item = {"type": "text", "text": prompt}
else:
raise ValueError(f"Invalid message type: {s['type']}, {s}")
content.append(item)
return content
def generate_inner(self, inputs, **kwargs) -> str:
default_kwargs = self.default_kwargs
default_kwargs.update(kwargs)
messages = []
messages.append(
{
"role": "user",
"content": self._prepare_content(
inputs, dataset=kwargs.get("dataset", None)
),
}
)
payload = dict(model=self.model, messages=messages, **default_kwargs)
response = requests.post(
self.api_base, headers=self.headers, data=json.dumps(payload)
)
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()
return ret_code, answer, response
except Exception as err:
import traceback
traceback.print_exc()
if self.verbose:
self.logger.error(f"{type(err)}: {err}")
self.logger.error(f"The input messages are {inputs}.")
return -1, "", ""
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