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, "", ""