Add eval_mm dir

eval_mm/vlmevalkit/vlmeval/api/gpt_int.py

@@ -0,0 +1,90 @@
+import json
+import warnings
+import requests
+from ..smp import *
+from .gpt import GPT_context_window, OpenAIWrapper
+
+url = 'http://ecs.sv.us.alles-apin.openxlab.org.cn/v1/openai/v2/text/chat'
+headers = {
+    'Content-Type': 'application/json'
+}
+
+
+class OpenAIWrapperInternal(OpenAIWrapper):
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model: str = 'gpt-3.5-turbo-0613',
+                 retry: int = 5,
+                 wait: int = 3,
+                 verbose: bool = True,
+                 system_prompt: str = None,
+                 temperature: float = 0,
+                 timeout: int = 60,
+                 max_tokens: int = 1024,
+                 img_size: int = 512,
+                 img_detail: str = 'low',
+                 **kwargs):
+
+        self.model = model
+        if 'KEYS' in os.environ and osp.exists(os.environ['KEYS']):
+            keys = load(os.environ['KEYS'])
+            headers['alles-apin-token'] = keys.get('alles-apin-token', '')
+        elif 'ALLES' in os.environ:
+            headers['alles-apin-token'] = os.environ['ALLES']
+        self.headers = headers
+        self.temperature = temperature
+        self.timeout = timeout
+        self.max_tokens = max_tokens
+
+        assert img_size > 0 or img_size == -1
+        self.img_size = img_size
+        assert img_detail in ['high', 'low']
+        self.img_detail = img_detail
+
+        super(OpenAIWrapper, self).__init__(
+            wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
+
+    def generate_inner(self, inputs, **kwargs) -> str:
+        input_msgs = self.prepare_inputs(inputs)
+
+        temperature = kwargs.pop('temperature', self.temperature)
+        max_tokens = kwargs.pop('max_tokens', self.max_tokens)
+
+        # Held out 100 tokens as buffer
+        context_window = GPT_context_window(self.model)
+        max_tokens = min(max_tokens, context_window - self.get_token_len(inputs))
+        if 0 < max_tokens <= 100:
+            print('Less than 100 tokens left, may exceed the context window with some additional meta symbols. ')
+        if max_tokens <= 0:
+            return 0, self.fail_msg + 'Input string longer than context window. ', 'Length Exceeded. '
+
+        payload = dict(
+            model=self.model,
+            messages=input_msgs,
+            max_tokens=max_tokens,
+            n=1,
+            stop=None,
+            timeout=self.timeout,
+            temperature=temperature,
+            **kwargs)
+
+        response = requests.post(url, 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)
+            assert resp_struct['msg'] == 'ok' and resp_struct['msgCode'] == '10000', resp_struct
+            answer = resp_struct['data']['choices'][0]['message']['content'].strip()
+        except:
+            pass
+        return ret_code, answer, response
+
+
+class GPT4V_Internal(OpenAIWrapperInternal):
+
+    def generate(self, message, dataset=None):
+        return super(GPT4V_Internal, self).generate(message)