Feature add glmv

vlmeval/api/__init__.py

@@ -7,9 +7,10 @@
 from .stepai import Step1V_INT
 from .claude import Claude_Wrapper, Claude3V
 from .reka import Reka
+from .glm_vision import GLMVisionAPI
 
 __all__ = [
     'OpenAIWrapper', 'HFChatModel', 'OpenAIWrapperInternal', 'GeminiWrapper',
     'GPT4V', 'GPT4V_Internal', 'GeminiProVision', 'QwenVLWrapper', 'QwenVLAPI',
-    'QwenAPI', 'Claude3V', 'Claude_Wrapper', 'Reka', 'Step1V_INT'
+    'QwenAPI', 'Claude3V', 'Claude_Wrapper', 'Reka', 'Step1V_INT', 'GLMVisionAPI'
 ]

vlmeval/api/glm_vision.py

@@ -0,0 +1,101 @@
+from vlmeval.smp import *
+from vlmeval.api.base import BaseAPI
+from vlmeval.utils.dataset import DATASET_TYPE
+from vlmeval.smp.vlm import encode_image_file_to_base64
+
+
+class GLMVisionWrapper(BaseAPI):
+
+    is_api: bool = True
+
+    def __init__(self,
+                 model: str,
+                 retry: int = 5,
+                 wait: int = 5,
+                 key: str = None,
+                 verbose: bool = True,
+                 system_prompt: str = None,
+                 max_tokens: int = 1024,
+                 proxy: str = None,
+                 **kwargs):
+
+        self.model = model
+        self.fail_msg = 'Failed to obtain answer via API. '
+        self.default_params = {
+            'top_p': 0.6,
+            'top_k': 2,
+            'temperature': 0.8,
+            'repetition_penalty': 1.1,
+            'best_of': 1,
+            'do_sample': True,
+            'stream': False,
+            'max_tokens': max_tokens
+        }
+        if key is None:
+            key = os.environ.get('GLMV_API_KEY', None)
+        assert key is not None, (
+            'Please set the API Key (obtain it here: '
+            'https://open.bigmodel.cn/dev/howuse/introduction)'
+        )
+        self.key = key
+        super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs)
+
+    def image_to_base64(self, image_path):
+        import base64
+        with open(image_path, 'rb') as image_file:
+            encoded_string = base64.b64encode(image_file.read())
+            return encoded_string.decode('utf-8')
+
+    def build_msgs(self, msgs_raw, system_prompt=None, dataset=None):
+        msgs = cp.deepcopy(msgs_raw)
+        content = []
+        text = ''
+        for i, msg in enumerate(msgs):
+            if msg['type'] == 'text':
+                text += msg['value']
+            elif msg['type'] == 'image':
+                content.append(dict(type='image_url', image_url=dict(url=encode_image_file_to_base64(msg['value']))))
+        if DATASET_TYPE(dataset) in ['multi-choice', 'Y/N']:
+            text += '\nShort Answer.'
+        content.append(dict(type='text', text=text))
+        ret = [dict(role='user', content=content)]
+        return ret
+
+    def generate_inner(self, inputs, **kwargs) -> str:
+        assert isinstance(inputs, str) or isinstance(inputs, list)
+        inputs = [inputs] if isinstance(inputs, str) else inputs
+        messages = self.build_msgs(msgs_raw=inputs, dataset=kwargs['dataset'])
+
+        url = 'https://api.chatglm.cn/v1/chat/completions'
+        headers = {
+            'Content-Type': 'application/json',
+            'Request-Id': 'remote-test',
+            'Authorization': f'Bearer {self.key}'
+        }
+        payload = {
+            'model': self.model,
+            'messages': messages,
+            **self.default_params
+        }
+        response = requests.post(url, headers=headers, data=json.dumps(payload), verify=False)
+        output = []
+        try:
+            assert response.status_code == 200
+            for line in response.iter_lines():
+                data = json.loads(line.decode('utf-8').lstrip('data: '))
+                output.append(data['choices'][0]['message']['content'])
+            answer = ''.join(output).replace('</s>', '')
+            if self.verbose:
+                self.logger.info(f'inputs: {inputs}\nanswer: {answer}')
+            return 0, answer, 'Succeeded! '
+        except Exception as err:
+            if self.verbose:
+                self.logger.error(err)
+                self.logger.error(f'The input messages are {inputs}.')
+            return -1, self.fail_msg, ''
+
+
+class GLMVisionAPI(GLMVisionWrapper):
+
+    def generate(self, message, dataset=None):
+        return super(GLMVisionAPI, self).generate(message, dataset=dataset)

vlmeval/config.py

@@ -54,6 +54,8 @@
     'Claude3V_Opus': partial(Claude3V, model='claude-3-opus-20240229', temperature=0, retry=10),
     'Claude3V_Sonnet': partial(Claude3V, model='claude-3-sonnet-20240229', temperature=0, retry=10),
     'Claude3V_Haiku': partial(Claude3V, model='claude-3-haiku-20240307', temperature=0, retry=10),
+    # GLM4V
+    'GLM4V': partial(GLMVisionAPI, model='glm4v-biz-eval', temperature=0, retry=10),
 }
 
 xtuner_series = {