update llava dependent folder and upgrade transformers 4.33.2 -> 4.37.2

.gitignore

@@ -186,4 +186,7 @@ playground
 log_reload.py
 
 *competition*
-apikey_local.yml
+apikey_local.yml
+llava-old/
+mmte/models/llava/serve/
+test_env/

env/requirements.txt

@@ -35,7 +35,7 @@ ninja==1.11.1.1
 torch==2.3.0+cu121
 torchvision==0.18.0+cu121
 tqdm==4.66.1
-transformers==4.33.2
+transformers==4.37.2
 webdataset==0.2.86
 datasets==2.18.0
 openai==1.9.0

mmte/models/__init__.py

@@ -1,22 +1,20 @@
 from mmte.utils.registry import registry
 from mmte.models.base import BaseChat, Response
-import transformers
+from mmte.models.llava_chat import LLaVAChat
+from mmte.models.llava_rlhf_chat import LLaVARLHFChat
+from mmte.models.mplug_owl2_chat import mPLUGOwl2Chat
+from mmte.models.internvl_chat import InternVLChat
+from mmte.models.lrv_instruction_chat import LRVInstructionChat
 from mmte.models.openai_chat import OpenAIChat
 from mmte.models.google_chat import GoogleChat
 from mmte.models.claude3_chat import ClaudeChat
 from mmte.models.qwen_plus_chat import QwenPlusChat
 from mmte.models.minigpt4_chat import MiniGPT4Chat
 from mmte.models.instructblip_chat import InstructBLIPChat
-if str(transformers.__version__)<'4.37.2':
-    from mmte.models.llava_chat import LLaVAChat
-    from mmte.models.llava_rlhf_chat import LLaVARLHFChat
-    from mmte.models.mplug_owl2_chat import mPLUGOwl2Chat
-    from mmte.models.internvl_chat import InternVLChat
 from mmte.models.qwen_chat import QwenChat
 from mmte.models.otter_chat import OtterChat
 from mmte.models.mplug_owl_chat import mPLUGOwlChat
 from mmte.models.internlm_xcomposer_chat import InternLMXComposerChat
-from mmte.models.lrv_instruction_chat import LRVInstructionChat
 from mmte.models.sharegpt4v_chat import ShareGPT4VChat
 from mmte.models.cogvlm_chat import CogVLMChat
 from mmte.models.phi3_chat import Phi3Chat

mmte/models/llava/conversation.py

@@ -1,6 +1,9 @@
 import dataclasses
 from enum import auto, Enum
 from typing import List, Tuple
+import base64
+from io import BytesIO
+from PIL import Image
 
 
 class SeparatorStyle(Enum):
@@ -68,7 +71,7 @@ def get_prompt(self):
                 else:
                     ret += role
         elif self.sep_style == SeparatorStyle.LLAMA_2:
-            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n"
+            wrap_sys = lambda msg: f"<<SYS>>\n{msg}\n<</SYS>>\n\n" if len(msg) > 0 else msg
             wrap_inst = lambda msg: f"[INST] {msg} [/INST]"
             ret = ""
 
@@ -106,79 +109,66 @@ def get_prompt(self):
     def append_message(self, role, message):
         self.messages.append([role, message])
 
+    def process_image(self, image, image_process_mode, return_pil=False, image_format='PNG', max_len=1344, min_len=672):
+        if image_process_mode == "Pad":
+            def expand2square(pil_img, background_color=(122, 116, 104)):
+                width, height = pil_img.size
+                if width == height:
+                    return pil_img
+                elif width > height:
+                    result = Image.new(pil_img.mode, (width, width), background_color)
+                    result.paste(pil_img, (0, (width - height) // 2))
+                    return result
+                else:
+                    result = Image.new(pil_img.mode, (height, height), background_color)
+                    result.paste(pil_img, ((height - width) // 2, 0))
+                    return result
+            image = expand2square(image)
+        elif image_process_mode in ["Default", "Crop"]:
+            pass
+        elif image_process_mode == "Resize":
+            image = image.resize((336, 336))
+        else:
+            raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
+        if max(image.size) > max_len:
+            max_hw, min_hw = max(image.size), min(image.size)
+            aspect_ratio = max_hw / min_hw
+            shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
+            longest_edge = int(shortest_edge * aspect_ratio)
+            W, H = image.size
+            if H > W:
+                H, W = longest_edge, shortest_edge
+            else:
+                H, W = shortest_edge, longest_edge
+            image = image.resize((W, H))
+        if return_pil:
+            return image
+        else:
+            buffered = BytesIO()
+            image.save(buffered, format=image_format)
+            img_b64_str = base64.b64encode(buffered.getvalue()).decode()
+            return img_b64_str
+
     def get_images(self, return_pil=False):
         images = []
         for i, (role, msg) in enumerate(self.messages[self.offset:]):
             if i % 2 == 0:
                 if type(msg) is tuple:
-                    import base64
-                    from io import BytesIO
-                    from PIL import Image
                     msg, image, image_process_mode = msg
-                    if image_process_mode == "Pad":
-                        def expand2square(pil_img, background_color=(122, 116, 104)):
-                            width, height = pil_img.size
-                            if width == height:
-                                return pil_img
-                            elif width > height:
-                                result = Image.new(pil_img.mode, (width, width), background_color)
-                                result.paste(pil_img, (0, (width - height) // 2))
-                                return result
-                            else:
-                                result = Image.new(pil_img.mode, (height, height), background_color)
-                                result.paste(pil_img, ((height - width) // 2, 0))
-                                return result
-                        image = expand2square(image)
-                    elif image_process_mode in ["Default", "Crop"]:
-                        pass
-                    elif image_process_mode == "Resize":
-                        image = image.resize((336, 336))
-                    else:
-                        raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
-                    max_hw, min_hw = max(image.size), min(image.size)
-                    aspect_ratio = max_hw / min_hw
-                    max_len, min_len = 800, 400
-                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
-                    longest_edge = int(shortest_edge * aspect_ratio)
-                    W, H = image.size
-                    if longest_edge != max(image.size):
-                        if H > W:
-                            H, W = longest_edge, shortest_edge
-                        else:
-                            H, W = shortest_edge, longest_edge
-                        image = image.resize((W, H))
-                    if return_pil:
-                        images.append(image)
-                    else:
-                        buffered = BytesIO()
-                        image.save(buffered, format="PNG")
-                        img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                        images.append(img_b64_str)
+                    image = self.process_image(image, image_process_mode, return_pil=return_pil)
+                    images.append(image)
         return images
 
     def to_gradio_chatbot(self):
         ret = []
         for i, (role, msg) in enumerate(self.messages[self.offset:]):
             if i % 2 == 0:
                 if type(msg) is tuple:
-                    import base64
-                    from io import BytesIO
                     msg, image, image_process_mode = msg
-                    max_hw, min_hw = max(image.size), min(image.size)
-                    aspect_ratio = max_hw / min_hw
-                    max_len, min_len = 800, 400
-                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
-                    longest_edge = int(shortest_edge * aspect_ratio)
-                    W, H = image.size
-                    if H > W:
-                        H, W = longest_edge, shortest_edge
-                    else:
-                        H, W = shortest_edge, longest_edge
-                    image = image.resize((W, H))
-                    buffered = BytesIO()
-                    image.save(buffered, format="JPEG")
-                    img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                    img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
+                    img_b64_str = self.process_image(
+                        image, "Default", return_pil=False,
+                        image_format='JPEG')
+                    img_str = f'<img src="data:image/jpeg;base64,{img_b64_str}" alt="user upload image" />'
                     msg = img_str + msg.replace('<image>', '').strip()
                     ret.append([msg, None])
                 else:
@@ -357,13 +347,38 @@ def dict(self):
     version="v1_mmtag",
 )
 
+conv_mistral_instruct = Conversation(
+    system="",
+    roles=("USER", "ASSISTANT"),
+    version="llama_v2",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.LLAMA_2,
+    sep="",
+    sep2="</s>",
+)
+
+conv_chatml_direct = Conversation(
+    system="""<|im_start|>system
+Answer the questions.""",
+    roles=("<|im_start|>user\n", "<|im_start|>assistant\n"),
+    version="mpt",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.MPT,
+    sep="<|im_end|>",
+)
+
 default_conversation = conv_vicuna_v1
 conv_templates = {
     "default": conv_vicuna_v0,
     "v0": conv_vicuna_v0,
     "v1": conv_vicuna_v1,
     "vicuna_v1": conv_vicuna_v1,
     "llama_2": conv_llama_2,
+    "mistral_instruct": conv_mistral_instruct,
+    "chatml_direct": conv_chatml_direct,
+    "mistral_direct": conv_chatml_direct,
 
     "plain": conv_llava_plain,
     "v0_plain": conv_llava_plain,

mmte/models/llava/eval/eval_gpt_review.py

@@ -0,0 +1,113 @@
+import argparse
+import json
+import os
+
+import openai
+import tqdm
+import ray
+import time
+
+NUM_SECONDS_TO_SLEEP = 3
+
+@ray.remote(num_cpus=4)
+def get_eval(content: str, max_tokens: int):
+    while True:
+        try:
+            response = openai.ChatCompletion.create(
+                model='gpt-4',
+                messages=[{
+                    'role': 'system',
+                    'content': 'You are a helpful and precise assistant for checking the quality of the answer.'
+                }, {
+                    'role': 'user',
+                    'content': content,
+                }],
+                temperature=0.2,  # TODO: figure out which temperature is best for evaluation
+                max_tokens=max_tokens,
+            )
+            break
+        except openai.error.RateLimitError:
+            pass
+        except Exception as e:
+            print(e)
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    print('success!')
+    return response['choices'][0]['message']['content']
+
+
+def parse_score(review):
+    try:
+        score_pair = review.split('\n')[0]
+        score_pair = score_pair.replace(',', ' ')
+        sp = score_pair.split(' ')
+        if len(sp) == 2:
+            return [float(sp[0]), float(sp[1])]
+        else:
+            print('error', review)
+            return [-1, -1]
+    except Exception as e:
+        print(e)
+        print('error', review)
+        return [-1, -1]
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.')
+    parser.add_argument('-q', '--question')
+    # parser.add_argument('-a', '--answer')
+    parser.add_argument('-a', '--answer-list', nargs='+', default=[])
+    parser.add_argument('-r', '--rule')
+    parser.add_argument('-o', '--output')
+    parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output')
+    args = parser.parse_args()
+
+    ray.init()
+
+    f_q = open(os.path.expanduser(args.question))
+    f_ans1 = open(os.path.expanduser(args.answer_list[0]))
+    f_ans2 = open(os.path.expanduser(args.answer_list[1]))
+    rule_dict = json.load(open(os.path.expanduser(args.rule), 'r'))
+
+    review_file = open(f'{args.output}', 'w')
+
+    js_list = []
+    handles = []
+    idx = 0
+    for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2):
+        # if idx == 1:
+        #     break
+
+        ques = json.loads(ques_js)
+        ans1 = json.loads(ans1_js)
+        ans2 = json.loads(ans2_js)
+
+        category = json.loads(ques_js)['category']
+        if category in rule_dict:
+            rule = rule_dict[category]
+        else:
+            rule = rule_dict['default']
+        prompt = rule['prompt']
+        role = rule['role']
+        content = (f'[Question]\n{ques["text"]}\n\n'
+                   f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
+                   f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
+                   f'[System]\n{prompt}\n\n')
+        js_list.append({
+            'id': idx+1,
+            'question_id': ques['question_id'],
+            'answer1_id': ans1['answer_id'],
+            'answer2_id': ans2['answer_id'],
+            'category': category})
+        idx += 1
+        handles.append(get_eval.remote(content, args.max_tokens))
+        # To avoid the rate limit set by OpenAI
+        time.sleep(NUM_SECONDS_TO_SLEEP)
+
+    reviews = ray.get(handles)
+    for idx, review in enumerate(reviews):
+        scores = parse_score(review)
+        js_list[idx]['content'] = review
+        js_list[idx]['tuple'] = scores
+        review_file.write(json.dumps(js_list[idx]) + '\n')
+    review_file.close()