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Running
on
Zero
Running
on
Zero
| import argparse | |
| import json | |
| import os | |
| import time | |
| from pytz import timezone | |
| from tqdm import tqdm | |
| import base64 | |
| from icecream import ic | |
| from PIL import Image | |
| # Function to encode the image | |
| def encode_image(image_path): | |
| with open(image_path, "rb") as image_file: | |
| return base64.b64encode(image_file.read()).decode('utf-8') | |
| def get_log_files(max_num_files=None): | |
| dates = [] | |
| for month in [2, 3]: | |
| for day in range(1, 32): | |
| dates.append(f"2024-{month:02d}-{day:02d}") | |
| num_servers = 1 | |
| filenames = [] | |
| for d in dates: | |
| for i in range(num_servers): | |
| # name = os.path.expanduser(f"~/fastchat_logs/server{i}/{d}-conv.json") | |
| name = os.path.expanduser(f"vision-arena-logs/{d}-conv.json") | |
| if os.path.exists(name): | |
| filenames.append(name) | |
| max_num_files = max_num_files or len(filenames) | |
| filenames = filenames[-max_num_files:] | |
| return filenames | |
| def pretty_print_conversation(messages): | |
| for role, msg in messages: | |
| print(f"[[{role}]]: {msg}") | |
| task_template_map = { | |
| "image_caption": "Give me the semantic alignment score between the given image and the given caption: \"{generated_sentence}\" on a scale of 0-100. Only reply the score value.", | |
| "vqa": "Rate the answer correctness regarding the question within the context of the given image on a scale of 0-100. Only reply the score value.", | |
| "pair_rate_old": "[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nGiven the instruction and the image, please compare the correctness of responses A and B. Reply with \"leftvote\" if you find A better, \"rightvote\" if B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both responses are equally satisfactory. If you are unable to make a decision, please reply with \"NA\".", | |
| "pair_rate_wexplanation": "<image>[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output your final verdict by strictly following this format: \"[[A]]\" if assistant A is better, \"[[B]]\" if assistant B is better, and \"[[C]]\" for a tie.", | |
| "pair_rate": "<image>[Instruction]\n\"{instruction}\"\n\n\"{generated_sentence}\"\n\n[System]\nPlease act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user’s instructions and answers the user’s question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. Reply with \"leftvote\" if you find assistant A better, \"rightvote\" if assistant B is better, \"bothbad_vote\" if both responses are wrong, and \"tievote\" if both assistants provide equally satisfactory answers. If you are unable to make a decision, please reply with \"NA\"." | |
| } | |
| def inspect_convs(log_files): | |
| json_data = [] | |
| ic(log_files) | |
| total_vote = 0 | |
| for filename in tqdm(log_files, desc="read files"): | |
| for retry in range(5): | |
| try: | |
| lines = open(filename).readlines() | |
| break | |
| except FileNotFoundError: | |
| time.sleep(2) | |
| for l in lines: | |
| row = json.loads(l) | |
| if "states" not in row: | |
| continue | |
| if row["type"] not in ["leftvote", "rightvote", "bothbad_vote", "tievote"]: | |
| continue | |
| model_names = row["states"][0]["model_name"], row["states"][1]["model_name"] | |
| # Iterate through each state and write the relevant information | |
| if not len(row["states"][0]['messages']): continue | |
| # ic(row["states"][0]['messages'][1][1]) | |
| if row["states"][0]['messages'][1][1] is None or row["states"][1]['messages'][1][1] is None or "NETWORK ERROR" in row["states"][0]['messages'][1][1] or "NETWORK ERROR" in row["states"][1]['messages'][1][1]: continue | |
| total_vote += 1 | |
| conv_id = row["states"][0]['conv_id'] | |
| image_path = os.path.join("/local/home/yujielu/project/Arena-Elo/vision-arena-logs", os.path.basename(filename)[:-5]+"input_images", f"input_image_{conv_id}.png") | |
| if not os.path.exists(image_path) : | |
| continue | |
| try: | |
| image = Image.open(image_path).convert("RGB") | |
| except: | |
| continue | |
| left_response = row["states"][0]['messages'][1][1] | |
| right_response = row["states"][1]['messages'][1][1] | |
| instruction = row["states"][0]['messages'][0][1] | |
| generated_sentence = f"[The Start of Assistant A’s Answer]\n{left_response}\n[The End of Assistant A’s Answer]\n\n[The Start of Assistant B’s Answer]\n{right_response}\n[The End of Assistant B’s Answer]" | |
| text_prompt = task_template_map["pair_rate"].format(instruction=instruction, generated_sentence=generated_sentence) | |
| user_input = text_prompt | |
| # Create the conversation structure | |
| conversation = [ | |
| { | |
| "from": "human", | |
| "value": user_input | |
| }, | |
| { | |
| "from": "gpt", | |
| "value": row["type"] | |
| } | |
| ] | |
| # Create the JSON object for each row | |
| json_obj = { | |
| "id": conv_id, | |
| "image": image_path, | |
| "conversations": conversation | |
| } | |
| # Append the JSON object to the list | |
| json_data.append(json_obj) | |
| # Write the JSON data to a file | |
| with open('output_evaluator_data.json', 'w') as json_file: | |
| json.dump(json_data, json_file, indent=2) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--max-num-files", type=int) | |
| args = parser.parse_args() | |
| log_files = get_log_files(args.max_num_files) | |
| inspect_convs(log_files) | |