| from ...smp import * |
| from .multiple_choice import extract_answer_from_item |
| from PIL import Image, ImageOps |
| import numpy as np |
|
|
| sys_prompt = "You are an AI assistant for question answering." |
|
|
| system_prompt_multi_choice = ( |
| "You will receive a multi-choice question, the ground-truth answer and the prediction from a question answering (QA) model. " |
| "Your task is to determine whether QA model prediction is correct, based on the question and ground-truth answer. " |
| "If the prediction is correct, respond \"Correct\". If the prediction is incorrect, respond \"Incorrect\"." |
| ) |
|
|
| system_prompt_caption_matching = ( |
| "You will receive a caption matching question, the ground-truth answer and the prediction from a question answering (QA) model. " |
| "Your task is to determine whether QA model prediction is correct, based on the question and ground-truth answer. " |
| "If the prediction is correct, respond \"Correct\". If the prediction is incorrect, respond \"Incorrect\"." |
| ) |
|
|
| system_prompt_captioning = """ |
| You will receive a video description and a multi-choice question. Your task is to choose the correct answer and briefly explain the reason why you choose the answer. \ |
| If none of the choice candidates are correct or the video description lacks enough information to answer the question, just answer "None of the choices are correct". \ |
| Please organize your response in this format: |
| ``` |
| Reasoning: [Your reason to obtain the answer] |
| Answer: [Your answer] |
| ``` |
| |
| Here are some examples of video description, multi-choice question and the expected answer: |
| ``` |
| Video Description: A person is palying football. |
| Multi-Choice Question: |
| What is the person doing in the video? |
| A. cooking |
| B. palying football |
| C. playing basketball |
| D. reading book |
| Reasoning: The video description mentions that the person is playing football. |
| Answer: B. palying football |
| |
| Video Description: A bird is flying clockwise. |
| Multi-Choice Question: |
| In which direction is the bird flying? |
| A. backwark |
| B. counter-clockwise |
| C. clockwise |
| D. downward |
| Reasoning: The video description mentions that the bird is flying clockwise |
| Answer: C. clockwise |
| |
| Video Description: An air balloon is inflating. |
| Multi-Choice Question: |
| What is happening to the air balloon? |
| A. exploding |
| B. getting smaller |
| C. flying |
| Reasoning: The video description mentions that the air balloon is inflating, while none of the coices can be explained as inflating. |
| Answer: None of the choices are correct |
| ``` |
| """ |
|
|
| system_prompt_YorN = """ |
| You will receive a Yes/No question, the ground-truth answer and the prediction from a question answering (QA) model. \ |
| Your task is to determine whether QA model prediction is correct, based on the question and ground-truth answer. \ |
| If the prediction is correct, respond "Correct". If the prediction is incorrect, respond "Incorrect". |
| """ |
|
|
|
|
| def eval_rule_caption_matching(line): |
| |
| video_llm_output = line['prediction'] |
| answer = line['answer'] |
| option_strs = eval(line['candidates']) |
| option_sents = [opt.split(': ')[1] for opt in option_strs] |
| |
| option_inds = [opt.split(': ')[0] for opt in option_strs] + [opt.split(': ')[0].replace('Sentence ', '').replace('Option ', '').replace('Caption ', '') for opt in option_strs] |
| video_llm_pred = None |
| for option_str in option_strs: |
| if option_str == video_llm_output: |
| video_llm_pred = option_str |
| for option_sent in option_sents: |
| if option_sent == video_llm_output or (') ' in video_llm_output and option_sent == video_llm_output.split(') ')[1]): |
| video_llm_pred = option_sent |
| for option_ind in option_inds: |
| if option_ind == video_llm_output or option_ind == video_llm_output.replace('.', ''): |
| video_llm_pred = option_ind |
|
|
| if video_llm_pred is None: |
| return "fail" |
| else: |
| return 1 if video_llm_pred == answer or video_llm_pred == answer.split(":")[0] or video_llm_pred == answer.split(": ")[1] or video_llm_pred == answer.split(": ")[0].split()[1] else 0 |
|
|
|
|
| def eval_rule_multi_choice(line): |
| if line['prediction'] == line['answer']: |
| return 1 |
| elif line['prediction'] in ['A', 'B', 'C', 'D']: |
| return 1 if line['prediction'] == line['answer'][0] else 0 |
| elif any(line['prediction'].startswith(prefix) for prefix in ['A.', 'B.', 'C.', 'D.']): |
| return 1 if line['prediction'].split('.')[0] == line['answer'][0] else 0 |
| elif any(line['prediction'].startswith(prefix) for prefix in ['A)', 'B)', 'C)', 'D)']): |
| return 1 if line['prediction'].split(')')[0] == line['answer'][0] else 0 |
| else: |
| return "fail" |
|
|
|
|
| def eval_rule_YorN(video_llm_output): |
| |
| video_llm_output = video_llm_output.lower() |
| if video_llm_output.startswith("yes"): |
| return "yes" |
| elif video_llm_output.startswith("no"): |
| return "no" |
| else: |
| return False |
|
|
|
|
| def llm_output_to_rating(llm_output): |
| if not ('Correct' in llm_output or 'Incorrect' in llm_output): |
| print(f"Warning: LLM output is not in the correct format: {llm_output}") |
| rating = 0 |
| return rating |
| if llm_output.startswith('Correct'): |
| rating = 1 |
| elif llm_output.startswith('Incorrect'): |
| rating = 0 |
| elif ('Correct' in llm_output) and ('Incorrect' not in llm_output): |
| rating = 1 |
| elif 'Incorrect' in llm_output: |
| rating = 0 |
| return rating |
|
|
|
|
| def parse_llm_output(llm_output, gt_answer): |
| if llm_output == "invalid_request_error" or not llm_output: |
| eval_result = {"rating": -1, "chatgpt-answer": None, "chatgpt-reasoning": None} |
| return eval_result |
|
|
| eval_result = {} |
| lines = llm_output.split("\n") |
|
|
| for line in lines: |
| line = line.strip() |
| if "Reasoning" in line: |
| eval_result['chatgpt-reasoning'] = line.replace("Reasoning:", "").strip() |
| if "Answer" in line: |
| eval_result['chatgpt-answer'] = line.replace("Answer:", "").strip() |
|
|
| if "chatgpt-answer" not in eval_result: |
| eval_result['chatgpt-answer'] = llm_output |
| if "chatgpt-reasoning" not in eval_result: |
| eval_result['chatgpt-reasoning'] = None |
|
|
| |
| |
| answer_counts = sum(eval_result['chatgpt-answer'].count(prefix) for prefix in ['A.', 'B.', 'C.', 'D.']) |
| if eval_result['chatgpt-answer'].split(". ")[0] == gt_answer.split(". ")[0] and answer_counts == 1: |
| eval_result['rating'] = 1 |
| else: |
| eval_result['rating'] = 0 |
| return eval_result |
|
|
|
|
| def evaluate_tempcompass_mcq(model, line): |
| eval_rules_dict = { |
| 'caption_matching': eval_rule_caption_matching, |
| 'multi-choice': eval_rule_multi_choice |
| } |
| gpt_eval_prompt = { |
| 'multi-choice': '{}\nMulti-Choice Question:\n{}\nGround-Truth Answer: {}\nModel Prediction: {}', |
| 'caption_matching': '{}\nCaption Matching Question:\n{}\nGround-Truth Answer: {}\nModel Prediction: {}' |
| } |
| base_prompt = { |
| 'multi-choice': system_prompt_multi_choice, |
| 'caption_matching': system_prompt_caption_matching |
| } |
| eval_result = { |
| "question": line['question'], |
| "answer": line['answer'], |
| "prediction": line['prediction'], |
| "task_type": line['task_type'], |
| "candidates": line['candidates'], |
| "match_success": True |
| } |
| result = eval_rules_dict[line['task_type']](line) |
| if result == "fail": |
| eval_result['match_success'] = False |
| if model is None: |
| eval_result['rating'] = 0 |
| else: |
| prompt_template = gpt_eval_prompt[line['task_type']] |
| prompt = prompt_template.format(base_prompt[line['task_type']], line['question'], line['answer'], line['prediction']) |
| llm_output = model.generate(prompt) |
| result = llm_output_to_rating(llm_output) |
| eval_result['chatgpt-response'] = llm_output |
| eval_result['rating'] = result |
| else: |
| eval_result['rating'] = result |
|
|
| return eval_result |
|
|
|
|
| def evaluate_tempcompass_captioning(model, line): |
| prompt = ( |
| f"{system_prompt_captioning}\n" |
| f"Video Description:{line['prediction']}\n" |
| f"Multi-Choice Question:\n{line['mc_question']}\n" |
| ) |
| if model is not None: |
| llm_output = model.generate(prompt) |
| eval_result = parse_llm_output(llm_output, gt_answer=line['mc_answer']) |
| return eval_result |
| else: |
| raise ValueError("Model is None, TempCompass Captioning task not supported exact matching") |
|
|
|
|
| def evaluate_tempcompass_YorN(model, line): |
| prompt = ( |
| f"{system_prompt_YorN}\n" |
| f"Yes/No Question:\n{line['question']}\n" |
| f"Ground-Truth Answer: {line['answer']}\n" |
| f"Model Prediction: {line['prediction']}" |
| ) |
| result = eval_rule_YorN(line['prediction']) |
| eval_result = { |
| "question": line['question'], |
| "answer": line['answer'], |
| "prediction": line['prediction'], |
| "match_success": True |
| } |
| if result: |
| eval_result['rating'] = 1 if result == line['answer'] else 0 |
| elif model is None: |
| eval_result['match_success'] = False |
| eval_result['rating'] = 0 |
| else: |
| eval_result['match_success'] = False |
| llm_output = model.generate(prompt) |
| result = llm_output_to_rating(llm_output) |
| eval_result['chatgpt-response'] = llm_output |
| eval_result['rating'] = result |
| return eval_result |
|
|
|
|
| def get_dimension_rating(score_file): |
| data = load(score_file) |
| result_dict = {} |
| for idx, item in data.iterrows(): |
| dict_key = item['dim'] + '. ' + item['task_type'] |
| if dict_key not in result_dict: |
| result_dict[dict_key] = [0,0] |
| result_dict[dict_key][0] += int(item['score']) |
| result_dict[dict_key][1] += 1 |
| return result_dict |
|
|
