from functools import partial import os import json from typing import OrderedDict import tqdm import torch from PIL import Image import ast import numpy as np from multiprocessing import Pool from decord import VideoReader, cpu import os from tasks.eval.eval_utils import ( dump_json, load_json, EvalDataset, ) from dataclasses import dataclass from openai import OpenAI from utils.easydict import EasyDict client = OpenAI( # This is the default and can be omitted api_key=os.environ.get("OPENAI_API_KEY"), ) task_type2chatgpt_contents = OrderedDict({ "Panda70M": { "system": "You are an intelligent chatbot designed for evaluating the correctness of generative outputs for video captioning. " "Your task is to compare the predicted captioning with a provided hint (which is usually a ground truth caption provided by human labor or autmated captioning pipeline)." "You should determine if they match meaningfully, logically and precisely. Here's how you can accomplish the task:" "------" "##INSTRUCTIONS: " "- Focus on the meaningful match between the predicted answer and the correct answer.\n" "- Consider synonyms or paraphrases as valid matches.\n" "- Evaluate the correctness of the prediction compared to the answer.", "user": """Please evaluate the following video-based Captioning pair:\n\n""" """Caption: {caption}\n""" """Predicted Caption: {pred}\n\n""" """Provide your evaluation only as a yes/no and score where the score is an integer value between 0 and 5, with 5 indicating the highest meaningful match. """ """Please generate the response in the form of a Python dictionary string with keys 'pred' and 'score', where value of 'pred' is a string of 'yes' or 'no' and value of 'score' is in INTEGER, not STRING.""" """DO NOT PROVIDE ANY OTHER OUTPUT TEXT OR EXPLANATION. Only provide the Python dictionary string. """ """For example, your response should look like this: {{'pred': 'yes', 'score': 4.8}}.""" }, }) # Follow the instructions carefully and be helpful and precise with your answer. def check_ans_recaption(pred, gt, task_type, model="gpt-3.5-turbo-0125"): try: # Compute the temporal understanding score user_input = task_type2chatgpt_contents[task_type]['user'] user_input = user_input.format(caption=gt, pred=pred) completion = client.chat.completions.create( model=model, messages=[ { "role": "system", "content": task_type2chatgpt_contents[task_type]['system'], }, { "role": "user", "content": user_input, } ] ) # Convert response to a Python dictionary. # response_message = completion["choices"][0]["message"]["content"] response_message = completion.choices[0].message.content num_tokens_openai = completion.usage.total_tokens response_dict = ast.literal_eval(response_message) pred = response_dict['pred'] score = response_dict['score'] if not pred in ('yes', 'no') or not isinstance(score, (int, float)): raise ValueError(f"{model} doesn't follow") flag = pred == 'yes' except Exception as e: import traceback traceback.print_exc() flag, score, num_tokens_openai = False, 0, 0 print( f"GPT cannot deal with:\n" f"--pred: {pred}\n" f"--gt: {gt}\n" f"--gpt responded: {response_message}\n" "--will assign flag=False and score=0" ) print(f"Dumb Answer in {task_type}") return flag, score, num_tokens_openai def chatgpt_eval(res, model="gpt-3.5-turbo-0125"): pred = res['pred'] gt = res['caption'] task_type = res['task_type'] correct, score, num_tokens_openai = check_ans_recaption(pred=pred, gt=gt,task_type=task_type, model=model) # acc is bool, score is given by chatgpt # update the scores in result_list for this sample res['score'] = score res['correct'] = correct res['num_tokens_openai'] = num_tokens_openai return res def save_results(result_list, save_path, model="gpt-3.5-turbo-0125"): dump_json(result_list, save_path, 'inference_results.json') with Pool(7) as pool: func = partial(chatgpt_eval, model=model) result_list = [ res for res in tqdm.tqdm(pool.imap_unordered(func, result_list), total=len(result_list), desc='Language Chat Model Automated Evaluation...')] # result_list = [chatgpt_eval(res, model=model) for res in result_list] final_res, acc_dict = {}, {} correct, total, total_score = 0, 0, 0 for i, res in enumerate(result_list): task_type = res['task_type'] if task_type not in acc_dict: acc_dict[task_type] = { 'correct': 0, 'total': 0, 'score': 0, } # correct, total acc_dict[task_type]['total'] += 1 acc_dict[task_type]['correct'] += res['correct'] acc_dict[task_type]['score'] += res['score'] for k, v in acc_dict.items(): final_res[k] = { 'acc': v['correct'] / v['total'] * 100, 'score': v['score'] / v['total'] } correct += v['correct'] total += v['total'] total_score += v['score'] final_res['Avg_Acc'] = correct / total * 100 final_res['Avg_Score'] = total_score / total all_results = { "acc_dict": acc_dict, "result_list": result_list } dump_json(all_results, save_path, f'final_results-{model}.json') dump_json(final_res, save_path, 'upload_leaderboard.json') def load_results(save_path, model="gpt-3.5-turbo-0125"): result_list = load_json(save_path, f'final_results-{model}.json') if result_list is not None: result_list = result_list['result_list'] if result_list is None: result_list = load_json(save_path, 'inference_results.json') return result_list class CaptionSample(EasyDict): def get_info(self): return {} class RecaptionSample(EasyDict): caption: str def get_info(self): # template = ("""To facilitate success in the task, I'll offer hints from the automated image captioning pipeline's output on the frames. """ # """Please note that this information may contain noise but remains descriptive.""" # """Presented below are the noisy details:\n""" # """Hint: {hint}\n""" # """The hint comprises noisy captions generated for certain frames in the video. """ # """Please refrain from disclosing the original hints provided; instead, provide rewritten accurate information.""") # hint = template.format(hint=self.hint,) return { "noisy_caption": self.caption } class RecaptionSampleWithMatchingScore(EasyDict): caption: str matching_score: float def get_info(self): # template = ("""To facilitate success in the task, I'll offer hints from the automated image captioning pipeline's output on the frames. """ # """Please note that this information may contain noise but remains descriptive.""" # """Presented below are the noisy details:\n""" # """Hint: {hint}\n""" # """Matching Score: {matching_score:.02f}\n""" # """The hint comprises noisy captions generated for certain frames in the video. """ # """Matching scores indicate the likelihood of these captions matching the original frames.\n""" # """Please refrain from disclosing the original hints provided; instead, provide rewritten accurate information.""" # ) # hint = template.format(hint=self.hint, # matching_score=self.matching_score) info = { "noisy_caption": self.caption, "matching_score": self.matching_score, } # by far, might use some prompting. return info class RecaptionDataset(EvalDataset): data_dir = "DATAS/Recaption" data_list_info = OrderedDict({ # "Panda70M": OrderedDict( # json_relpath="Panda70M/annotations.json", # prefix="DATAS/Recaption/Panda70M/videos", # data_type="video", # bound=False, # key_rename_map={ # # 'caption': 'hint', # }, # name_key='video_name', # postfix=('mp4', 'mkv', 'webm'), # recaption_type=RecaptionSample, # ), # don't has start & end "Inter4K": OrderedDict( json_relpath="Inter4K/annotations.json", prefix="DATAS/Recaption/Inter4K/60fps/UHD", data_type="video", bound=False, key_rename_map={ # 'caption': 'hint', }, name_key='video_name', postfix=('mp4', 'mkv', 'webm'), recaption_type=CaptionSample, ), # don't has start & end }) def __init__(self, *args, **kwargs): # recaption's test_ratio should shuffle the dataset test_ratio = kwargs.pop('test_ratio', None) super().__init__(*args, **kwargs) self.test_ratio = test_ratio test_ratio = 1. if test_ratio is None else test_ratio data_list_info = self.data_list_info data_dir = self.data_dir self.data_list = [] for k, v in data_list_info.items(): with open(os.path.join(data_dir, v['json_relpath']), 'r') as f: annotation_json_data = json.load(f) indexs = list(range(len(annotation_json_data))) np.random.RandomState(42).shuffle(indexs) num_samples = int(len(indexs) * test_ratio) if 0 < test_ratio <= 1 else int(test_ratio) indexs = indexs[:num_samples] for i in indexs: annotation_data = annotation_json_data[i] for key_old, key_new in v['key_rename_map'].items(): # temporary renameing the keys value = annotation_data.pop(key_old) annotation_data[key_new] = value data = dict(annotation_data) self.data_list.append({ 'task_type': k, 'data': data, }) def __getitem__(self, idx): task_type = self.data_list[idx]['task_type'] decord_method = self.decord_method[self.data_list_info[task_type]['data_type']] bound = None if self.data_list_info[task_type]['bound']: bound = ( self.data_list[idx]['data']['start'], self.data_list[idx]['data']['end'], ) video_name_key = self.data_list_info[task_type]['name_key'] video_name = self.data_list[idx]['data'][video_name_key] video_postfixs = self.data_list_info[task_type]['postfix'] video_paths = [] for p in video_postfixs: video_path = os.path.join(self.data_list_info[task_type]['prefix'], video_name + '.' + p) if os.path.exists(video_path): video_paths.append(video_path) assert len(video_paths) > 0, f'no video named {video_name}' # video_filename = self.data_list[idx]['data'][video_name_key] + video_postfix video_path = video_paths[0] images_group = decord_method(video_path, bound) sample = self.data_list_info[task_type]['recaption_type'](**self.data_list[idx]['data'],) info = sample.get_info() return { 'video_pils': images_group, # some might use the original pils and do their own transforms 'video_path': video_path, 'info': info, 'sample': sample, 'task_type': task_type, }