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# import json
# import os
# import glob
# import argparse
# import csv
#
#
# def chatgpt_json(merge_file):
# # chat results
# merge_data = merge_file.decode("utf-8")
# merge_data = eval(merge_data)
# correct_answer_file = 'file/ANSWER.json'
# with open(correct_answer_file, 'r', encoding='utf-8') as f:
# correct_answer_data = json.load(f)
#
# dataset_scores_dict = {}
# for dataset_name, item in merge_data.items():
#
# total_nums = len(item)
# correct = 0
# # assert len(item) >= len(correct_answer_data[dataset_name]), f'Video-Bench-Input.json---{dataset_name}---is incomplete!'
# for id, sub_item in item.items():
# if sub_item['output_chatgpt_choice'] == correct_answer_data[dataset_name][id]['answer']:
# correct += 1
#
# dataset_scores_dict[dataset_name] = round(correct / total_nums * 100, 2)
# return dataset_scores_dict
#
#
# def compute_scores(merge_file):
# dataset_score_dict = chatgpt_json(merge_file)
# dataset_weight = {
# 1:
# {
# "ActivityNet": 1,
# "MSVD": 1,
# "MSRVTT": 1,
# "TGIF": 1,
# "Youcook2": 1,
# "Ucfcrime": 1,
# "MOT": 0.5,
# },
#
# 2:
# {
# "TVQA": 1,
# "MV": 1,
# "NBA": 1,
# },
#
# 3:
# {
# "Driving-exam": 0.5,
# "Driving-decision-making": 1,
# "SQA3D": 1,
# }
#
# }
#
# # Video-exclusive Understanding score
# exclusive_understanding_weight = dataset_weight[1]
# weights_sum = sum(exclusive_understanding_weight.values())
# exclusive_understanding_score = 0
# # import ipdb; ipdb.set_trace()
# for dataset_name, weight in exclusive_understanding_weight.items():
# exclusive_understanding_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
# # Prior Knowledge-based Question-answer
# prior_QA_weight = dataset_weight[2]
# weights_sum = sum(prior_QA_weight.values())
# prior_QA_score = 0
# for dataset_name, weight in prior_QA_weight.items():
# prior_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
# # Comprehension and Decision-making
# com_and_dec_QA_weight = dataset_weight[3]
# weights_sum = sum(com_and_dec_QA_weight.values())
# com_and_dec_QA_score = 0
# for dataset_name, weight in com_and_dec_QA_weight.items():
# com_and_dec_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum
#
# dataset_score_dict['Exclusive_understanding'] = exclusive_understanding_score
# dataset_score_dict['Prior_Knowledge'] = prior_QA_score
# dataset_score_dict['Comprehension_and_Decision-making'] = com_and_dec_QA_score
#
# # final score
# final_score = sum([exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score]) / 3
# dataset_score_dict['final_score'] = final_score
#
# # print(dataset_score_dict)
# # with open(args.score_output_file, 'w', encoding='utf-8') as f:
# # json.dump(dataset_score_dict, f, indent=2)
# # print(f'{args.score_output_file} is saved!')
# # ========================
# data = [
#
# ["Avg. All", "Avg. Video-Exclusive", "Avg. Prior-Knowledge QA", "Avg. Decision-Making",
# "ActivityNet", "MSVD", "MSRVTT", "TGIF", "Youcook2", "Ucfcrime",
# "MOT", "TVQA", "MV", "NBA", "Driving-exam", "Driving-decision-making", "SQA3D"],
#
# [final_score, exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score,
# dataset_score_dict['ActivityNet'],
# dataset_score_dict["MSVD"],
# dataset_score_dict['MSRVTT'],
# dataset_score_dict['TGIF'],
# dataset_score_dict['Youcook2'],
# dataset_score_dict['Ucfcrime'],
# dataset_score_dict['MOT'],
# dataset_score_dict['TVQA'],
# dataset_score_dict['MV'],
# dataset_score_dict['NBA'],
# dataset_score_dict['Driving-exam'],
# dataset_score_dict['Driving-decision-making'],
# dataset_score_dict['SQA3D'],
# ],
# ]
#
# return data
#
import json
import os
import glob
import argparse
import csv
def chatgpt_json(merge_file):
# chat results
merge_data = merge_file.decode("utf-8")
merge_data = eval(merge_data)
correct_answer_file = 'file/ANSWER.json'
with open(correct_answer_file, 'r', encoding='utf-8') as f:
correct_answer_data = json.load(f)
dataset_scores_dict = {}
for dataset_name, item in merge_data.items():
total_nums = len(item)
correct = 0
# assert len(item) >= len(correct_answer_data[dataset_name]), f'Video-Bench-Input.json---{dataset_name}---is incomplete!'
for id, sub_item in item.items():
if sub_item['output_chatgpt_choice'] == correct_answer_data[dataset_name][id]['answer']:
correct += 1
# dataset_scores_dict[dataset_name] = round(correct / total_nums * 100, 2)
dataset_scores_dict[dataset_name] = round(correct / total_nums , 4)
return dataset_scores_dict
def compute_scores(merge_file):
dataset_score_dict = chatgpt_json(merge_file)
dataset_weight = {
1:
{
"ActivityNet": 1,
"MSVD": 1,
"MSRVTT": 1,
"TGIF": 1,
"Youcook2": 1,
"Ucfcrime": 1,
"MOT": 0.5,
},
2:
{
"TVQA": 1,
"MV": 1,
"NBA": 1,
},
3:
{
"Driving-exam": 0.5,
"Driving-decision-making": 1,
"SQA3D": 1,
}
}
# Video-exclusive Understanding score
exclusive_understanding_weight = dataset_weight[1]
weights_sum = sum(exclusive_understanding_weight.values())
exclusive_understanding_score = 0
# import ipdb; ipdb.set_trace()
for dataset_name, weight in exclusive_understanding_weight.items():
exclusive_understanding_score += weight * dataset_score_dict[dataset_name] / weights_sum * 100
# Prior Knowledge-based Question-answer
prior_QA_weight = dataset_weight[2]
weights_sum = sum(prior_QA_weight.values())
prior_QA_score = 0
for dataset_name, weight in prior_QA_weight.items():
prior_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum *100
# Comprehension and Decision-making
com_and_dec_QA_weight = dataset_weight[3]
weights_sum = sum(com_and_dec_QA_weight.values())
com_and_dec_QA_score = 0
for dataset_name, weight in com_and_dec_QA_weight.items():
com_and_dec_QA_score += weight * dataset_score_dict[dataset_name] / weights_sum *100
dataset_score_dict['Exclusive_understanding'] = exclusive_understanding_score
dataset_score_dict['Prior_Knowledge'] = prior_QA_score
dataset_score_dict['Comprehension_and_Decision-making'] = com_and_dec_QA_score
# final score
final_score = sum([exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score]) / 3
dataset_score_dict['final_score'] = final_score
# print(dataset_score_dict)
# with open(args.score_output_file, 'w', encoding='utf-8') as f:
# json.dump(dataset_score_dict, f, indent=2)
# print(f'{args.score_output_file} is saved!')
# ========================
data = [
["Avg. All", "Avg. Video-Exclusive", "Avg. Prior-Knowledge QA", "Avg. Decision-Making",
"ActivityNet", "MSVD", "MSRVTT", "TGIF", "Youcook2", "Ucfcrime",
"MOT", "TVQA", "MV", "NBA", "Driving-exam", "Driving-decision-making", "SQA3D"],
[final_score, exclusive_understanding_score, prior_QA_score, com_and_dec_QA_score,
dataset_score_dict['ActivityNet'],
dataset_score_dict["MSVD"],
dataset_score_dict['MSRVTT'],
dataset_score_dict['TGIF'],
dataset_score_dict['Youcook2'],
dataset_score_dict['Ucfcrime'],
dataset_score_dict['MOT'],
dataset_score_dict['TVQA'],
dataset_score_dict['MV'],
dataset_score_dict['NBA'],
dataset_score_dict['Driving-exam'],
dataset_score_dict['Driving-decision-making'],
dataset_score_dict['SQA3D'],
],
]
return data
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