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random_guess.py
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import csv
import json
from tqdm import tqdm
import numpy as np
from prettytable import PrettyTable
import os
import time
from utils import *
import random
import openai
### to evaluate your method, implement and run generate_answer function!
root_dir = "."
###
input_file_name = "HallusionBench.json"
save_json_path_vd = "./hallusion_output_vd_random_guess2.json"
save_json_path_vs = "./hallusion_output_vs_random_guess2.json"
###
# load_json = False
load_json = True
model_output_entry = "model_prediction"
model_correctness_entry = "gpt4v_output_gpt_check"
def generate_answer(data, model_output_entry):
for i in data:
i[model_output_entry] = "Yes" if random.random() > 0.5 else "No"
## TODO
## implement this section with yout model!
## your_function(img_filename, question) -> "0" (No), "1" (Yes), "2" (Uncertain)
# for r in data:
# r[model_output_entry] = your_function(r["filename"], r["question"])
return data
if __name__ == "__main__":
data_vd = []
data_vs = []
with open(input_file_name) as json_file:
datas = json.load(json_file)
datas = generate_answer(datas, model_output_entry)
for data in tqdm(datas):
if data['category'] == 'VD':
data_vd.append(data)
if data['category'] == 'VS':
data_vs.append(data)
data_vd = evaluate_by_chatgpt(data_vd, model_output_entry, model_correctness_entry, load_json=load_json, save_json_path=save_json_path_vd)
data_vd = check_same_by_chatgpt(data_vd, model_output_entry, load_json=load_json, save_json_path=save_json_path_vd)
#time.sleep(60) #
try:
data_vs = evaluate_by_chatgpt(data_vs, model_output_entry, model_correctness_entry, load_json=load_json, save_json_path=save_json_path_vs)
data_vs = check_same_by_chatgpt(data_vs, model_output_entry, load_json=load_json, save_json_path=save_json_path_vs)
except:
time.sleep(60)
data_vs = evaluate_by_chatgpt(data_vs, model_output_entry, model_correctness_entry, load_json=load_json, save_json_path=save_json_path_vs)
data_vs = check_same_by_chatgpt(data_vs, model_output_entry, load_json=load_json, save_json_path=save_json_path_vs)
print("##### GPT Evaluate #####")
data_vd = assign_correctness(data_vd, correctness_entry=model_correctness_entry)
data_vs = assign_correctness(data_vs, correctness_entry=model_correctness_entry)
data = data_vd + data_vs
all_data = get_eval_all(data, model_correctness_entry)
all_vd = get_eval_all(data_vd, model_correctness_entry)
all_vs = get_eval_all(data_vs, model_correctness_entry)
table1 = [["per question", "Total"],
["VD", round(100 * all_vd["correct"]/all_vd["total"], 4)],
["VS", round(100 * all_vs["correct"]/all_vs["total"], 4)],
["Overall", round(100 * all_data["correct"]/all_data["total"], 4)]]
tab1 = PrettyTable(table1[0])
tab1.add_rows(table1[1:])
q_acc_gpt = round(100 * all_data["correct"]/all_data["total"], 4)
all_data = get_eval_pair_all(data, model_correctness_entry)
easy = get_eval_pair_easy(data)
hard = get_eval_pair_hard(data)
all_vd = get_eval_pair_all(data_vd, model_correctness_entry)
easy_vd = get_eval_pair_easy(data_vd)
hard_vd = get_eval_pair_hard(data_vd)
all_vs = get_eval_pair_all(data_vs, model_correctness_entry)
easy_vs = get_eval_pair_easy(data_vs)
hard_vs = get_eval_pair_hard(data_vs)
# question pair level
table3 = [["per question pair", "Easy", "Hard", "Total"],
["VD", round(100 * easy_vd["correct"]/easy_vd["total"], 4), round(100 * hard_vd["correct"]/hard_vd["total"], 4), round(100 * all_vd["correct"]/all_vd["total"], 4)],
["VS", round(100 * easy_vs["correct"]/easy_vs["total"], 4), round(100 * hard_vs["correct"]/hard_vs["total"], 4), round(100 * all_vs["correct"]/all_vs["total"], 4)],
["Overall", round(100 * easy["correct"]/easy["total"], 4), round(100 * hard["correct"]/hard["total"], 4), round(100 * all_data["correct"]/all_data["total"], 4)]]
tab3 = PrettyTable(table3[0])
tab3.add_rows(table3[1:])
#print(tab3)
fig_all = get_eval_fig(data)
fig_vd = get_eval_fig(data_vd)
fig_vs = get_eval_fig(data_vs)
# image level
table2 = [["per figure", "Correct", "Wrong", "Score"],
["VD", round(100 * fig_vd["correct"]/fig_vd["total"], 4), round(100 * fig_vd["inconsistent"]/fig_vd["total"], 4) + round(100 * fig_vd["wrong"]/fig_vd["total"], 4), round(fig_vd["score"], 4)],
["VS", round(100 * fig_vs["correct"]/fig_vs["total"], 4), round(100 * fig_vs["inconsistent"]/fig_vs["total"], 4) + round(100 * fig_vs["wrong"]/fig_vs["total"], 4), round(fig_vs["score"], 4)],
["Overall", round(100 * fig_all["correct"]/fig_all["total"], 4), round(100 * fig_all["inconsistent"]/fig_all["total"], 4) + round(100 * fig_all["wrong"]/fig_all["total"], 4), round(fig_all["score"], 4)]]
tab2 = PrettyTable(table2[0])
tab2.add_rows(table2[1:])
pair_acc_gpt = round(100 * all_data["correct"]/all_data["total"], 4)
figure_acc_gpt = round(100 * fig_all["correct"]/fig_all["total"], 4)
easy_acc_gpt = round(100 * easy["correct"]/easy["total"], 4)
hard_acc_gpt = round(100 * hard["correct"]/hard["total"], 4)
print("##### Question Stats #####")
print("Easy Questions: " + str(easy_vd["total_q"]) + "(Visual Dependent) + " + str(easy_vs["total_q"]) + "(Visual Supplement)")
print("Hard Questions: " + str(hard_vd["total_q"]) + "(Visual Dependent) + " + str(hard_vs["total_q"]) + "(Visual Supplement)")
print("Total Questions: " + str(all_data["total_q"]))
print("##### Figure Stats #####")
print("Visual Dependent Figures: " + str(fig_vd["total"]))
print("Visual Supplement Figures: " + str(fig_vs["total"]))
print("Total Figures: " + str(fig_all["total"]))
print("##### Leaderboard Stats #####")
table = [["", "Acc per question pair (qAcc)", "Acc per figure (fAcc)", "Acc per easy question (easy aAcc)", "Acc per hard question (hard aAcc)", "Acc per question (aAcc)"],
["GPT Eval", pair_acc_gpt, figure_acc_gpt, easy_acc_gpt, hard_acc_gpt, q_acc_gpt]]
leaderboard = PrettyTable(table[0])
leaderboard.add_rows(table[1:])
print(leaderboard)
stats = yes_ratio_stats(data)
table = [["", "Yes/No Bias (Pct Diff)", "Yes/No Bias (FP Ratio)", "Consistency Test (correct)", "Consistency Test (inconsistent)", "Consistency Test (wrong)", "LH", "VI", "Mixed"],
["GPT Eval", stats["diff"], stats["fp"], round(100 * fig_all["correct"]/fig_all["total"], 4), round(100 * fig_all["inconsistent"]/fig_all["total"], 4), round(100 * fig_all["wrong"]/fig_all["total"], 4), round(100 * all_data["LH_cg"]/(all_data["LH_cg"] + all_data["VI_cg"] + all_data["Mix_cg"]), 4), round(100 * all_data["VI_cg"]/(all_data["LH_cg"] + all_data["VI_cg"] + all_data["Mix_cg"]), 4), round(100 * all_data["Mix_cg"]/(all_data["LH_cg"] + all_data["VI_cg"] + all_data["Mix_cg"]), 4)]]
test = PrettyTable(table[0])
test.add_rows(table[1:])
print(test)
orig = [i for i in data if int(i["visual_input"]) == 1]
edit = [i for i in data if int(i["visual_input"]) == 2]
a = np.unique([i["category"] + "_" + i["subcategory"] + "_" + i["set_id"] + "_" + i["figure_id"] for i in orig])
b = np.unique([i["category"] + "_" + i["subcategory"] + "_" + i["set_id"] + "_" + i["figure_id"] for i in edit])
print(len(a))
print(len(b))