-
Notifications
You must be signed in to change notification settings - Fork 3
/
random_guess.py
164 lines (122 loc) · 7.39 KB
/
random_guess.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
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))