Datasets:
File size: 6,580 Bytes
45f037d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
import pathlib
import textwrap
import google.generativeai as genai
import PIL.Image
import time
import os
import random
import numpy as np
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
# torch.manual_seed(seed)
# torch.cuda.manual_seed(seed)
# torch.backends.cudnn.deterministic = True
# env.seed(seed)
seed_everything(1)
def random_generate_questions():
if os.path.isdir("questions-hole-pos-50"):
print("Questions are already generated")
return
os.makedirs("questions-hole-pos-50", exist_ok=False)
os.makedirs("answers-hole-pos-50", exist_ok=False)
for level in range(3,9):
os.makedirs("questions-hole-pos-50/level%d"%(level))
os.makedirs("answers-hole-pos-50/level%d"%(level))
for question_id in range(100):
# random ask a grid
row_num = random.sample(range(level), 1)[0]
col_num = random.sample(range(level), 1)[0]
# find the GT
with open("utils/maps-50-text/level%d/%d.txt"%(level, question_id), "r") as f:
contents = f.read()
rows = contents.split('\n')
grid_content = rows[row_num][col_num]
if grid_content == "H":
answer = "Y"
else:
answer = "N"
# write question and answer
question = "Is there a hole in row %d, column %d?"%(row_num+1, col_num+1)
with open("questions-hole-pos-50/level%d/%d.txt"%(level, question_id), "w") as f:
f.write(question)
with open("answers-hole-pos-50/level%d/%d.txt"%(level, question_id), "w") as f:
f.write(answer)
levels = [3,4,5,6,7,8]
in_context_example_num = 0 # 0, 1, 2, 4, 8
GOOGLE_API_KEY='YOUR-API-KEY'
genai.configure(api_key=GOOGLE_API_KEY)
model = genai.GenerativeModel('gemini-pro-vision')
if in_context_example_num > 0:
output_path = "output/output_img_%d/"%(in_context_example_num)
input_backup_path = "input/input_backup_img_%d/"%(in_context_example_num)
else:
output_path = "output/output_img/"
input_backup_path = "input/input_backup_img/"
os.makedirs(output_path, exist_ok=True)
os.makedirs(input_backup_path, exist_ok=True)
EXAMPLE_DICT = {
3: [],
4: [],
5: [],
6: [],
7: [],
8: [],
}
# Prepare examples
for level in levels:
for example_id in range(8):
img_input = PIL.Image.open("example/level%d/img/%d.png"%(level, example_id))
with open("example/level%d/question/%d.txt"%(level, example_id), "r") as f:
question_input = f.read()
with open("example/level%d/answer/%d.txt"%(level, example_id), "r") as f:
answer_input = f.read()
this_example = (img_input, question_input, answer_input)
EXAMPLE_DICT[level].append(this_example)
# import ipdb; ipdb.set_trace()
for level in levels:
os.makedirs(output_path + "level%d"%(level), exist_ok=True)
os.makedirs(input_backup_path + "level%d"%(level), exist_ok=True)
start_idx = 0
end_idx = 100
runned_term = 0
map_path = "maps/level%d/img/"%(level)
while True:
try:
curr_id = start_idx + runned_term
if curr_id >= end_idx:
break
img_input = PIL.Image.open(map_path + "%d.png"%(curr_id))
prompt_input_1 = '''
In this task, you will analyze a maze to determine if there is a hole in a specific position.
The following figure illustrates the appearances of the player, holes, lands, and the goal within the maze. You will need to focus on the appearance of the hole.
'''
prompt_input_2 = '''
Here is an example to illustrate how to analyze and answer the question:
'''
prompt_input_3 = '''
Example question: Is there a hole in row 3, column 3?
In this example:
- We check the position in row 3, column 3.
- According to the image, it is a land square. It does not contain a hole.
- Therefore, you will output "<Output> No".
Your output should be: "<Output> No" or "<Output> Yes", depending on whether there is a hole at the specified position.
'''
question_examples = []
answer_examples = []
image_examples = []
if in_context_example_num > 0:
example_indices = random.sample(range(8), in_context_example_num)
for example_index in example_indices:
this_example = EXAMPLE_DICT[level][example_index]
image_examples.append(this_example[0])
question_examples.append(this_example[1] + "\n")
answer_examples.append(this_example[2] + "\n\n")
prompt_input_4 = "\n\nNow you will analyze the following maze and answer the question: "
with open("maps/level%d/question/%d.txt"%(level, curr_id), "r") as f:
prompt_input_5 = f.read()
prompt_img_1 = PIL.Image.open('prompt-visual-images/system-figure-1.png')
prompt_img_2 = PIL.Image.open('prompt-visual-images/system-figure-2.png')
model_input_seq = [prompt_input_1, prompt_img_1, prompt_input_2, prompt_img_2, prompt_input_3]
if in_context_example_num > 0:
assert len(question_examples) == len(image_examples)
assert len(question_examples) == in_context_example_num
model_input_seq.append("## Example:\n")
for example_index in range(in_context_example_num):
model_input_seq.append(image_examples[example_index])
model_input_seq.append(question_examples[example_index])
model_input_seq.append(answer_examples[example_index])
model_input_seq += [prompt_input_4, img_input, prompt_input_5]
response = model.generate_content(model_input_seq)
with open(input_backup_path + "level%d/%d.txt"%(level, curr_id), "w") as f:
contents = ""
for input_prompt_index in range(len(model_input_seq)):
if type(model_input_seq[input_prompt_index]) == type("string"):
contents += model_input_seq[input_prompt_index]
f.write(contents)
with open(output_path + "level%d/%d.txt"%(level, curr_id), "w") as f:
f.write(response.text)
# import ipdb; ipdb.set_trace()
# pass
time.sleep(2)
runned_term += 1
except:
time.sleep(2)
pass
|