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import json
import os
import fire
import re
from convert_sqa_to_llava_base_prompt import build_prompt_chatbot
def convert_to_llava(base_dir, split, prompt_format="QCM-LEA"):
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
split_problems = build_prompt_chatbot(problems, split_indices, prompt_format, use_caption=False, is_test=False)
target_format = []
for prob_id, (input, output) in split_problems.items():
if input.startswith("Question: "):
input = input.replace("Question: ", "")
if output.startswith("Answer: "):
output = output.replace("Answer: ", "")
raw_prob_data = problems[prob_id]
if raw_prob_data["image"] is None:
target_format.append(
{
"id": prob_id,
"conversations": [
{"from": "human", "value": f"{input}"},
{"from": "gpt", "value": f"{output}"},
],
}
)
else:
target_format.append(
{
"id": prob_id,
"image": os.path.join(prob_id, raw_prob_data["image"]),
"conversations": [
{"from": "human", "value": f"{input}\n<image>"},
{"from": "gpt", "value": f"{output}"},
],
}
)
print(f"Number of samples: {len(target_format)}")
with open(os.path.join(base_dir, f"llava_{split}_{prompt_format}.json"), "w") as f:
json.dump(target_format, f, indent=2)
def convert_to_jsonl(base_dir, split, prompt_format="QCM-LEPA"):
split_indices = json.load(open(os.path.join(base_dir, "pid_splits.json")))[split]
problems = json.load(open(os.path.join(base_dir, "problems.json")))
split_problems = build_prompt_chatbot(problems, split_indices, prompt_format, use_caption=False, is_test=False)
writer = open(os.path.join(base_dir, f"scienceqa_{split}_{prompt_format}.jsonl"), "w")
for prob_id, (input, output) in split_problems.items():
if input.startswith("Question: "):
input = input.replace("Question: ", "")
if output.startswith("Answer: "):
output = output.replace("Answer: ", "")
raw_prob_data = problems[prob_id]
if raw_prob_data["image"] is None:
data = {
"id": prob_id,
"instruction": f"{input}",
"output": f"{output}",
}
else:
data = {
"id": prob_id,
"image": os.path.join(prob_id, raw_prob_data["image"]),
"instruction": f"{input}\n<image>",
"output": f"{output}",
}
writer.write(json.dumps(data) + "\n")
writer.close()
def main(task, **kwargs):
globals()[task](**kwargs)
if __name__ == "__main__":
fire.Fire(main)
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