VILA/scripts/convert_sqa_to_llava.py

# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0

import json
import os

import fire
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)