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# Copyright 2024 Big Vision Authors.
#
# 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.
# pylint: disable=line-too-long
r"""Implements ScienceQA train/val/test-set in TFDS structure.
First, download the science QA dataset from their website https://scienceqa.github.io/#download
- mkdir -p /tmp/data/ScienceQA_DATA
- From Google Drive: https://drive.google.com/corp/drive/folders/1w8imCXWYn2LxajmGeGH_g5DaL2rabHev
Then, run conversion locally (make sure to install tensorflow-datasets for the `tfds` util):
- cd big_vision/datasets
- env TFDS_DATA_DIR=/tmp/tfds tfds build --datasets=science_qa
Example to load:
import tensorflow_datasets as tfds
dataset = tfds.load(
'science_qa', split='train',
data_dir='/tmp/tfds')
"""
import json
import os
import numpy as np
import tensorflow_datasets as tfds
_DESCRIPTION = """Sci QA test-set."""
# pylint: disable=line-too-long
_CITATION = """
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
"""
# pylint: enable=line-too-long
# When running locally (recommended), copy files as above an use these:
_SCIQA_PATH = '/tmp/data/ScienceQA_DATA/'
# _IMAGE_COCO_PATH = '/tmp/data/val2014'
_ALPHABETS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
class ScienceQA(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for ScienceQA dataset."""
VERSION = tfds.core.Version('1.0.0')
RELEASE_NOTES = {'1.0.0': 'First release.'}
def _info(self):
"""Returns the metadata."""
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds.features.FeaturesDict({
'question': tfds.features.Text(),
'choices': tfds.features.Sequence(tfds.features.Text()),
'answer': tfds.features.Scalar(np.int32),
'hint': tfds.features.Text(),
'task': tfds.features.Text(),
'grade': tfds.features.Text(),
'subject': tfds.features.Text(),
'topic': tfds.features.Text(),
'category': tfds.features.Text(),
'skill': tfds.features.Text(),
'lecture': tfds.features.Text(),
'solution': tfds.features.Text(),
'image': tfds.features.Image(encoding_format='png'),
'indexed_choices': tfds.features.Text(),
'indexed_answer': tfds.features.Text(),
}),
supervised_keys=None,
homepage='https://github.com/lupantech/ScienceQA/tree/main',
citation=_CITATION,
)
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
return {
split: self._generate_examples(split)
for split in ('train', 'test', 'val')
}
def _generate_examples(self, split):
"""Yields (key, example) tuples from test set."""
annot_fname = os.path.join(_SCIQA_PATH, 'problems.json')
with open(annot_fname, 'r') as f:
data = json.loads(f.read())
for k, v in data.items():
if v['split'] == split: # "split":"train"
image = v['image']
# Science QA contains the example without image as well. As this
# conversion is for VQA tasks, we dropped the examples without Image.
# TODO: Include the examples without image, and udpate the
# downstream pipeline to skip the examples without image, instead of
# doing it at pre-processing.
if image:
image = os.path.join(f'{_SCIQA_PATH}/{split}/{k}/', f'{image}')
else:
# image = None
continue
question = v['question']
choices = v['choices']
answer = v['answer']
hint = v['hint']
if not hint:
hint = 'N/A' # align with orignal github implementation
task = v['task']
grade = v['grade']
subject = v['subject']
topic = v['topic']
category = v['category']
skill = v['skill']
lecture = v['lecture']
solution = v['solution']
split = v['split']
indexed_choices = ', '.join(
f'({_ALPHABETS[i]}) {c}' for i, c in enumerate(choices)
)
indexed_answer = _ALPHABETS[int(answer)]
yield int(k), {
'question': question,
'choices': choices,
'answer': answer,
'hint': hint,
'task': task,
'grade': grade,
'subject': subject,
'topic': topic,
'category': category,
'skill': skill,
'lecture': lecture,
'solution': solution,
'image': image,
'indexed_choices': indexed_choices,
'indexed_answer': indexed_answer,
}
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