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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
"""Generates xGQA in a TFDS-ready structure.
First, download the data:
mkdir -p /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_bn.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_de.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_en.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_id.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_ko.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_pt.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_ru.json -P /tmp/data/xgqa/annotations
wget https://raw.githubusercontent.com/e-bug/iglue/main/datasets/xGQA/annotations/zero_shot/testdev_balanced_questions_zh.json -P /tmp/data/xgqa/annotations
wget https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip -P /tmp/data/xgqa/
unzip /tmp/data/xgqa/images.zip -d /tmp/data/xgqa/
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=xgqa
Example to load:
import tensorflow_datasets as tfds
dataset = tfds.load(
'xgqa', split='test_zs_en',
data_dir='/tmp/tfds')
"""
import json
import os
import tensorflow_datasets as tfds
_DESCRIPTION = """xGQA (uses GQA images)."""
# pylint: disable=line-too-long
_CITATION = (
'@inproceedings{pfeiffer-etal-2022-xgqa,'
'title = "x{GQA}: Cross-Lingual Visual Question Answering",'
'author = "Pfeiffer, Jonas and'
' Geigle, Gregor and'
' Kamath, Aishwarya and'
' Steitz, Jan-Martin and'
' Roth, Stefan and'
' Vuli{\'c}, Ivan and'
' Gurevych, Iryna",'
'booktitle = "Findings of the Association for Computational Linguistics: '
'ACL 2022",'
'month = may,'
'year = "2022",'
'address = "Dublin, Ireland",'
'publisher = "Association for Computational Linguistics",'
'url = "https://aclanthology.org/2022.findings-acl.196",'
'doi = "10.18653/v1/2022.findings-acl.196",'
'pages = "2497--2511",'
'}'
)
# pylint: enable=line-too-long
# When running locally (recommended), copy files as above an use these:
_DATA_PATH = '/tmp/data/xgqa/'
_IMAGE_PATH = '/tmp/data/xgqa/images/'
LANGUAGES = frozenset(['bn', 'de', 'en', 'id', 'ko', 'pt', 'ru', 'zh'])
class XGQA(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for XGQA 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({
'example_id': tfds.features.Text(),
'image/id': tfds.features.Text(),
'image': tfds.features.Image(encoding_format='jpeg'),
'question': tfds.features.Text(),
'answer': tfds.features.Text(),
}),
supervised_keys=None,
homepage='https://github.com/adapter-hub/xGQA',
citation=_CITATION,
)
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
d = dict()
for l in LANGUAGES:
d.update({
f'test_zs_{l}': self._generate_examples('test', 'zero_shot', l),
f'test_fs_{l}': self._generate_examples('test', 'few_shot', l),
f'dev_fs_{l}': self._generate_examples('test', 'few_shot', l),
f'train_fs1_{l}': self._generate_examples('train_1', 'few_shot', l),
f'train_fs5_{l}': self._generate_examples('train_5', 'few_shot', l),
f'train_fs10_{l}': self._generate_examples('train_10', 'few_shot', l),
f'train_fs20_{l}': self._generate_examples('train_20', 'few_shot', l),
f'train_fs25_{l}': self._generate_examples('train_25', 'few_shot', l),
f'train_fs48_{l}': self._generate_examples('train_48', 'few_shot', l),
})
return d
def _generate_examples(self, split, num_shots, lang):
"""Yields (key, example) tuples."""
# Loads the questions for each image.
if num_shots == 'few_shot':
file_path = os.path.join(_DATA_PATH, 'annotations', 'few_shot', lang,
f'{split}.json')
elif num_shots == 'zero_shot':
file_path = os.path.join(_DATA_PATH, 'annotations', 'zero_shot',
f'testdev_balanced_questions_{lang}.json')
else:
raise ValueError(f'Unknown num_shots: {num_shots}')
with open(file_path, 'r') as f:
entries = json.load(f)
# Make one entry per question-answer pair.
for question_id, question_data in entries.items():
example_id = f'{question_id}_{lang}'
yield example_id, {
'example_id': example_id,
'image/id': question_data['imageId'],
'image': os.path.join(_IMAGE_PATH, f'{question_data["imageId"]}.jpg'),
'question': question_data['question'],
'answer': question_data['answer'],
}
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