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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 textcaps val-set in TFDS structure.
It's small data, so simple to run locally. First, copy the data to local disk:
mkdir -p /tmp/data/textcaps
cd /tmp/data/textcaps
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_train.json
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_val.json
curl -O https://dl.fbaipublicfiles.com/textvqa/data/textcaps/TextCaps_0.1_test.json
curl -O https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
curl -O https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip
unzip train_val_images.zip
rm train_val_images.zip
unzip test_images.zip
rm test_images.zip
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=textcaps
Example to load:
import tensorflow_datasets as tfds
dataset = tfds.load('text_caps', split='val', data_dir='/tmp/tfds')
"""
import collections
import json
import os
from absl import logging
import numpy as np
import tensorflow_datasets as tfds
_DESCRIPTION = """TextCaps dataset."""
# pylint: disable=line-too-long
_CITATION = (
'@inproceedings{sidorov2019textcaps,'
'title={TextCaps: a Dataset for Image Captioningwith Reading Comprehension},'
'author={Sidorov, Oleksii and Hu, Ronghang and Rohrbach, Marcus and Singh, Amanpreet},'
'journal={European Conference on Computer Vision},'
'year={2020}}')
# pylint: enable=line-too-long
# When running locally (recommended), copy files as above an use these:
_FILEPATH = '/tmp/data/textcaps/'
_TRAIN_FILES = '/tmp/data/textcaps/TextCaps_0.1_train.json'
_VAL_FILES = '/tmp/data/textcaps/TextCaps_0.1_val.json'
_TEST_FILES = '/tmp/data/textcaps/TextCaps_0.1_test.json'
class TextCaps(tfds.core.GeneratorBasedBuilder):
"""DatasetBuilder for TextCaps dataset."""
VERSION = tfds.core.Version('1.0.0')
RELEASE_NOTES = {
'1.0.0': 'Initial release.',
}
def _info(self) -> tfds.core.DatasetInfo:
"""Returns the dataset metadata.
(tfds.core.DatasetInfo object)
These are the features of your dataset like images, labels, etc.
"""
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds.features.FeaturesDict({
'image/id': tfds.features.Text(),
'image_filepath': tfds.features.Text(),
'image': tfds.features.Image(encoding_format='jpeg'),
'texts': tfds.features.Sequence(tfds.features.Text()),
}),
supervised_keys=None, # Set to `None` to disable
homepage='https://textvqa.org/textcaps/',
citation=_CITATION,
)
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
def group_by_id(data, image_dir):
id_to_example = collections.defaultdict(list)
for ex in data:
id_to_example[ex['image_id']].append(ex)
for k, exs in id_to_example.items():
image_ids, image_names, texts = [], [], []
for ex in exs:
image_ids.append(ex['image_id'])
image_names.append(ex['image_name'])
if ex.get('caption_str'):
texts.append(ex.get('caption_str'))
assert len(set(image_ids)) == 1
assert len(set(image_names)) == 1
image_filepath = os.path.join(
_FILEPATH, image_dir, str(image_names[0])+'.jpg')
id_to_example[k] = {
'image/id': image_ids[0],
'image_filepath': image_filepath,
'image': image_filepath,
'texts': texts,
}
return id_to_example
# Returns the Dict[split names, Iterator[Key, Example]]
with open(_TRAIN_FILES) as f:
train_data = group_by_id(json.load(f)['data'], 'train_images')
with open(_VAL_FILES) as f:
val_data = group_by_id(json.load(f)['data'], 'train_images')
with open(_TEST_FILES) as f:
test_data = group_by_id(json.load(f)['data'], 'test_images')
return {
'train': self._generate_examples(train_data),
'val': self._generate_examples(val_data),
'test': self._generate_examples(test_data),
}
def _generate_examples(self, data):
"""Generate a tf.Example object.
This contains the image, objects, attributes, regions and relationships.
Args:
data: a dictionary with the image/id.
Yields:
(key, example) tuples from dataset. The example has format specified in
the above DatasetInfo.
"""
for k, v in data.items():
yield k, v
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