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# coding=utf-8
# 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.
"""High-Level dataset."""
import json
from pathlib import Path
import datasets
_CITATION = """\
@inproceedings{Cafagna2023HLDG,
title={HL Dataset: Grounding High-Level Linguistic Concepts in Vision},
author={Michele Cafagna and Kees van Deemter and Albert Gatt},
year={2023}
}
"""
_DESCRIPTION = """\
High-level Dataset
"""
# github link
_HOMEPAGE = "https://github.com/michelecafagna26/HL-dataset"
_LICENSE = "Apache 2.0"
_IMG = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/images.tar.gz"
_TRAIN = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/train.jsonl"
_TEST = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/test.jsonl"
class HL(datasets.GeneratorBasedBuilder):
"""High Level Dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"image": datasets.Image(),
"scene": datasets.Sequence(datasets.Value("string")),
"action": datasets.Sequence(datasets.Value("string")),
"rationale": datasets.Sequence(datasets.Value("string")),
"object": datasets.Sequence(datasets.Value("string")),
"confidence": {
"scene": datasets.Sequence(datasets.Value("float32")),
"action": datasets.Sequence(datasets.Value("float32")),
"rationale": datasets.Sequence(datasets.Value("float32")),
},
"purity": {
"scene": datasets.Sequence(datasets.Value("float32")),
"action": datasets.Sequence(datasets.Value("float32")),
"rationale": datasets.Sequence(datasets.Value("float32")),
},
"diversity": {
"scene": datasets.Value("float32"),
"action": datasets.Value("float32"),
"rationale": datasets.Value("float32"),
},
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
image_files = dl_manager.download(_IMG)
annotation_files = dl_manager.download_and_extract([_TRAIN, _TEST])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file_path": annotation_files[0],
"images": dl_manager.iter_archive(image_files),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file_path": annotation_files[1],
"images": dl_manager.iter_archive(image_files),
},
),
]
def _generate_examples(self, annotation_file_path, images):
idx = 0
#assert Path(annotation_file_path).suffix == ".jsonl"
with open(annotation_file_path, "r") as fp:
metadata = {json.loads(item)['file_name']: json.loads(item) for item in fp}
# This loop relies on the ordering of the files in the archive:
# Annotation files come first, then the images.
for img_file_path, img_obj in images:
file_name = Path(img_file_path).name
if file_name in metadata:
yield idx, {
"file_name": file_name,
"image": {"path": img_file_path, "bytes": img_obj.read()},
"scene": metadata[file_name]['captions']['scene'],
"action": metadata[file_name]['captions']['action'],
"rationale": metadata[file_name]['captions']['rationale'],
"object": metadata[file_name]['captions']['object'],
"confidence": metadata[file_name]['confidence'],
"purity": metadata[file_name]['purity'],
"diversity": metadata[file_name]['diversity'],
}
idx += 1 |