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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""PASS dataset."""
import os
from datetime import datetime
import numpy as np
import pandas as pd
import datasets
_DESCRIPTION = """\
PASS (Pictures without humAns for Self-Supervision) is a large-scale dataset of 1,440,191 images that does not include any humans
and which can be used for high-quality pretraining while significantly reducing privacy concerns.
The PASS images are sourced from the YFCC-100M dataset.
"""
_CITATION = """\
@Article{asano21pass,
author = "Yuki M. Asano and Christian Rupprecht and Andrew Zisserman and Andrea Vedaldi",
title = "PASS: An ImageNet replacement for self-supervised pretraining without humans",
journal = "NeurIPS Track on Datasets and Benchmarks",
year = "2021"
}
"""
_HOMEPAGE = "https://www.robots.ox.ac.uk/~vgg/research/pass/"
_LICENSE = "Creative Commons Attribution 4.0 International"
_IMAGE_ARCHIVE_DOWNLOAD_URL_TEMPLATE = "https://zenodo.org/record/6615455/files/PASS.{idx}.tar?download=1"
_METADATA_DOWNLOAD_URL = "https://zenodo.org/record/6615455/files/pass_metadata.csv?download=1"
class PASS(datasets.GeneratorBasedBuilder):
"""PASS dataset."""
# 1.0.0 - v2 from https://github.com/yukimasano/PASS/blob/6226b456d23efa56b44e79648a9913e086d57335/version_history.txt
# 2.0.0 - v3 from https://github.com/yukimasano/PASS/blob/6226b456d23efa56b44e79648a9913e086d57335/version_history.txt
VERSION = datasets.Version("2.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"creator_username": datasets.Value("string"),
"hash": datasets.Value("string"),
"gps_latitude": datasets.Value("float32"),
"gps_longitude": datasets.Value("float32"),
"date_taken": datasets.Value("timestamp[us]"),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
metadata_file, *image_dirs = dl_manager.download(
[_METADATA_DOWNLOAD_URL] + [_IMAGE_ARCHIVE_DOWNLOAD_URL_TEMPLATE.format(idx=i) for i in range(10)]
)
metadata = pd.read_csv(metadata_file, encoding="utf-8")
metadata = metadata.replace(np.NaN, pd.NA).where(metadata.notnull(), None)
metadata = metadata.set_index("hash")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata": metadata,
"image_archives": [dl_manager.iter_archive(image_dir) for image_dir in image_dirs],
},
)
]
def _generate_examples(self, metadata, image_archives):
"""Yields examples."""
for image_archive in image_archives:
for path, file in image_archive:
img_hash = os.path.basename(path).split(".")[0]
img_meta = metadata.loc[img_hash]
yield img_hash, {
"image": {"path": path, "bytes": file.read()},
"creator_username": img_meta["unickname"],
"hash": img_hash,
"gps_latitude": img_meta["latitude"],
"gps_longitude": img_meta["longitude"],
"date_taken": datetime.strptime(img_meta["datetaken"], "%Y-%m-%d %H:%M:%S.%f")
if img_meta["datetaken"] is not None
else None,
}
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