Datasets:
Tasks:
Other
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 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. | |
"""PIRM dataset: An validation and test dataset for the image super resolution task""" | |
import datasets | |
from pathlib import Path | |
_CITATION = """ | |
@misc{shoeiby2019pirm2018, | |
title={PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study}, | |
author={Mehrdad Shoeiby and Antonio Robles-Kelly and Ran Wei and Radu Timofte}, | |
year={2019}, | |
eprint={1904.00540}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CV} | |
} | |
""" | |
_DESCRIPTION = """ | |
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. | |
These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. | |
Images vary in size, and are typically ~300K pixels in resolution. | |
This dataset was first used for evaluating the perceptual quality of super-resolution algorithms in The 2018 PIRM | |
challenge on Perceptual Super-resolution, in conjunction with ECCV 2018. | |
""" | |
_HOMEPAGE = "https://github.com/roimehrez/PIRM2018" | |
_LICENSE = "cc-by-nc-sa-4.0" | |
_DL_URL = "https://huggingface.co/datasets/eugenesiow/PIRM/resolve/main/data/" | |
_DEFAULT_CONFIG = "bicubic_x2" | |
_DATA_OPTIONS = { | |
"bicubic_x2": { | |
"hr_test": _DL_URL + "PIRM_test_HR.tar.gz", | |
"lr_test": _DL_URL + "PIRM_test_LR_x2.tar.gz", | |
"hr_valid": _DL_URL + "PIRM_valid_HR.tar.gz", | |
"lr_valid": _DL_URL + "PIRM_valid_LR_x2.tar.gz", | |
}, | |
"bicubic_x3": { | |
"hr_test": _DL_URL + "PIRM_test_HR.tar.gz", | |
"lr_test": _DL_URL + "PIRM_test_LR_x3.tar.gz", | |
"hr_valid": _DL_URL + "PIRM_valid_HR.tar.gz", | |
"lr_valid": _DL_URL + "PIRM_valid_LR_x3.tar.gz", | |
}, | |
"bicubic_x4": { | |
"hr_test": _DL_URL + "PIRM_test_HR.tar.gz", | |
"lr_test": _DL_URL + "PIRM_test_LR_x4.tar.gz", | |
"hr_valid": _DL_URL + "PIRM_valid_HR.tar.gz", | |
"lr_valid": _DL_URL + "PIRM_valid_LR_x4.tar.gz", | |
}, | |
"unknown_x4": { | |
"hr_test": _DL_URL + "PIRM_test_HR.tar.gz", | |
"lr_test": _DL_URL + "PIRM_test_LR_unknown_x4.tar.gz", | |
"hr_valid": _DL_URL + "PIRM_valid_HR.tar.gz", | |
"lr_valid": _DL_URL + "PIRM_valid_LR_unknown_x4.tar.gz", | |
} | |
} | |
class PirmConfig(datasets.BuilderConfig): | |
"""BuilderConfig for PIRM.""" | |
def __init__( | |
self, | |
name, | |
download_urls, | |
**kwargs, | |
): | |
if name not in _DATA_OPTIONS: | |
raise ValueError("data must be one of %s" % _DATA_OPTIONS) | |
super(PirmConfig, self).__init__(name=name, version=datasets.Version("1.0.0"), **kwargs) | |
self.download_urls = download_urls | |
class Pirm(datasets.GeneratorBasedBuilder): | |
"""PIRM dataset for single image super resolution test and validation.""" | |
BUILDER_CONFIGS = [ | |
PirmConfig( | |
name=key, | |
download_urls=values, | |
) for key, values in _DATA_OPTIONS.items() | |
] | |
DEFAULT_CONFIG_NAME = _DEFAULT_CONFIG | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"hr": datasets.Value("string"), | |
"lr": datasets.Value("string"), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
extracted_paths = dl_manager.download_and_extract( | |
self.config.download_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"lr_path": extracted_paths["lr_valid"], | |
"hr_path": str(Path(extracted_paths["hr_valid"]) / 'PIRM_valid_HR') | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"lr_path": extracted_paths["lr_test"], | |
"hr_path": str(Path(extracted_paths["hr_test"]) / 'PIRM_test_HR') | |
}, | |
) | |
] | |
def _generate_examples( | |
self, hr_path, lr_path | |
): | |
""" Yields examples as (key, example) tuples. """ | |
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
# The `key` is here for legacy reason (tfds) and is not important in itself. | |
extensions = {'.png'} | |
for file_path in sorted(Path(lr_path).glob("**/*")): | |
if file_path.suffix in extensions: | |
file_path_str = str(file_path.as_posix()) | |
yield file_path_str, { | |
'lr': file_path_str, | |
'hr': str((Path(hr_path) / file_path.name).as_posix()) | |
} | |