PIRM.py

# 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())
                }