GBI-16-2D-Legacy.py

import os
import random
from glob import glob
import json
from huggingface_hub import hf_hub_download


from astropy.io import fits
import datasets
from datasets import DownloadManager
from fsspec.core import url_to_fs

_DESCRIPTION = (
"GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression "
"dataset assembled by Maireles-González et al. (Publications of the "
"Astronomical Society of the Pacific, 135:094502, 2023 September; doi: "
"[https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/"
"acf6e0)). It contains 226 FITS images from 5 different ground-based "
"telescope/cameras with a varying amount of entropy per image."
)

_HOMEPAGE = "https://google.github.io/AstroCompress"

_LICENSE = "CC BY 4.0"

_URL = "https://huggingface.co/datasets/AstroCompress/GBI-16-2D-Legacy/resolve/main/"

_URLS = {
    "tiny": {
        "train": "./splits/tiny_train.jsonl",
        "test": "./splits/tiny_test.jsonl",
    },
    "full": {
        "train": "./splits/full_train.jsonl",
        "test": "./splits/full_test.jsonl",
    }
}

_REPO_ID = "AstroCompress/GBI-16-2D-Legacy"

class GBI_16_2D_Legacy(datasets.GeneratorBasedBuilder):
    """GBI-16-2D-Legacy Dataset"""

    VERSION = datasets.Version("1.0.1")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="tiny",
            version=VERSION,
            description="A small subset of the data, to test downsteam workflows.",
        ),
        datasets.BuilderConfig(
            name="full",
            version=VERSION,
            description="The full dataset",
        ),
    ]

    DEFAULT_CONFIG_NAME = "tiny"

    def __init__(self, **kwargs):
        super().__init__(version=self.VERSION, **kwargs)

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    # Images are variable size across the dataset
                    # so use the Image type here, returning as
                    # numpy uint16
                    "image": datasets.Image(decode=True, mode="I;16"),
                    "telescope": datasets.Value("string"),
                    "image_id": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation="TBD",
        )

    def _split_generators(self, dl_manager: DownloadManager):

        ret = []
        base_path = dl_manager._base_path
        locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) 
        _, path = url_to_fs(base_path)

        for split in ["train", "test"]:
            if locally_run:
                split_file_location = os.path.normpath(os.path.join(path, _URLS[self.config.name][split]))
                split_file = dl_manager.download_and_extract(split_file_location)
            else:
                split_file = hf_hub_download(repo_id=_REPO_ID, filename=_URLS[self.config.name][split], repo_type="dataset")
            with open(split_file, encoding="utf-8") as f:
                data_filenames = []
                data_metadata = []
                for line in f:
                    item = json.loads(line)
                    data_filenames.append(item["image"])
                    data_metadata.append({"telescope": item["telescope"],
                                          "image_id": item["image_id"]})
                if locally_run:
                    data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames]
                    data_files = [dl_manager.download(data_url) for data_url in data_urls]
                else:
                    data_urls = data_filenames
                    data_files = [hf_hub_download(repo_id=_REPO_ID, filename=data_url, repo_type="dataset") for data_url in data_urls]
            ret.append(
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,
                    gen_kwargs={"filepaths": data_files,
                                "split_file": split_file,
                                "split": split, 
                                "data_metadata": data_metadata},
                ),
            )
        return ret

    def _generate_examples(self, filepaths, split_file, split, data_metadata):
        """Generate GBI-16-2D-Legacy examples"""

        for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
            task_instance_key = f"{self.config.name}-{split}-{idx}"
            with fits.open(filepath, memmap=False) as hdul:
                # this data is natively formatted like (1, 4200, 2154)
                # just use the 2D image
                image_data = hdul[0].data[0,:,:].tolist()
                yield task_instance_key, {**{"image": image_data}, **item}