jhtdb.py

"""TODO: Add a description here."""

import csv
import json
import os
import numpy as np
from pathlib import Path
import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
    "small_50": {
        "train": (
            "datasets/jhtdb/small_50/metadata_train.csv",
            "datasets/jhtdb/small_50/train.zip",
        ),
        "val": (
            "datasets/jhtdb/small_50/metadata_val.csv",
            "datasets/jhtdb/small_50/val.zip",
        ),
        "test": (
            "datasets/jhtdb/small_50/metadata_test.csv",
            "datasets/jhtdb/small_50/test.zip",
        ),
    }
}


class JHTDB(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="small_50", version=VERSION, description=""),
    ]

    DEFAULT_CONFIG_NAME = "small_50"

    def _info(self):
        if self.config.name.startswith("small"):
            features = datasets.Features(
                {
                    "lrs": datasets.Sequence(
                        datasets.Array4D(shape=(3, 4, 4, 4), dtype="float32"),
                    ),
                    "hr": datasets.Array4D(shape=(3, 16, 16, 16), dtype="float32"),
                }
            )
        elif self.config.name.startswith("large"):
            features = datasets.Features(
                {
                    "lrs": datasets.Sequence(
                        datasets.Array4D(shape=(3, 16, 16, 16), dtype="float32"),
                    ),
                    "hr": datasets.Array4D(shape=(3, 64, 64, 64), dtype="float32"),
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        named_splits = {
            "train": datasets.Split.TRAIN,
            "val": datasets.Split.VALIDATION,
            "test": datasets.Split.TEST,
        }
        return [
            datasets.SplitGenerator(
                name=named_splits[split],
                gen_kwargs={
                    "metadata_path": Path(metadata_path),
                    "data_path": Path(data_path),
                },
            )
            for split, (metadata_path, data_path) in data_dir.items()
        ]

    def _generate_examples(self, metadata_path: Path, data_path: Path):
        with open(metadata_path) as f:
            reader = csv.DictReader(f)
            for key, data in enumerate(reader):
                yield key, {
                    "lrs": [
                        np.load(data_path / Path(p).name)
                        for p in json.loads(data["lr_paths"])
                    ],
                    "hr": np.load(data_path / Path(data["hr_path"]).name),
                }