File size: 3,992 Bytes
16e1eec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
"""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),
}
|