def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Download the data and define splits."""
download_server = environ.get('ROAD_OBSTACLE_URL')
if download_server is None:
raise RuntimeError('Please specify server URL as ROAD_OBSTACLE_URL env variable.')
v = self.builder_config.version
download_url = download_server + "/dataset_RoadObstacle_{v}.zip".format(v=v)
download_dir = dl_manager.download_and_extract(download_url)
data_dir = Path(download_dir) / 'dataset_RoadObstacle'
splits = json.loads((data_dir / 'splits.json').read_text())
make_split_entry = lambda name, key: SplitGenerator(
name=name,
gen_kwargs = dict(data_dir=str(data_dir), split=key)
)
return [
make_split_entry(tfds.Split.TEST, 'full')
] + [
make_split_entry(k, k)
for k in sorted(splits.keys())
]
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(galsim_cosmos): Downloads the data and defines the splits
path = dl_manager.download_and_extract('https://zenodo.org/record/3242143/files/COSMOS_25.2_training_sample.tar.gz')
cat = galsim.COSMOSCatalog(dir= path / 'COSMOS_25.2_training_sample')
# TODO(galsim_cosmos): Returns the Dict[split names, Iterator[Key, Example]]
return {tfds.Split.TRAIN: self._generate_examples(cat)}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
if self.builder_config.v == 1:
splits = dl_manager.download({
"train":
"https://raw.githubusercontent.com/korquad/korquad.github.io/918f5229639203d741045fdcdbb7462c602887da/dataset/KorQuAD_v1.0_train.json",
"dev":
"https://raw.githubusercontent.com/korquad/korquad.github.io/918f5229639203d741045fdcdbb7462c602887da/dataset/KorQuAD_v1.0_dev.json",
})
return {
"train": self._generate_examples(splits["train"]),
"dev": self._generate_examples(splits["dev"]),
}
splits = dl_manager.download_and_extract({
"train": [
f"https://raw.githubusercontent.com/korquad/korquad.github.io/918f5229639203d741045fdcdbb7462c602887da/dataset/KorQuAD_2.1/train/KorQuAD_2.1_train_{i:02}.zip"
for i in range(13)
],
"dev": [
f"https://raw.githubusercontent.com/korquad/korquad.github.io/918f5229639203d741045fdcdbb7462c602887da/dataset/KorQuAD_2.1/dev/KorQuAD_2.1_dev_{i:02}.zip"
for i in range(2)
],
})
return {
"train":
itertools.chain.from_iterable(
[self._generate_examples(i) for i in splits["train"]]),
"dev":
itertools.chain.from_iterable(
[self._generate_examples(i) for i in splits["dev"]]),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
data_index_path = dl_manager.download(_INDEX_URL)
# Download videos and update paths
with GFile(data_index_path, "r") as f:
data = json.load(f)
if self._builder_config.include_video:
paths = self._download_videos(data, dl_manager)
for datum in data:
for instance in datum["instances"]:
instance["video"] = paths[
instance["video_id"]] if instance[
"video_id"] in paths else None
if self._builder_config.include_pose == "openpose":
pose_path = dl_manager.download_and_extract(
_POSE_URLS[self._builder_config.include_pose])
else:
pose_path = None
return {
"train": self._generate_examples(data, pose_path, "train"),
"validation": self._generate_examples(data, pose_path, "val"),
"test": self._generate_examples(data, pose_path, "test"),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Return SplitGenerators."""
paths = dl_manager.download_and_extract({
'train-1': 'https://archive.physionet.org/users/shared/challenge-2019/training_setA.zip', # noqa: E501
'train-2': 'https://archive.physionet.org/users/shared/challenge-2019/training_setB.zip' # noqa: E501
})
train_1_path = os.path.join(paths['train-1'], 'training')
train_2_path = os.path.join(paths['train-2'], 'training_setB')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
'data_paths': [train_1_path, train_2_path],
'listfile': os.path.join(RESOURCES, 'train_listfile.csv')
}
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
gen_kwargs={
'data_paths': [train_1_path, train_2_path],
'listfile': os.path.join(RESOURCES, 'val_listfile.csv')
}
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
gen_kwargs={
'data_paths': [train_1_path, train_2_path],
'listfile': os.path.join(RESOURCES, 'test_listfile.csv')
}
)
]
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(f"{DGL_URL}reddit.zip")
return {
"full": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Download the data and define splits."""
# Download and Extract data.zip
extracted_path = dl_manager.download_and_extract(
"http://www.patreo.dcc.ufmg.br/wp-content/uploads/2017/11/brazilian_coffee_dataset.zip"
)
# dl_manager returns pathlib-like objects with `path.read_text()`,
# `path.iterdir()`,...
return {
'fold1':
self._generate_examples(path=extracted_path /
'brazilian_coffee_scenes/fold1'),
'fold2':
self._generate_examples(path=extracted_path /
'brazilian_coffee_scenes/fold2'),
'fold3':
self._generate_examples(path=extracted_path /
'brazilian_coffee_scenes/fold3'),
'fold4':
self._generate_examples(path=extracted_path /
'brazilian_coffee_scenes/fold4'),
'fold5':
self._generate_examples(path=extracted_path /
'brazilian_coffee_scenes/fold5'),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
name = self.builder_config.name
path = dl_manager.download_and_extract(
"https://github.com/kaize0409/GCN_AnomalyDetection/raw/master/gae/data/"
f"{name}.mat")
return {"train": self._generate_examples(path)}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
url = f"http://snap.stanford.edu/graphsage/{self.builder_config.name}.zip"
path = dl_manager.download_and_extract(url)
return {
"full": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(
f"{DGL_URL}{self._URL_NAME}.zip")
return {
"full": self._generate_examples(data_dir),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(
f"https://data.dgl.ai/dataset/{self.builder_config.name}.tgz"
)
return {
"full": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(
"https://snap.stanford.edu/data/soc-LiveJournal1.txt.gz")
return {
"full": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(tensorflowdb): Downloads the data and defines the splits
path = dl_manager.download_and_extract('https://github.com/drawwithai/Dataset/raw/main/FormatedImages.zip')
# TODO(tensorflowdb): Returns the Dict[split names, Iterator[Key, Example]]
return {
'train': self._generate_examples(path / "FormatedImages"),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(SVBRDF): Downloads the data and defines the splits
path = dl_manager.download_and_extract('https://todo-data-url')
# TODO(SVBRDF): Returns the Dict[split names, Iterator[Key, Example]]
return {
'train': self._generate_examples(path / 'train_imgs'),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
path = dl_manager.download_and_extract(
"https://github.com/shchur/gnn-benchmark/raw/master/data/npz/"
f"{self.builder_config.url_name}.npz"
)
return {
"full": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(hi_en): Downloads the data and defines the splits
train = dl_manager.download_and_extract(
'https://storage.googleapis.com/ai4b-anuvaad-nmt/test-runs/train.zip'
)
val = dl_manager.download_and_extract(
'https://storage.googleapis.com/ai4b-anuvaad-nmt/test-runs/validation.zip'
)
# TODO(hi_en): Returns the Dict[split names, Iterator[Key, Example]]
return {
'train':
self._generate_examples(source=train / 'train.hi',
target=train / 'train.en'),
'validation':
self._generate_examples(source=train / 'validation.hi',
target=train / 'validation.en')
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(
{"data": "http://sami.haija.org/graph/datasets.tgz"})["data"]
return {
"full":
self._generate_examples(path / "datasets" /
self.builder_config.name),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(OneLine45): Downloads the data and defines the splits
path = dl_manager.download_and_extract(
'https://github.com/drawwithai/Dataset/raw/main/OneLine_45/OneLine_45.zip'
)
# TODO(OneLine45): Returns the Dict[split names, Iterator[Key, Example]]
return {
'train': self._generate_examples(path / 'OneLine_45-train'),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
paths = dl_manager.download_and_extract(_URLS)
return [
tfds.core.SplitGenerator(
name=patch_size,
gen_kwargs={
"path": os.path.join(paths, "test"),
"size": patch_size
},
) for patch_size in ["large", "medium", "small"]
]
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
splits = dl_manager.download_and_extract(
{
"train": "https://raw.githubusercontent.com/KLUE-benchmark/KLUE/ab22cd5cfdd6b527a9a4e2d177f9dacb85ddde2c/klue_benchmark/klue-dp-v1/klue-dp-v1_train.tsv",
"dev": "https://raw.githubusercontent.com/KLUE-benchmark/KLUE/ab22cd5cfdd6b527a9a4e2d177f9dacb85ddde2c/klue_benchmark/klue-dp-v1/klue-dp-v1_dev.tsv",
}
)
return {
"train": self._generate_examples(splits["train"]),
"dev": self._generate_examples(splits["dev"]),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
filename_map = {
False: "sae4k_v1.txt",
True: "sae4k_v2.txt", # augmented
}
path = dl_manager.download_and_extract(
f"https://raw.githubusercontent.com/warnikchow/sae4k/a3a7a4510ea010d210956ad50e38a61c7c838b0f/data/{filename_map[self.builder_config.augmented]}"
)
return {
"train": self._generate_examples(path),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(self.builder_config.data_url)
if self.builder_config.manual_split is not None:
path = _update_split(path, self.builder_config.manual_split)
split_fn = self.builder_config.manual_split['split']
return {
k: self._generate_examples(v, split_fn[k])
for k, v in path.items()
}
# TODO(kor_corpora): Returns the Dict[split names, Iterator[Key, Example]]
return {k: self._generate_examples(v) for k, v in path.items()}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
name = self.builder_config.name
path = dl_manager.download_and_extract(
f"https://zenodo.org/record/3689089/files/botnet_{name}.tar.gz")
paths = {
"split_path": path / f"{name}_split_idx.pkl",
"data_path": path / f"{name}_raw.hdf5",
}
return {
tfds.core.Split.TRAIN: self._generate_examples("train", **paths),
tfds.core.Split.VALIDATION:
self._generate_examples("val", **paths),
tfds.core.Split.TEST: self._generate_examples("test", **paths),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
files = dl_manager.download_and_extract(
{
"train": "https://raw.githubusercontent.com/KLUE-benchmark/KLUE/ab22cd5cfdd6b527a9a4e2d177f9dacb85ddde2c/klue_benchmark/klue-re-v1/klue-re-v1_train.json",
"dev": "https://raw.githubusercontent.com/KLUE-benchmark/KLUE/ab22cd5cfdd6b527a9a4e2d177f9dacb85ddde2c/klue_benchmark/klue-re-v1/klue-re-v1_dev.json",
"relation_list": "https://raw.githubusercontent.com/KLUE-benchmark/KLUE/ab22cd5cfdd6b527a9a4e2d177f9dacb85ddde2c/klue_benchmark/klue-re-v1/relation_list.json",
}
)
with files["relation_list"].open() as f:
relations = json.load(f)["relations"]
return {
"train": self._generate_examples(files["train"], relations=relations),
"dev": self._generate_examples(files["dev"], relations=relations),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = dl_manager.download_and_extract(_DL_URL)
splits = []
for subdir, split in zip(
["train", "val", "test"],
[tfds.Split.TRAIN, tfds.Split.VALIDATION, tfds.Split.TEST],
):
splits.append(
tfds.core.SplitGenerator(
name=split,
gen_kwargs={
"path": os.path.join(path, "data_64x64", subdir)
},
))
return splits
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
urls = [
_VIDEO_ANNOTATIONS_URL
if self._builder_config.include_video else _ANNOTATIONS_URL
]
if self._builder_config.include_pose is not None:
urls.append(_POSE_URLS[self._builder_config.include_pose])
downloads = dl_manager.download_and_extract(urls)
annotations_path = path.join(downloads[0], "PHOENIX-2014-T-release-v3",
"PHOENIX-2014-T")
if self._builder_config.include_pose == "holistic":
pose_path = path.join(downloads[1], "holistic")
else:
pose_path = None
return [
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
gen_kwargs={
"annotations_path": annotations_path,
"pose_path": pose_path,
"split": "dev"
},
),
tfds.core.SplitGenerator(
name=tfds.Split.TEST,
gen_kwargs={
"annotations_path": annotations_path,
"pose_path": pose_path,
"split": "test"
},
),
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
"annotations_path": annotations_path,
"pose_path": pose_path,
"split": "train"
},
),
]
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path_kv = {k:dl_manager.download_and_extract(v) for k, v in self.builder_config.data_url.items()}
if not self.builder_config.name.startswith("v1.0"):
for k, v in path_kv.items():
file_names = []
for vv in v:
file_names.extend(tf.io.gfile.glob(os.path.join(vv, "*.json")))
path_kv[k] = file_names
if self.builder_config.manual_split is not None:
path_kv = _update_split(path_kv, self.builder_config.manual_split)
split_fn = self.builder_config.manual_split['split']
return {k:self._generate_examples(v, split_fn[k]) for k, v in path_kv.items()}
# TODO(korquad): Returns the Dict[split names, Iterator[Key, Example]]
return {k:self._generate_examples(v) for k, v in path_kv.items()}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Return SplitGenerators."""
paths = dl_manager.download_and_extract({
'set-a':
'http://physionet.org/files/challenge-2012/1.0.0/set-a.tar.gz', # noqa: E501
# 'train-1-outcome': 'http://physionet.org/files/challenge-2012/1.0.0/Outcomes-a.txt?download', # noqa: E501
'set-b':
'http://physionet.org/files/challenge-2012/1.0.0/set-b.tar.gz', # noqa: E501
# 'train-2-outcome': 'http://physionet.org/files/challenge-2012/1.0.0/Outcomes-b.txt?download', # noqa: E501
'set-c':
'http://physionet.org/files/challenge-2012/1.0.0/set-c.tar.gz', # noqa: E501
# 'test-outcome': 'http://physionet.org/files/challenge-2012/1.0.0/Outcomes-c.txt?download', # noqa: E501
})
a_path = os.path.join(paths['set-a'], 'set-a')
b_path = os.path.join(paths['set-b'], 'set-b')
c_path = os.path.join(paths['set-c'], 'set-c')
return [
tfds.core.SplitGenerator(
name=tfds.Split.TRAIN,
gen_kwargs={
'data_dirs': [a_path, b_path, c_path],
'outcome_file': os.path.join(RESOURCES,
'train_listfile.csv')
},
),
tfds.core.SplitGenerator(
name=tfds.Split.VALIDATION,
gen_kwargs={
'data_dirs': [a_path, b_path, c_path],
'outcome_file': os.path.join(RESOURCES, 'val_listfile.csv')
},
),
tfds.core.SplitGenerator(name=tfds.Split.TEST,
gen_kwargs={
'data_dirs': [a_path, b_path, c_path],
'outcome_file':
os.path.join(RESOURCES,
'test_listfile.csv')
})
]
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
splits = dl_manager.download_and_extract({
"train":
"https://github.com/lovit/namuwikitext/releases/download/v0.3/namuwikitext_20200302.train.zip",
"dev":
"https://github.com/lovit/namuwikitext/releases/download/v0.3/namuwikitext_20200302.dev.zip",
"test":
"https://github.com/lovit/namuwikitext/releases/download/v0.3/namuwikitext_20200302.test.zip",
})
return {
"train":
self._generate_examples(
splits["train"] / "namuwikitext_20200302.train", "train"),
"dev":
self._generate_examples(
splits["dev"] / "namuwikitext_20200302.dev", "dev"),
"test":
self._generate_examples(
splits["test"] / "namuwikitext_20200302.test", "test"),
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(indic_corpus): Downloads the data and defines the splits
path = dl_manager.download_and_extract(
"https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/all_langs/monoling.zip"
)
# TODO(indic_corpus): Returns the Dict[split names, Iterator[Key, Example]]
splits = []
for lang in self.builder_config.languages:
splits.extend([
tfds.core.SplitGenerator(
name=lang,
gen_kwargs=dict(path=path / f"monoling/{lang}.txt")),
tfds.core.SplitGenerator(
name=f"{lang}-validation",
gen_kwargs=dict(path=path /
f"monoling/{lang}-validation.txt"),
),
])
return splits