# 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. | |
# TODO: Address all TODOs and remove all explanatory comments | |
"""TODO: Add a description here.""" | |
import csv | |
import json | |
import os | |
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) | |
_URL = "dataset_coco.json" | |
_URLS = { | |
"train": _URL | |
} | |
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
class NewDataset(datasets.GeneratorBasedBuilder): | |
"""TODO: Short description of my dataset.""" | |
VERSION = datasets.Version("1.1.0") | |
# This is an example of a dataset with multiple configurations. | |
# If you don't want/need to define several sub-sets in your dataset, | |
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
# If you need to make complex sub-parts in the datasets with configurable options | |
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
# BUILDER_CONFIG_CLASS = MyBuilderConfig | |
# You will be able to load one or the other configurations in the following list with | |
# data = datasets.load_dataset('my_dataset', 'first_domain') | |
# data = datasets.load_dataset('my_dataset', 'second_domain') | |
#BUILDER_CONFIGS = [ | |
# datasets.BuilderConfig(name="images", version=VERSION, description="This part of my dataset covers a first domain"), | |
#] | |
#DEFAULT_CONFIG_NAME = "train" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
features=datasets.Features( | |
{ | |
"filepath": datasets.Value("string"), | |
"filename": datasets.Value("string") | |
#"raw": datasets.Value("string") | |
} | |
), | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
# specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
# supervised_keys=("sentence", "label"), | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
urls_to_download = _URLS | |
downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
print(download_files['train']) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
# datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
logger.info("generating examples from = %s", filepath) | |
print(download_files['train']) | |
with open(filepath, encoding="utf-8") as f: | |
data = json.loads(f) | |
for image in data["images"]: | |
file_path = image["filepath"] | |
file_name = image["filename"] | |
yield id_, { | |
"filepath": file_path, | |
"filename": file_name | |
} | |
#for sentence in image["sentences"]: | |
# raw = sentence["raw"] | |
# yield id_, { | |
# "filepath": filepath, | |
# "filename": filename, | |
# "raw": raw | |
# } |