rico_captions / rico_captions.py
ncoop57
Add filtered rico version where bad hierarchies are removed
cd1cf68
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# coding=utf-8
# 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: Add a description here."""
import csv
import glob
import os
import datasets
import numpy as np
# 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 = "http://interactionmining.org/rico"
# 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 dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_DATA_URLs = {
"screenshots_captions": "https://huggingface.co/datasets/ncoop57/rico_captions/resolve/main/captions_hierarchies_images.zip",
"screenshots_captions_filtered": "https://huggingface.co/datasets/ncoop57/rico_captions/resolve/main/captions_hierarchies_images_filtered.zip",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class RicoDataset(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="screenshots_captions",
version=VERSION,
description="Contains 66k+ unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model.",
),
datasets.BuilderConfig(
name="screenshots_captions_filtered",
version=VERSION,
description="Contains 25k unique UI screens. For each UI, we present a screenshot (JPG file) and the text shown on the screen that was extracted using an OCR model. Filtering was done as discussed in this paper: https://aclanthology.org/2020.acl-main.729.pdf",
),
]
DEFAULT_CONFIG_NAME = "screenshots_captions_filtered"
def _info(self):
features = datasets.Features(
{
"screenshot_path": datasets.Value("string"),
"caption": datasets.Value("string"),
# This is a JSON obj, but will be coded as a string
"hierarchy": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
my_urls = _DATA_URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"root_dir": data_dir,
"split": "train",
},
)
]
def _generate_examples(
self,
root_dir,
split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
screen_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.jpg")))
hierarchy_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.json")))
caption_glob = sorted(glob.glob(os.path.join(root_dir, "**/*.txt")))
for idx, (screen_filepath, hierarchy_filepath, caption_filepath) in enumerate(
zip(screen_glob, hierarchy_glob, caption_glob)
):
with open(hierarchy_filepath, "r", encoding="utf-8") as f:
hierarchy = f.read()
with open(caption_filepath, "r", encoding="utf-8") as f:
caption = f.read()
yield idx, {"screenshot_path": screen_filepath, "hierarchy": hierarchy, "caption": caption}