Datasets:

nlphuji
/

vasr

	# 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.
	""" VASR Loading Script """

	import json
	import os
	import pandas as pd
	import datasets
	from huggingface_hub import hf_hub_url

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """
	"""

	_DESCRIPTION = """\
	VASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy).
	"""

	_HOMEPAGE = "https://vasr-dataset.github.io/"

	_LICENSE = "https://creativecommons.org/licenses/by/4.0/"

	_URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main"

	class Vasr(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.1.0")

	# 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

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="v1", version=VERSION, description="vasr gold test dataset"),
	]
	DATASET_KEYS = ["A", "A'", "B", "B'", 'candidates', 'label', "A_str", "A'_str", "B_str", "B'_str"]
	HIDDEN_LABEL = '? (hidden)'
	QMARK_IMG = 'qmark.png'

	def _info(self):
	features = datasets.Features(
	{
	"A": datasets.Image(),
	"A'": datasets.Image(),
	"B": datasets.Image(),
	"B'": datasets.Image(),
	"candidates_images": [datasets.Image()],
	"label": datasets.Value("int64"),
	"candidates": [datasets.Value("string")],
	"A_str": datasets.Value("string"),
	"A'_str": datasets.Value("string"),
	"B_str": datasets.Value("string"),
	"B'_str": 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):
	# 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
	data_dir = dl_manager.download_and_extract({
	"images_dir": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="vasr_images.zip")
	})
	test_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="test_gold_unlabeled.csv")
	dev_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="dev_gold.csv")
	train_examples = hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="train_gold.csv")

	train_gen = datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={data_dir, {'examples_csv': train_examples}})
	dev_gen = datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={data_dir, {'examples_csv': dev_examples}})
	test_gen = datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={data_dir, {'examples_csv': test_examples}})

	return [train_gen, dev_gen, test_gen]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, examples_csv, images_dir):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.

	df = pd.read_csv(examples_csv)

	for r_idx, r in df.iterrows():
	r_dict = r.to_dict()
	r_dict['candidates'] = json.loads(r_dict['candidates'])
	candidates_images = [os.path.join(images_dir, "vasr_images", x) for x in
	r_dict['candidates']]
	r_dict['candidates_images'] = candidates_images
	r_dict["A_str"] = r_dict['A_img']
	r_dict["A'_str"] = r_dict['B_img']
	r_dict["B_str"] = r_dict['C_img']
	r_dict["B'_str"] = r_dict['D_img']
	for img in ['A_img', 'B_img', 'C_img', 'D_img']:
	if r_dict[img] == self.HIDDEN_LABEL:
	r_dict[img] = os.path.join(images_dir, "vasr_images", self.QMARK_IMG)
	else:
	r_dict[img] = os.path.join(images_dir, "vasr_images", r_dict[img])
	r_dict["A"] = r_dict['A_img']
	r_dict["A'"] = r_dict['B_img']
	r_dict["B"] = r_dict['C_img']
	r_dict["B'"] = r_dict['D_img']
	relevant_r_dict = {k:v for k,v in r_dict.items() if k in self.DATASET_KEYS or k == 'candidates_images'}
	yield r_idx, relevant_r_dict