Datasets:

nlphuji
/

vasr

Languages:

English

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1K<n<10K

ArXiv:

Tags:

commonsense-reasoning

visual-reasoning

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vasr / vasr.py

yonatanbitton

Update vasr.py

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	# 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 Winogavil(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

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('vasr', 'test')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="TEST", version=VERSION, description="vasr dataset"),
	]
	IMAGE_EXTENSION = "jpg"

	def _info(self):
	features = datasets.Features(
	{
	"A_img": datasets.Value("string"),
	"B_img": datasets.Value("string"),
	"C_img": datasets.Value("string"),
	"candidates": [datasets.Value("string")],
	"candidates_images": [datasets.Value("string")],
	"label": datasets.Value("int64"),
	"D_img": datasets.Value("string"),
	"A_verb": datasets.Value("string"),
	"B_verb": datasets.Value("string"),
	"C_verb": datasets.Value("string"),
	"D_verb": datasets.Value("string"),
	"diff_item_A": datasets.Value("string"),
	"diff_item_A_str_first": datasets.Value("string"),
	"diff_item_B": datasets.Value("string"),
	"diff_item_B_str_first": 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({
	"examples_csv": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset', filename="test_gold.csv"),
	"images_dir": hf_hub_url(repo_id="nlphuji/vasr", repo_type='dataset',filename="vasr_images.zip")
	})

	return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]

	# 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)

	d_keys = ['A_img', 'B_img', 'C_img', 'candidates', 'label', 'D_img', 'A_verb', 'B_verb', 'C_verb', 'D_verb', 'diff_item_A', 'diff_item_A_str_first', 'diff_item_B', 'diff_item_B_str_first']

	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
	relevant_r_dict = {k:v for k,v in r_dict.items() if k in d_keys or k == 'candidates_images'}
	yield r_idx, relevant_r_dict