Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
# 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 |