# 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")], | |
"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("datasets/nlphuji/vasr", filename="test_gold.csv"), | |
"images_dir": hf_hub_url("datasets/nlphuji/vasr", 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) | |
# columns_to_serialize = ['candidates', 'associations'] | |
# for c in columns_to_serialize: | |
# df[c] = df[c].apply(json.loads) | |
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", f"{x}.{self.IMAGE_EXTENSION}") for x in | |
r_dict['candidates']] | |
r_dict['candidates_images'] = candidates_images | |
yield r_idx, r_dict |