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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"
_URLS = {
"train": os.path.join(_URL, "train_gold.csv"),
"dev": os.path.join(_URL, "dev_gold.csv"),
"test": os.path.join(_URL, "test_gold.csv"),
}
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 gold test 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)]
# downloaded_files = dl_manager.download_and_extract(_URLS)
data_dir = dl_manager.download_and_extract({
"images_dir": hf_hub_url("datasets/nlphuji/vasr", filename="vasr_images.zip")
})
train_gen = datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=data_dir)
dev_gen = datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=data_dir)
test_gen = datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)
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)
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 |