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# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""adVQA loading script."""
import csv
import json
import os
from pathlib import Path
import datasets
_CITATION = """\
@InProceedings{sheng2021human,
author = {Sheng, Sasha and Singh, Amanpreet and Goswami, Vedanuj and Magana, Jose Alberto Lopez and Galuba, Wojciech and Parikh, Devi and Kiela, Douwe},
title = {Human-Adversarial Visual Question Answering},
journal={arXiv preprint arXiv:2106.02280},
year = {2021},
}
"""
_DESCRIPTION = """\
This is v1.0 of the ADVQA dataset.
"""
_HOMEPAGE = "https://adversarialvqa.org"
_LICENSE = "CC BY-NC 4.0" # In json file
_URLS = {
"questions": {
"val": "https://dl.fbaipublicfiles.com/advqa/v1_OpenEnded_mscoco_val2017_advqa_questions.json",
"test-dev": "https://dl.fbaipublicfiles.com/advqa/v1_OpenEnded_mscoco_testdev2015_advqa_questions.json",
},
"annotations": {
"val": "https://dl.fbaipublicfiles.com/advqa/v1_mscoco_val2017_advqa_annotations.json",
},
"images": {
"val": "http://images.cocodataset.org/zips/val2014.zip",
"test-dev": "http://images.cocodataset.org/zips/test2015.zip",
},
}
_SUB_FOLDER_OR_FILE_NAME = {
"questions": {
"val": None,
"test-dev": None,
},
"annotations": {
"val": None,
},
"images": {
"val": "val2014",
"test-dev": "test2015",
},
}
class VQAv2Dataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
# BUILDER_CONFIGS = [
# datasets.BuilderConfig(name="v2", version=VERSION, description="TODO later"), coco version in-domain
# datasets.BuilderConfig(name="v1", version=VERSION, description="TODO later"), AVQA out-of-domain
# ]
def _info(self):
features = datasets.Features(
{
"answers": [
{
"answer": datasets.Value("string"),
"answer_id": datasets.Value("int64"),
}
],
"image_id": datasets.Value("int64"),
"answer_type": datasets.Value("string"),
"question_id": datasets.Value("int64"),
"question": datasets.Value("string"),
"image": datasets.Image(),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# urls = _URLS[self.config.name] # TODO later
data_dir = dl_manager.download_and_extract(_URLS)
gen_kwargs = {}
for split_name in ["val", "test-dev"]:
gen_kwargs_per_split = {}
for dir_name in _URLS.keys():
sub_folder_or_file_name = _SUB_FOLDER_OR_FILE_NAME.get(dir_name, None).get(split_name, None)
if split_name in data_dir[dir_name] and sub_folder_or_file_name is not None:
path = Path(data_dir[dir_name][split_name]) / sub_folder_or_file_name
elif split_name in data_dir[dir_name]:
path = Path(data_dir[dir_name][split_name])
else:
path = None
gen_kwargs_per_split[f"{dir_name}_path"] = path
gen_kwargs[split_name] = gen_kwargs_per_split
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs=gen_kwargs["val"],
),
datasets.SplitGenerator(
name="testdev",
gen_kwargs=gen_kwargs["test-dev"],
),
]
def _generate_examples(self, questions_path, annotations_path, images_path):
questions = json.load(open(questions_path, "r"))
if annotations_path is not None:
dataset = json.load(open(annotations_path, "r"))
qa = {ann["question_id"]: [] for ann in dataset["annotations"]}
for ann in dataset["annotations"]:
qa[ann["question_id"]] = ann
for question in questions["questions"]:
annotation = qa[question["question_id"]]
# some checks
assert len(set(question.keys()) ^ set(["image_id", "question", "question_id"])) == 0
assert (
len(
set(annotation.keys())
^ set(
[
"answers",
"image_id",
"answer_type",
"question_id",
]
)
)
== 0
)
record = question
record.update(annotation)
record["image"] = str(images_path / f"COCO_{images_path.name}_{record['image_id']:0>12}.jpg")
yield question["question_id"], record
else:
# No annotations for the test split
for question in questions["questions"]:
question.update(
{
"answers": None,
"answer_type": None,
}
)
question["image"] = str(images_path / f"COCO_{images_path.name}_{question['image_id']:0>12}.jpg")
yield question["question_id"], question
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