| # Builder script | |
| # coding=utf-8 | |
| # 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. | |
| # MIT License | |
| # Copyright (c) PlotQA. | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE | |
| """PlotQA dataset""" | |
| import copy | |
| import json | |
| import os | |
| import pandas as pd | |
| # importing the "tarfile" module | |
| import tarfile | |
| import datasets | |
| from datasets import load_dataset | |
| _CITATION = """\ | |
| @inproceedings{methani2020plotqa, | |
| title={Plotqa: Reasoning over scientific plots}, | |
| author={Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M and Kumar, Pratyush}, | |
| booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, | |
| pages={1527--1536}, | |
| year={2020} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| PlotQA dataset | |
| Chart images, tables, image annotations, questions, answers | |
| """ | |
| _LICENSE = "CC-BY-4.0 license" | |
| _SPLITS = ["train", "val", "test"] | |
| _URL = "https://huggingface.co/datasets/Dodon/PlotQA_dataset/resolve/main/PlotQA.zip" | |
| class PlotQA(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "imgname": datasets.Value("string"), | |
| #"image": datasets.Image(), | |
| #"version": datasets.Value("string"), | |
| "query": datasets.Sequence(datasets.Value("string")), | |
| "query_token": datasets.Sequence(datasets.Value("string")), | |
| "label": datasets.Sequence(datasets.Value("string")), | |
| "img_ann": datasets.Value("string"), | |
| ### | |
| "image_index": datasets.Value("string"), | |
| #"question_string": datasets.Value("string"), | |
| #"answer": datasets.Value("string"), | |
| #"answer_bbox": datasets.Value("string"), | |
| #"template": datasets.Value("string"), | |
| #"type": datasets.Value("string"), | |
| #This format required change | |
| ## "table_name": datasets.Value("string"), | |
| ## "table": datasets.Value("string"), | |
| #"table": datasets.table.Table(), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| license=_LICENSE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_file = dl_manager.download_and_extract(_URL) + "/PlotQA" | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "annotation_v1_path": downloaded_file + "/train/qa_pairs_V1.json", | |
| "annotation_v2_path": downloaded_file + "/train/qa_pairs_V2.json", | |
| "images_path": downloaded_file + "/train/png.tar.gz", | |
| "img_anno_path": downloaded_file + "/train/annotations.json", | |
| ##"table_path": downloaded_file + "/train/tables", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "annotation_v1_path": downloaded_file + "/validation/qa_pairs_V1.json", | |
| "annotation_v2_path": downloaded_file + "/validation/qa_pairs_V2.json", | |
| "images_path": downloaded_file + "/validation/png.tar.gz", | |
| "img_anno_path": downloaded_file + "/validation/annotations.json", | |
| ##"table_path": downloaded_file + "/train/tables", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "annotation_v1_path": downloaded_file + "/test/qa_pairs_V1.json", | |
| "annotation_v2_path": downloaded_file + "/test/qa_pairs_V2.json", | |
| "images_path": downloaded_file + "/test/png.tar.gz", | |
| "img_anno_path": downloaded_file + "/test/annotations.json", | |
| ##"table_path": downloaded_file + "/train/tables", | |
| }, | |
| ), | |
| ] | |
| # def find_qa(self, annotation_v1_path:str, annotation_v2_path:str, img_idx:int): | |
| # with open(annotation_v1_path, "r", encoding="utf-8") as v1: | |
| # data_v1 = json.load(v1) | |
| # with open(annotation_v2_path, "r", encoding="utf-8") as v2: | |
| # data_v2 = json.load(v2) | |
| # #1. | |
| # version = 1 | |
| # _temp_item = [] | |
| # for ele in data_v1: | |
| # if ele['image_index'] == img_idx: | |
| # _temp_item.append(ele) | |
| # for ele in data_v2: | |
| # if ele['image_index'] == img_idx: | |
| # _temp_item.append(ele) | |
| # return _temp_item | |
| def _generate_examples(self, annotation_v1_path:str, annotation_v2_path:str, img_anno_path:str ,images_path: str): | |
| #Load image folder | |
| # open file | |
| ## file = tarfile.open(images_path) | |
| # extracting file | |
| ## file.extractall('./imgs') | |
| ## file.close() | |
| _multi_anno = [annotation_v1_path, annotation_v2_path] | |
| with open(_multi_anno, "r", encoding="utf-8") as v: | |
| data = json.load(v) | |
| idx = 0 | |
| with open(img_anno_path, "r", encoding="utf-8") as a: | |
| img_data = json.load(a) | |
| for ele in img_data: | |
| item = {} | |
| item["img_ann"] = ele | |
| item["imgname"] = str(ele['image_index'])+'.png' | |
| # item['image'] = os.path.join('./imgs',item['imgname']) | |
| item["query_token"] = [] | |
| _temp_item = [] | |
| for element in data_v1: | |
| if element['image_index'] == ele['image_index']: | |
| _temp_item.append(element) | |
| #qa_returns = find_qa(annotation_v1_path,annotation_v2_path, item['image_index']) | |
| _question = [] | |
| _label = [] | |
| for pair in qa_returns: | |
| _question.append(pair['question_string']) | |
| _label.append(pair["answer"]) | |
| # file = os.path.splitext(file_name) | |
| # if file == "test_augmented": | |
| # item["human"] = False | |
| # else: | |
| # item["human"] = True | |
| # img_anot_file = os.path.splitext(item["imgname"])[0]+'.json' | |
| # img_anot = os.path.join(img_anno_path, img_anot_file) | |
| # with open(img_anot) as f: | |
| # item["img_ann"] = json.load(f) | |
| """ | |
| item['table'] = os.path.join(images_path,item["imgname"]) | |
| # annotation | |
| item["img_anno"] = load json file... | |
| t_path = os.path.join(table_path,item["table_name"]) | |
| table_data = load_dataset("csv", data_files=[t_path]) | |
| yield table_data | |
| """ | |
| yield idx, item | |
| idx += 1 |