Update PlotQA_dataset.py

PlotQA_dataset.py

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+# 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 ChartQA(datasets.GeneratorBasedBuilder):
+    
+    def _info(self):
+        features = datasets.Features(
+            {
+                "imgname": datasets.Value("string"),
+                "image": datasets.Image(),
+                "human": datasets.Value("bool"),
+                "query": datasets.Value("string"),
+                "query_token": datasets.Sequence(datasets.Value("string")),
+                "label": datasets.Value("string"),
+                "img_ann": datasets.Value("string"),
+                "qid": datasets.Value('int64')
+                #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 _generate_examples(self, annotations_path:str, human_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]
+        idx = 0
+        
+        ###
+        for anno_path in _multi_anno:
+            with open(anno_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+                #returns the examples in the raw in json file
+                for item in data:
+                    item = copy.deepcopy(item)
+                    item["image"] = os.path.join('./imgs',item["image_index"])
+                    item["query_token"] = []
+                    item["img_ann"] = item["question_string"]
+                    item["label"] = item["answer"]
+
+
+                    
+                    # item["image"] = os.path.join(images_path,item["imgname"])
+                    # item["query_token"] = []
+                    # item["table_name"] = os.path.splitext(item["imgname"])[0]+'.csv'
+                    # #item["table"] = os.path.join(table_path,item[idx]["table_name"])
+                    # file_name = os.path.basename(anno_path)
+                    # #Table load
+                    # df = pd.read_csv (os.path.join(table_path,item["imgname"].split('.')[0]+'.csv'))
+                    # item["table"] = df.to_dict()
+                    
+                    # 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