add working loading script

AdVQA.py

@@ -0,0 +1,173 @@
+# 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