working loading script

OK-VQA.py

@@ -0,0 +1,162 @@
+# 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.
+"""OK-VQA loading script."""
+
+
+import csv
+import json
+import os
+from pathlib import Path
+import datasets
+
+
+_CITATION = """\
+@article{DBLP:journals/corr/abs-1906-00067,
+  author    = {Kenneth Marino and
+               Mohammad Rastegari and
+               Ali Farhadi and
+               Roozbeh Mottaghi},
+  title     = {{OK-VQA:} {A} Visual Question Answering Benchmark Requiring External
+               Knowledge},
+  journal   = {CoRR},
+  volume    = {abs/1906.00067},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1906.00067},
+  eprinttype = {arXiv},
+  eprint    = {1906.00067},
+  timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-00067.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+
+_DESCRIPTION = """\
+OK-VQA is a new dataset for visual question answering that requires methods which can draw upon outside knowledge to answer questions.
+- 14,055 open-ended questions
+- 5 ground truth answers per question
+- Manually filtered to ensure all questions require outside knowledge (e.g. from Wikipeida)
+- Reduced questions with most common answers to reduce dataset bias
+"""
+
+
+_HOMEPAGE = "https://okvqa.allenai.org/"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC BY 4.0"  # found in the zip files bellow - we show maybe ask for confirmation
+
+
+_URLS = {
+    "annotations": {
+        "train": "https://okvqa.allenai.org/static/data/mscoco_train2014_annotations.json.zip",
+        "val": "https://okvqa.allenai.org/static/data/mscoco_val2014_annotations.json.zip",
+    },
+    "questions": {
+        "train": "https://okvqa.allenai.org/static/data/OpenEnded_mscoco_train2014_questions.json.zip",
+        "val": "https://okvqa.allenai.org/static/data/OpenEnded_mscoco_val2014_questions.json.zip",
+    },
+    "images": {
+        "train": "http://images.cocodataset.org/zips/train2014.zip",
+        "val": "http://images.cocodataset.org/zips/val2014.zip",
+    },
+}
+
+
+class OKVQADataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "question_type": datasets.Value('string'),
+                "confidence": datasets.Value('int32'),
+                "answers": [{
+                    "answer": datasets.Value('string'),
+                    "raw_answer": datasets.Value('string'),
+                    "answer_confidence": 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'),
+            }
+        )
+        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 ["train", "val"]:
+            gen_kwargs_per_split = {}
+            for dir_name in _URLS.keys():
+                if split_name in data_dir[dir_name]:
+                    file_name = Path(_URLS[dir_name][split_name]).name[: -len(".zip")]
+                    path = Path(data_dir[dir_name][split_name]) / file_name
+                    gen_kwargs_per_split[f"{dir_name}_path"] = path
+                else:
+                    gen_kwargs_per_split[f"{dir_name}_path"] = None
+            gen_kwargs[split_name] = gen_kwargs_per_split
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs=gen_kwargs["train"],
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs=gen_kwargs["val"],
+            ),
+        ]
+
+    def _generate_examples(self, questions_path, annotations_path, images_path):
+        dataset = json.load(open(annotations_path, "r"))
+        questions = json.load(open(questions_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()) ^ {"image_id", "question", "question_id"}) == 0
+            assert (
+                len(
+                    set(annotation.keys())
+                    ^ {
+                            "question_type",
+                            "confidence",
+                            "answers",
+                            "image_id",
+                            "answer_type",
+                            "question_id",
+                        }
+                )
+                == 0
+            )
+            # build record
+            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