Add gqa dataset script

gqa.py

@@ -0,0 +1,99 @@
+# 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.
+"""The GQA dataset."""
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{hudson2019gqa,
+  title={Gqa: A new dataset for real-world visual reasoning and compositional question answering},
+  author={Hudson, Drew A and Manning, Christopher D},
+  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
+  pages={6700--6709},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+GQA is a new dataset for real-world visual reasoning and compositional question answering,
+seeking to address key shortcomings of previous visual question answering (VQA) datasets.
+"""
+
+_URLS = {
+    "train": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/train.json",
+    "dev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
+    "img": "https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip",
+    "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_ans2label.json",
+}
+
+_IMG_DIR = "images"
+
+
+class Gqa(datasets.GeneratorBasedBuilder):
+    """The GQA dataset."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="gqa", version=datasets.Version("1.0.0"), description="GQA dataset."),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "question_id": datasets.Value("int32"),
+                "image_id": datasets.Value("string"),
+                "label": datasets.Value("int32"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_dir = dl_manager.download_and_extract(_URLS)
+        self.ans2label = json.load(open(dl_dir["ans2label"]))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": dl_dir["train"], "img_dir": os.path.join(dl_dir["img"], _IMG_DIR)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dl_dir["dev"], "img_dir": os.path.join(dl_dir["img"], _IMG_DIR)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, img_dir):
+        """ Yields examples as (key, example) tuples. """
+        with open(filepath, encoding="utf-8") as f:
+            gqa = json.load(f)
+            for id_, d in enumerate(gqa):
+                img_id = os.path.join(img_dir, d["img_id"] + ".jpg")
+                label = self.ans2label[next(iter(d["label"]))]
+                yield id_, {
+                    "question": d["sent"],
+                    "question_id": d["question_id"],
+                    "image_id": img_id,
+                    "label": label,
+                }