Commit file to HF hub

gqa-lxmert.py

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+# 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 preprocessed for LXMERT."""
+
+import base64
+import csv
+import json
+import os
+import sys
+
+import datasets
+import numpy as np
+
+
+csv.field_size_limit(sys.maxsize)
+
+
+_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",
+    "train_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
+    "dev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
+    "dev_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/gqa_testdev_obj36.zip",
+    "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_ans2label.json",
+}
+
+_TRAIN_IMG_PATH = "vg_gqa_obj36.tsv"
+_DEV_IMG_PATH = "vg_gqa_imgfeat/gqa_testdev_obj36.tsv"
+
+FIELDNAMES = [
+    "img_id", "img_h", "img_w", "objects_id", "objects_conf", "attrs_id", "attrs_conf", "num_boxes", "boxes", "features"
+]
+
+SHAPE_FEATURES = (36, 2048)
+SHAPE_BOXES = (36, 4)
+
+
+class GqaLxmert(datasets.GeneratorBasedBuilder):
+    """The GQA dataset preprocessed for LXMERT, with the objects features detected by a Faster RCNN replacing the
+    raw images."""
+
+    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"),
+                "features": datasets.Array2D(SHAPE_FEATURES, dtype="float32"),
+                "boxes": datasets.Array2D(SHAPE_BOXES, dtype="float32"),
+                "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"], "imgfeat": os.path.join(dl_dir["train_feat"], _TRAIN_IMG_PATH)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dl_dir["dev"], "imgfeat": os.path.join(dl_dir["dev_feat"], _DEV_IMG_PATH)},
+            ),
+        ]
+
+    def _load_features(self, filepath):
+        """Returns a dictionary mapping an image id to the corresponding image's objects features."""
+        id2features = {}
+        with open(filepath) as f:
+            reader = csv.DictReader(f, FIELDNAMES, delimiter="\t")
+            for i, item in enumerate(reader):
+                features = {}
+                for key in ["img_h", "img_w", "num_boxes"]:
+                    features[key] = int(item[key])
+                num_boxes = features["num_boxes"]
+                decode_config = [
+                    ("objects_id", (num_boxes,), np.int64),
+                    ("objects_conf", (num_boxes,), np.float32),
+                    ("attrs_id", (num_boxes,), np.int64),
+                    ("attrs_conf", (num_boxes,), np.float32),
+                    ("boxes", (num_boxes, 4), np.float32),
+                    ("features", (num_boxes, -1), np.float32),
+                ]
+                for key, shape, dtype in decode_config:
+                    features[key] = np.frombuffer(base64.b64decode(item[key]), dtype=dtype).reshape(shape)
+                id2features[item["img_id"]] = features
+        return id2features
+
+    def _generate_examples(self, filepath, imgfeat):
+        """ Yields examples as (key, example) tuples."""
+        id2features = self._load_features(imgfeat)
+        with open(filepath, encoding="utf-8") as f:
+            gqa = json.load(f)
+            for id_, d in enumerate(gqa):
+                img_features = id2features[d["img_id"]]
+                label = self.ans2label[next(iter(d["label"]))]
+                yield id_, {
+                    "question": d["sent"],
+                    "question_id": d["question_id"],
+                    "image_id": d["img_id"],
+                    "features": img_features["features"],
+                    "boxes": img_features["boxes"],
+                    "label": label,
+                }