vqa-lxmert.py

# 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.
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"""The Visual Question Answering (VQA) 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{antol2015vqa,
  title={Vqa: Visual question answering},
  author={Antol, Stanislaw and Agrawal, Aishwarya and Lu, Jiasen and Mitchell, Margaret and Batra, Dhruv and Zitnick, C Lawrence and Parikh, Devi},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2425--2433},
  year={2015}
}
"""

_DESCRIPTION = """\
VQA is a new dataset containing open-ended questions about images. 
These questions require an understanding of vision, language and commonsense knowledge to answer.
"""

_URLS = {
    "train": "https://nlp.cs.unc.edu/data/lxmert_data/vqa/train.json",
    "train_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/train2014_obj36.zip",
    "valid": "https://nlp.cs.unc.edu/data/lxmert_data/vqa/valid.json",
    "valid_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/val2014_obj36.zip",
    "test": "https://nlp.cs.unc.edu/data/lxmert_data/vqa/test.json",
    "test_feat": "https://nlp.cs.unc.edu/data/lxmert_data/mscoco_imgfeat/test2015_obj36.zip",
    "ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_ans2label.json",
}

_TRAIN_FEAT_PATH = "train2014_obj36.tsv"
_VALID_FEAT_PATH = "mscoco_imgfeat/val2014_obj36.tsv"
_TEST_FEAT_PATH = "mscoco_imgfeat/test2015_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 VqaV2Lxmert(datasets.GeneratorBasedBuilder):
    """The VQAv2.0 dataset preprocessed for LXMERT, with the objects features detected by a Faster RCNN replacing the
    raw images."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="vqa", version=datasets.Version("2.0.0"), description="VQA version 2 dataset."),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "question": datasets.Value("string"),
                "question_type": datasets.Value("string"),
                "question_id": datasets.Value("int32"),
                "image_id": datasets.Value("string"),
                "features": datasets.Array2D(_SHAPE_FEATURES, dtype="float32"),
                "normalized_boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"),
                "answer_type": datasets.Value("string"),
                "label": datasets.Sequence(
                    {
                        "ids": datasets.ClassLabel(num_classes=3129),
                        "weights": datasets.Value("float32"),
                    }
                ),
            }
        )
        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_FEAT_PATH)},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": dl_dir["valid"], "imgfeat": os.path.join(dl_dir["valid_feat"], _VALID_FEAT_PATH)},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": dl_dir["test"], "imgfeat": os.path.join(dl_dir["test_feat"], _TEST_FEAT_PATH), "labeled": False},
            ),
        ]

    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 = {}
                img_h = int(item["img_h"])
                img_w = int(item["img_w"])
                num_boxes = int(item["num_boxes"])
                features["features"] = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape(
                    (num_boxes, -1)
                )
                boxes = np.frombuffer(base64.b64decode(item["boxes"]), dtype=np.float32).reshape((num_boxes, 4))
                features["normalized_boxes"] = self._normalize_boxes(boxes, img_h, img_w)
                id2features[item["img_id"]] = features
        return id2features

    def _normalize_boxes(self, boxes, img_h, img_w):
        """ Normalize the input boxes given the original image size."""
        normalized_boxes = boxes.copy()
        normalized_boxes[:, (0, 2)] /= img_w
        normalized_boxes[:, (1, 3)] /= img_h
        return normalized_boxes

    def _generate_examples(self, filepath, imgfeat, labeled=True):
        """ Yields examples as (key, example) tuples."""
        id2features = self._load_features(imgfeat)
        with open(filepath, encoding="utf-8") as f:
            vqa = json.load(f)
            if labeled:
                for id_, d in enumerate(vqa):
                    img_features = id2features[d["img_id"]]
                    ids = [self.ans2label[x] for x in d["label"].keys()]
                    weights = list(d["label"].values())
                    yield id_, {
                        "question": d["sent"],
                        "question_type": d["question_type"],
                        "question_id": d["question_id"],
                        "image_id": d["img_id"],
                        "features": img_features["features"],
                        "normalized_boxes": img_features["normalized_boxes"],
                        "answer_type": d["answer_type"],
                        "label": {
                            "ids": ids,
                            "weights": weights,
                        },
                    }
            else:
                for id_, d in enumerate(vqa):
                    img_features = id2features[d["img_id"]]
                    yield id_, {
                        "question": d["sent"],
                        "question_type": "",
                        "question_id": d["question_id"],
                        "image_id": d["img_id"],
                        "features": img_features["features"],
                        "normalized_boxes": img_features["normalized_boxes"],
                        "answer_type": "",
                        "label": {
                            "ids": [],
                            "weights": [],
                        },
                    }