# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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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": [],
},
}