|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""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": [], |
|
}, |
|
} |
