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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",
"valid": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json",
"trainval_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip",
"testdev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/testdev.json",
"test_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",
}
TRAINVAL_FEAT_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv"
TEST_FEAT_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"),
"normalized_boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"),
"label": datasets.ClassLabel(num_classes=1842),
}
)
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)
trainval_features_path = os.path.join(dl_dir["trainval_feat"], TRAINVAL_FEAT_PATH)
test_features_path = os.path.join(dl_dir["test_feat"], TEST_FEAT_PATH)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": dl_dir["train"],
"ans2label_path": dl_dir["ans2label"],
"features_path": trainval_features_path,
"testset": False,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dl_dir["valid"],
"ans2label_path": dl_dir["ans2label"],
"features_path": trainval_features_path,
"testset": False,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": dl_dir["testdev"],
"ans2label_path": dl_dir["ans2label"],
"features_path": test_features_path,
"testset": True,
},
),
]
def _generate_examples(self, filepath, ans2label_path, features_path, testset=False):
""" Yields examples as (key, example) tuples."""
if not hasattr(self, "ans2label"):
with open(ans2label_path, encoding="utf-8") as f:
self.ans2label = json.load(f)
if testset:
self.test_id2features = self._load_features(features_path)
else:
if not hasattr(self, "trainval_id2features"):
self.trainval_id2features = self._load_features(features_path)
with open(filepath, encoding="utf-8") as f:
gqa = json.load(f)
for id_, d in enumerate(gqa):
# test_id2features only contains features of a subset of samples in testdev.json
if testset:
try:
img_features = self.test_id2features[d["img_id"]]
except KeyError:
continue
label_key = next(iter(d["label"]))
if label_key not in self.ans2label:
print ("Ignored one sample because of unseen label.")
continue
label = self.ans2label[label_key]
else:
img_features = self.trainval_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"],
"normalized_boxes": img_features["normalized_boxes"],
"label": label,
}
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):
""" Normalizes 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
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