# 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