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vqaloader

vqaloader

 
 

.gitignore

.gitignore

 
 

README.md

README.md

 
 

requirements.txt

requirements.txt

 
 

setup.py

setup.py

 
 

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VQALoader

Install

pip install git+https://github.com/phiyodr/vqaloader

Data download

VQAv2

  • Splits: train, val, testdev, test
  • df columns: ['question', 'question_id', 'image_path', 'question_type', 'multiple_choice_answer', 'answers', 'answer_type', 'image_id']
  • Answers:
    • One main answer in multiple_choice_answer.
    • List of 10 dicts in answers. Example: {'answer': 'net', 'answer_confidence': 'maybe', 'answer_id': 1}.
# cd ~/Data/ # or anywhere you want to place it
mkdir VQAv2 && cd VQAv2
# VQA Input Questions
wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Train_mscoco.zip # Training questions 2017 v2.0* 443,757 questions 
wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Val_mscoco.zip   # Validation questions 2017 v2.0* 214,354 questions 
wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Questions_Test_mscoco.zip  # Testing questions 2017 v2.0 447,793 questions 
v2_OpenEnded_mscoco_test-dev2015_questions.json 
# VQA Input Images
wget http://images.cocodataset.org/zips/train2014.zip # COCO Training images 82,783 images 
wget http://images.cocodataset.org/zips/val2014.zip   # Validation images 40,504 images 
wget http://images.cocodataset.org/zips/test2015.zip  # Testing images 81,434 images 
# VQA Annotations Balanced Real Images
wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Train_mscoco.zip # Training annotations 2017 v2.0* 4,437,570 answers 
wget https://s3.amazonaws.com/cvmlp/vqa/mscoco/vqa/v2_Annotations_Val_mscoco.zip   # Validation annotations 2017 v2.0* 2,143,540 answers 
# unzip
unzip "*.zip"
echo "Done!"

GQA

  • Splits: challenge, submission, test, testdev, train, val
  • df columns: ['semantic', 'entailed', 'equivalent', 'question', 'imageId', 'isBalanced', 'groups', 'answer', 'semanticStr', 'annotations', 'types', 'fullAnswer'
  • Answer: One answer in answer.
# cd ~/Data/ # or anywhere you want to place it
mkdir GQA && cd GQA
ẁget https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip       # Download Images (20.3 GB images)
wget https://downloads.cs.stanford.edu/nlp/data/gqa/questions1.2.zip # Download Questions (1.4 GB questions)
unzip images.zip
unzip questions1.2.zip
echo "Done!"

VizWiz

  • Splits: train, val, test
  • df columns: ['semantic', 'entailed', 'equivalent', 'question', 'imageId', 'isBalanced', 'groups', 'answer', 'semanticStr', 'annotations', 'types', 'fullAnswer'
  • Answer: One answer in answer.
# cd ~/Data/ # or anywhere you want to place it
mkdir vizwiz && cd vizwiz
ẁget https://vizwiz.cs.colorado.edu/VizWiz_final/images/train.zip # Download Images (10.5 GB images)
ẁget https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip  # Download Images (3.2 GB images)
wget https://vizwiz.cs.colorado.edu/VizWiz_final/images/val.zip   # Download Images (3.7 GB questions)
wget https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip # Download Annotations
# unzip
unzip "*.zip"
echo "Done!"

Visual7W

  • Splits:
    • train, test and val
  • df columns:
    • ['image_id', 'question', 'multiple_choices', 'qa_id', 'answer', 'type']
# cd ~/Data/ # or anywhere you want to place it
mkdir visual7w && cd visual7w
wget http://vision.stanford.edu/yukezhu/visual7w_images.zip # Download COCO Images (1.7 GB images)
wget http://ai.stanford.edu/~yukez/papers/resources/dataset_v7w_telling.zip #Telling QA
# unzip
unzip "*.zip"
echo "Done!"

COCO-QA

  • Splits:
    • train and test
  • df columns:
    • ['questions', 'answers', 'image_ids', 'types']
# cd ~/Data/ # or anywhere you want to place it
mkdir COCOQA && cd COCOQA
wget http://www.cs.toronto.edu/~mren/imageqa/data/cocoqa/cocoqa-2015-05-17.zip # Download train and test split
wget http://images.cocodataset.org/zips/train2014.zip #Download train images
wget http://images.cocodataset.org/zips/test2015.zip #Dowload test images
# unzip
unzip "*.zip"
echo "Done!"

TextVQA

  • Splits: train, val, test
  • df columns: ['dataset_type', 'dataset_name', 'dataset_version', 'data', 'question', 'image_id', 'image_classes', 'flickr_original_url', 'flickr_300k_url', 'image_width', 'image_height', 'answers', 'question_tokens', 'question_id', 'set_name']
  • Answer: List of 10 answers in answers.
# cd ~/Data/ # or anywhere you want to place it
mkdir TextVQA && cd TextVQA
ẁget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_train.json # Training set 34,602 questions (103 MB)
wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json   # Validation set 5,000 questions (16MB)
wget https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_test.json  # Test set 5,734 questions (13MB)
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip   # Training set 21,953 images (6.6 GB)
wget https://dl.fbaipublicfiles.com/textvqa/images/test_images.zip        # Test set 3,289 images (926MB)
# unzip
unzip train_val_images.zip
unzip test_images.zip
echo "Done!"

OK-VQA

  • Splits:
    • train for training
    • test for testing. Authors call the file "val" and state it is for testing.
  • df columns: ['question', 'question_id', 'image_path', 'answer_type', 'question_type', 'answers', 'image_id']
  • Answers: List of 10 dicts in answers. Example: {'answer_id': 8, 'raw_answer': 'labrador retriever', 'answer_confidence': 'yes', 'answer': 'labrador retriev'} (raw_answer is stemmed, answer is full answer).
# cd ~/Data/ # or anywhere you want to place it
mkdir OKVQA && cd OKVQA
# OK-VQA Input Questions
wget https://okvqa.allenai.org/static/data/OpenEnded_mscoco_train2014_questions.json # Training questions
wget https://okvqa.allenai.org/static/data/OpenEnded_mscoco_val2014_questions.json   # Testing questions
# Images (COCO)
wget http://images.cocodataset.org/zips/train2014.zip # Training images
wget http://images.cocodataset.org/zips/val2014.zip   # Testing images
# OK-VQA Annotations
wget https://okvqa.allenai.org/static/data/mscoco_train2014_annotations.json # Training annotations (v1.1 updated 7/29/2020)
wget https://okvqa.allenai.org/static/data/mscoco_val2014_annotations.json   # Testing annotations (v1.1 updated 7/29/2020)
# unzip
unzip "*.zip"
echo "Done!"

KVQA

  • Splits:
    • train, test and val
  • df columns:
    • ['NamedEntities', 'imgPath', 'ParaQuestions', 'Qids', 'Questions', 'split', 'wikiCap', 'Answers', 'Type of Question']
# cd ~/Data/ # or anywhere you want to place it
mkdir KVQA && cd KVQA
wget http://dosa.cds.iisc.ac.in/kvqa/dataset.json # Dataset JSON
wget hhttp://dosa.cds.iisc.ac.in/kvqa/KVQAimgs.tar.gz # Dataset images (25 GB)
wget http://dosa.cds.iisc.ac.in/kvqa/KVQArefImgs.tar.gz # Reference images (61 GB)
# unzip
tar -xf "*.gz"
echo "Done!"

Pre-training data

COCOO2017

mkdir coco2017 && cd coco2017
wget http://images.cocodataset.org/zips/train2017.zip # 2017 Train images [118K/18GB]
wget http://images.cocodataset.org/zips/val2017.zip # 2017 Val images [5K/1GB]
wget http://images.cocodataset.org/zips/test2017.zip # 2017 Test images [41K/6GB]

COCOO2014

mkdir COCO2014 && cd COCO2014
wget http://images.cocodataset.org/zips/train2014.zip # 2014 Train images [13 GB]
wget http://images.cocodataset.org/zips/val2014.zip # 2014 Val images [6 GB]
wget http://images.cocodataset.org/zips/test2014.zip # 2014 Test images [6 GB]
wget http://images.cocodataset.org/zips/test2015.zip # 2015 Test images [12 GB]

wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip # 241 MB
wget http://images.cocodataset.org/annotations/image_info_test2014.zip # 
wget http://images.cocodataset.org/annotations/image_info_test2015.zip #

Usage

  • VQAv2Dataset
from vqaloader.loaders import VQAv2Dataset
dataset = VQAv2Dataset(split="train", data_path="~/Data/VQAv2", testing=False)
print(dataset[0])
  • GQADataset
from vqaloader.loaders import GQADataset
dataset = GQADataset(split="train", balanced=True, data_path="~/Data/GQA", testing=False)
print(dataset[0])
  • VizwizDataset
from vqaloader.loaders import VizwizDataset
dataset = VizwizDataset(split="train", data_path="~/Data/vizwiz", testing=False)
print(dataset[0])
  • Visual7WDataset
from vqaloader.loaders import Visual7WDataset 
dataset = Visual7WDataset(split="train",  data_path="~/Data/visual7w", testing=False)
print(dataset[0])
  • COCO-QADataset
from vqaloader.loaders import COCOQADataset 
dataset = COCOQADataset(split="train",  data_path="~/Data/COCOQA", testing=False)
print(dataset[0])
  • TextVQADataset
from vqaloader.loaders import TextVQADataset
dataset = TextVQADataset(split="train", data_path="~/Data/TextVQA", testing=False)
print(dataset[0])
  • OKVQADataset
from vqaloader.loaders import OKVQADataset 
dataset = OKVQADataset(split="train",  data_path="~/Data/OKVQA", testing=False)
print(dataset[0])
  • KVQADataset
from vqaloader.loaders import KVQADataset 
dataset = KVQADataset(split="train",  data_path="~/Data/Visual7W", testing=False)
print(dataset[0])
  • COCO2014Dataset
from vqaloader.captioner import COCO2014Dataset 
dataset = COCO2014Dataset(split=split, data_path="~/Data/COCO2014", testing=False)
print(dataset[0])

Versions

  • 0.1.4 Add COCO2014

About

PyTorch DataLoader for many VQA datasets

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pytorch vqa dataloader gqa textvqa vqav2

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