- Authors: Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal
- Paper (To appear in ICML 2021)
(VQA inference using pretrained model on custom image/question)
- Try web demo and docker image on VQA here
# Create python environment (optional)
conda create -n vlt5 python=3.7
source activate vlt5
# Install python dependencies
pip install -r requirements.txt
# Download T5/BART backbone checkpoint
python download_backbones.py
# For MSCOCO captioning evaluation (optional; for captioning only)
python -c "import language_evaluation; language_evaluation.download('coco')"
# Store images, features, and annotations
./datasets
COCO/
images/
featuers/
VG/
images/
features/
GQA/
images/
features/
nlvr/
images/
features/
RefCOCO/
...
# Run feature extraction
./feature_extraction
# Train VL-T5
./VL-T5/
src/
modeling_t5.py modeling_bart.py <= VL-T5/VL-BART model classes
pretrain.py, pretrain_data.py, pretrain_model.py <= pretraining
vqa.py, vqa_data.py vqa_model.py ... <= fine-tuning on downstream tasks (ex. VQA, GQA, NLVR2)
multitask.py, multitask_data.py multiask_model.py <= multitask learning on 7 downstream tasks
param.py <= (argparse) configuration
tokenization.py <= custom tokenizer
utils.py, dist_utils.py <= utility functions
snap/ <= store weight checkpoints
scripts/ <= bash scripts for pretraining and finetuning
import sys
sys.path.append('./VL-T5/src')
# Parse configuration
from param import parse_args
args = parse_args(
backbone='t5-base' # Backbone architecture
load='./snap/pretrain/VLT5/Epoch30' # Pretrained checkpoint
parse=False, # False for interactive env (ex. jupyter)
)
# Assign GPU
args.gpu = 0
# Load data loaders
from vqa_data import get_loader
train_loader = get_loader(
args,
split=args.train,
...
)
val_loader = get_loader(
args,
split=args.valid,
...
)
test_loader = get_loader(
args,
split=args.test,
...
)
# Import trainer
from vqa import Trainer
trainer = Trainer(
args,
train_loader=train_loader
val_loader=val_loader
test_loader=test_loader,
)
# model is attached to trainer
model = trainer.model
# Each task-specific model class is inherited from VLT5/VLBart classes, which are inherited from Huggingface transformers T5/BART classes
print(model)
>>> VLT5VQA(
(shared): Embedding(...)
(encoder): JointEncoder(...)
...
)
# Training
train_batch = next(iter(train_loader))
model.train_step(train_batch)
>>> {'loss': ... }
# Inference
test_batch = next(iter(test_loader))
model.test_step(test_batch)
>>> {'pred_ans': ... }
To add a new task, you can start with writing 3 files by editing from existing ones.
NEW_TASK_model.py # Define a VLT5NewTask/VLBartNewTask model which inherits VLT5/VLBart class
NEW_TASK_data.py # Define Dataset/DataLoader/Evaluator
NEW_TASK.py # Define a trainer which inherits TrainerBase (trainer_base.py)
We host model checkpoints and features via google drive. We recommend using gdrive to download them.
- Download
snap/
from Google Drive
gdrive download 1_SBj4sZ0gUqfBon1gFBiNRAmfHv5w_ph --recursive
VL-T5/snap/pretrain/VLT5/Epoch30.pth
: VL-T5 pretrained for 30 epochs on COCO+VGVL-T5/snap/pretrain/VLBart/Epoch30.pth
: VL-BART pretrained for 30 epochs on COCO+VG
VL-T5/snap/vcr_pretrain/VLT5/Epoch20.pth
: VL-T5 further pretrained for 20 epochs on VCRVL-T5/snap/vcr_pretrain/VLBart/Epoch20.pth
: VL-BART further pretrained for 20 epochs on VCR
- Download
datasets/
from Google Drive
gdrive download 1MBBhlkP83VMKS2Qe0SmFfzkHhMpIG5wf --recursive
- Multi30K only
git clone --recursive https://github.com/multi30k/dataset ./datasets/multi30k-dataset
- unzip
train.en.gz
,val.en.gz
,test_2017_flickr.en.gz
,test_2018_flickr.en.gz
in./datasets/multi30k-dataset/data/task1/raw/
- unzip
train.de.gz
,val.de.gz
,test_2017_flickr.de.gz
,test_2018_flickr.de.gz
in./datasets/multi30k-dataset/data/task1/raw/
- For manual feature extraction, please checkout ./feature_extraction
# Pretraining with 4 gpus
cd VL-T5/
bash scripts/COCOVG_pretrain_VLT5.sh 4
bash scripts/COCOVG_pretrain_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/VQA_VLT5.sh 4
bash scripts/VQA_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/GQA_VLT5.sh 4
bash scripts/GQA_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/NLVR_VLT5.sh 4
bash scripts/NLVR_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/RefCOCOg_VLT5.sh 4
bash scripts/RefCOCOG_VLBart.sh 4
# Pretraining on VCR with 4 gpus (optional)
cd VL-T5/
bash scripts/VCR_pretrain_VLT5.sh 4
bash scripts/VCR_pretrain_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/VCR_VLT5.sh 4
bash scripts/VCR_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/COCOCaption_VLT5.sh 4
bash scripts/COCOCaption_VLBart.sh 4
# Finetuning with 4 gpus
cd VL-T5/
bash scripts/Multi30K_VLT5.sh 4
bash scripts/Multi30K_VLBart.sh 4
Please cite our paper if you use our models in your works:
@inproceedings{cho2021vlt5,
title = {Unifying Vision-and-Language Tasks via Text Generation},
author = {Jaemin Cho and Jie Lei and Hao Tan and Mohit Bansal},
booktitle = {ICML},
year = {2021}
}