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https://github.com/jayleicn/ClipBERT
[CVPR 2021 Best Student Paper Honorable Mention, Oral] Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks.
https://github.com/jayleicn/ClipBERT
cvpr2021 pytorch video-question-answering video-retrieval vision-and-language vqa
Last synced: about 2 months ago
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[CVPR 2021 Best Student Paper Honorable Mention, Oral] Official PyTorch code for ClipBERT, an efficient framework for end-to-end learning on image-text and video-text tasks.
- Host: GitHub
- URL: https://github.com/jayleicn/ClipBERT
- Owner: jayleicn
- License: mit
- Created: 2021-02-10T20:48:30.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-08T22:18:55.000Z (about 1 year ago)
- Last Synced: 2024-07-24T23:01:50.553Z (about 2 months ago)
- Topics: cvpr2021, pytorch, video-question-answering, video-retrieval, vision-and-language, vqa
- Language: Python
- Homepage: https://arxiv.org/abs/2102.06183
- Size: 73.2 KB
- Stars: 693
- Watchers: 9
- Forks: 84
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-video-text-retrieval - ClipBERT
README
# ClipBERT
[Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://arxiv.org/abs/2102.06183)
[CVPR 2021](http://cvpr2021.thecvf.com/), Oral, [Best Student Paper Honorable Mention](http://cvpr2021.thecvf.com/node/329).
[Jie Lei](http://www.cs.unc.edu/~jielei/)\*, [Linjie Li](https://www.linkedin.com/in/linjie-li/)\*,
[Luowei Zhou](https://luoweizhou.github.io/), [Zhe Gan](https://zhegan27.github.io/),
[Tamara L. Berg](http://tamaraberg.com/), [Mohit Bansal](http://www.cs.unc.edu/~mbansal/),
[Jingjing Liu](https://www.linkedin.com/in/jingjing-liu-65703431/)
Official PyTorch code for ClipBERT, an efficient framework for
end-to-end learning for image-text and video-text tasks.
It takes raw videos/images + text as inputs, and outputs task predictions.
ClipBERT is designed based on 2D CNNs and transformers, and uses a sparse sampling strategy
to enable efficient end-to-end video-and-language learning. In this repository,
we support end-to-end pretraining and finetuning for the following tasks:
- Image-text pretraining on COCO and VG captions.
- Text-to-video retrieval finetuning on MSRVTT, DiDeMo, and ActivityNet Captions.
- Video-QA finetuning on TGIF-QA and MSRVTT-QA.
- Image-QA finetuning on VQA 2.0.
It is also feasible and easy to add other image-text or video-text tasks for pretraining and finetuning.
**[NEW]** If you are interested in ClipBERT, you might also be interested in our recent work, **Singularity** ([paper](https://arxiv.org/abs/2206.03428), [code](https://github.com/jayleicn/singularity)), with a single-frame trained model, it achieves state-of-the-art results a set of video-language tasks.
## Requirements
We provide a Docker image for easier reproduction. Please install the following:
- [nvidia driver](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#package-manager-installation) (418+),
- [Docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/) (19.03+),
- [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-docker#quickstart).
Our scripts require the user to have the [docker group membership](https://docs.docker.com/install/linux/linux-postinstall/)
so that docker commands can be run without sudo.
We only support Linux with NVIDIA GPUs. We test on Ubuntu 18.04 and V100 cards.
We use mixed-precision training hence GPUs with Tensor Cores are recommended.
## Getting Started
### General
1. Create a folder that stores pretrained models, all the data, and results.
```bash
PATH_TO_STORAGE=/path/to/your/data/
mkdir -p $PATH_TO_STORAGE/txt_db # annotations
mkdir -p $PATH_TO_STORAGE/vis_db # image and video
mkdir -p $PATH_TO_STORAGE/finetune # finetuning results
mkdir -p $PATH_TO_STORAGE/pretrained # pretrained models
```
2. Download pretrained models.
Our e2e pretrained ClipBERT model (849MB), can be downloaded with the following command.
```bash
bash scripts/download_pretrained.sh $PATH_TO_STORAGE
```
This pretrained model can be used for finetuning on video-text tasks and image-text tasks.
For your convenience, this script will also download `bert-base-uncased` and `grid-feat-vqa`
model weights, which are used as initialization for pretraining.
3. Launch the Docker container for running the experiments.
```bash
# docker image should be automatically pulled
source launch_container.sh $PATH_TO_STORAGE/txt_db $PATH_TO_STORAGE/vis_db \
$PATH_TO_STORAGE/finetune $PATH_TO_STORAGE/pretrained
```
The launch script respects $CUDA_VISIBLE_DEVICES environment variable.
Note that the source code is mounted into the container under `/clipbert` instead
of built into the image so that user modification will be reflected without
re-building the image. (Data folders are mounted into the container separately
for flexibility on folder structures.)
### Downstream Task Finetuning
#### Text-to-Video Retrieval
Tasks: MSRVTT retrieval, DiDeMo and ActivityNet Captions paragprah-to-video retrieval, MSRVTT MC Test.
1. Download data.
```bash
# outside the container
# download videos + annotations for $DSET
bash scripts/download_$DSET.sh $PATH_TO_STORAGE
```
`$DSET` can be one of `msrvtt`, `didemo`, `anet`.
2. Finetuning.
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_video_retrieval.py \
--config $CONFIG_PATH \
--output_dir $OUTPUT_DIR
# for single GPU
python src/tasks/run_video_retrieval.py \
--config $CONFIG_PATH \
--output_dir $OUTPUT_DIR
```
`$CONFIG_PATH` should be set to one of the .json config files available at [src/configs](src/configs)
prefixed with `_ret`. For example, you can use `src/configs/msrvtt_ret_base_resnet50.json`
for MSRVTT retrieval.
3. Run inference.
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_video_retrieval.py \
--do_inference 1 --output_dir $OUTPUT_DIR \
--inference_split val --inference_model_step $STEP \
--inference_txt_db $TXT_DB \
--inference_img_db $IMG_DB --inference_batch_size 64 \
--inference_n_clips $INFERENCE_N_CLIPS
```
`$STEP` is an integer, it tells the script to use the checkpoint
`$OUTPUT_DIR/ckpt/model_step_$STEP.pt` for inference.
`$TXT_DB` and `$IMG_DB` are path to annotation file and video data. You can use
`TXT_DB=/txt/downstream/msrvtt_retrieval/msrvtt_retrieval_val.jsonl` and
`IMG_DB=/img/msrvtt` for inference on MSRVTT retrieval val split.
The results will be written under `$OUTPUT_DIR`. You can use different `$INFERENCE_N_CLIPS`
for inference, such as 1 or 16. Using more clips will have a large impact
on inference speed and memory usage. You may want to use smaller batch sizes if larger
values are set.
After MSRVTT retrieval model is trained, you can use it for inference
for the MSRVTT MC Test task as well, which is essentially a retrieval
task in a multiple-choice task setup.
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_msrvtt_mc.py \
--do_inference 1 --output_dir $OUTPUT_DIR \
--inference_split val --inference_model_step $STEP \
--inference_txt_db /txt/downstream/msrvtt_retrieval_mc/msrvtt_retrieval_mc_test.jsonl \
--inference_img_db /img/msrvtt --inference_batch_size 64 \
--inference_n_clips $INFERENCE_N_CLIPS
```
#### Video Question Answering
Tasks: TGIF-QA action, transition, and frameQA tasks; MSRVTT-QA.
1. Download data.
```bash
# outside the container
# download MSRVTT videos, and QA + retrieval annotations
bash scripts/download_msrvtt.sh $PATH_TO_STORAGE
# download TGIF-QA videos and annotations
bash scripts/download_tgif_qa.sh $PATH_TO_STORAGE
```
2. Finetuning.
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_video_qa.py \
--config $CONFIG_PATH \
--output_dir $OUTPUT_DIR
```
`$CONFIG_PATH` should be set to one of the .json config files available at [src/configs](src/configs)
contains the substring `_qa`. For example, you can use `src/configs/msrvtt_qa_base_resnet50.json`
for MSRVTT-QA.
3. Run inference.
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_video_qa.py \
--do_inference 1 --output_dir $OUTPUT_DIR \
--inference_split val --inference_model_step $STEP \
--inference_txt_db $TXT_DB \
--inference_img_db $IMG_DB --inference_batch_size 64 \
--inference_n_clips $INFERENCE_N_CLIPS
```
`$STEP` is an integer, which tells the script to use the checkpoint
`$OUTPUT_DIR/ckpt/model_step_$STEP.pt` for inference.
`$TXT_DB` and `$IMG_DB` are path to annotation file and video data. You can use
`TXT_DB=/txt/downstream/msrvtt_retrieval/msrvtt_qa_val.jsonl` and
`IMG_DB=/img/msrvtt` for inference on MSRVTT QA val split.
The results will be written under `$OUTPUT_DIR`. You can use different `$INFERENCE_N_CLIPS`
for inference, such as 1 or 16. Using more clips will have a large impact
on inference speed and memory usage. You may want to use smaller batch sizes if larger
values are set.
#### Image Question Answering (VQA)
1. Download data
```bash
# outside the container
# download COCO and VG data
bash scripts/download_coco_vg.sh $PATH_TO_STORAGE
# download VQA annotations
bash scripts/download_vqa.sh $PATH_TO_STORAGE
```
2. Finetuning
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_vqa.py \
--config src/configs/vqa_base_resnet50.json \
--output_dir $OUTPUT_DIR
```
3. Inference
```bash
# inside the container
horovodrun -np 4 python src/tasks/run_vqa.py \
--do_inference 1 --output_dir $OUTPUT_DIR \
--inference_split val --inference_model_step $STEP \
--inference_txt_db $TXT_DB \
--inference_img_db $IMG_DB \
--inference_batch_size 64
```
## Pretraining
1. Download data
```bash
# outside the container
bash scripts/download_coco_vg.sh $PATH_TO_STORAGE
```
2. Pretraining
```bash
#inside the container
horovodrun -np 8 python src/pretrain/run_pretrain.py \
--config src/configs/pretrain_image_text_base_resnet50_mlm_itm.json \
--output_dir $OUTPUT_DIR
```
## Data Preprocessing
ClipBERT takes raw video and text as inputs, there is no need to do feature extraction.
However, to improve data loading speed, we use LMDB to store the raw image and video files.
You can use the following script to convert a list of videos with file extensions `mp4` and `avi` into LMDB:
```bash
# outside the container
python src/preprocessing/file2lmdb.py \
--data_root /path/to/videos \
--lmdb_save_dir /path/to/save/lmdb \
--ext avi mp4 \
--file_type video
```
For images, use appropriate file extensions for `--ext` and `--file_type image`.
Text annotation files are reorganized into `jsonl` files,
see example preprocessed files downloaded by the scripts in [scripts/](scripts).
## Citation
If you find this code useful for your research, please consider citing:
```
@inproceedings{lei2021less,
title={Less is More: ClipBERT for Video-and-Language Learningvia Sparse Sampling},
author={Lei, Jie and Li, Linjie and Zhou, Luowei and Gan, Zhe and Berg, Tamara L. and Bansal, Mohit and Liu, Jingjing},
booktitle={CVPR},
year={2021}
}
```
## Acknowledgement
We thank [Yen-Chun Chen](https://scholar.google.com/citations?user=Gptgy4YAAAAJ&hl=en),
[Ruotian Luo](https://ttic.uchicago.edu/~rluo/), and other members and interns at
[Microsoft Multimodal AI](https://multimodalai.azurewebsites.net/people/members)
for their helpful discussions.
We also thank the anonymous reviewers for their constructive feedback.
This code used resources from [transformers](https://github.com/huggingface/transformers),
[UNITER](https://github.com/ChenRocks/UNITER), [HERO](https://github.com/linjieli222/HERO),
[grid-feats-vqa](https://github.com/facebookresearch/grid-feats-vqa),
[SlowFast](https://github.com/facebookresearch/SlowFast),
[Detectron2](https://github.com/facebookresearch/detectron2).
The code is implemented using [PyTorch](https://github.com/pytorch/pytorch),
with multi-GPU support from [Horovod](https://github.com/horovod/horovod)
and mixed precision support from [apex](https://github.com/NVIDIA/apex). We thank the authors for open-sourcing their awesome projects.
## License
MIT