OPPOMKLab / u-LLaVA

u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model

u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model

Multi-modal multi task LLM
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🎉 News

• [2024/07] We will release the grounding&segmentation weights soon.
• [2024/07] ViT-336 supports, MM-Bench, TextVQA, SQA, GQA involves, coming soon (before Aug).
• [2024/07] Salient-15k is released.
• [2024/07] The work is accepted by ECAI 2024 Main Track!
• [2024/01] The code and segmentation weights are released.
• [2023/10] The paper is released.

Table of Contents

1. About The Project
   • Features
2. Results
3. Getting Started
   • Requirements
   • Datasets
   • Training
   • Evaluation
4. License
5. Citation
6. Acknowledgments

About The Project

Structure:

Examples

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Demo is coming soon.

Features

Code

• Epoch Quantitative Evaluation

  • Compute metrics

• Mixed Datasets

  • Dataset scale specification (portion)
  • Text, Image-Text, Video-Text

• DeepSpeed

• LoRA

Task

• Visual Understanding
  • Image Captioning
  • Video Captioning
  • Visual Question Answering (VQA)
• Visual Segmentation
  • Referring Expression Segmentation (RES)
  • Salient Object Segmentation
  • Semantic Segmentation
• Visual Grounding
  • Referring Expression Comprehension (REC)

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Model Release

Models	Images/Videos
u-LLaVA	uLLaVA Stage 2

RESULTS

RES

REC

SALIENT

General MLLM

Fine-tune	ScienceQA	MM-Bench	Seed-Bench
u-LLaVA-7B	87.74	soon	soon

Video QA

zero-shot	Accuracy (Type 3)
Activity-QA	51.70%

Getting Started

Requirements

Run the following commands in terminal:

pip install -r ./shells/requirements.txt
cd ./models/GroundingDINO && ./install.sh && cd ../..

Why do these?

1. install requirements: pip install -r requirements.txt
2. build cuda core for GroundingDINO: cd ./models/GroundingDINO && ./install.sh && cd ../.., if not may arise UserWarning: Failed to load custom C++ ops. Running on CPU mode Only! warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")

Datasets

Annotation download link: ullava modified annotations, LLaVA pretrain annotations and LLaVA finetuning annotaions

Image storage (download link can be found in the table):

image_root
├─ade20k
│  ├─annotations
│  └─images
├─coco2014
│  ├─test2014
│  ├─train2014
│  └─val2014
├─coco2017
│  ├─annotations
│  ├─train2017
│  └─val2017
├─cocostuff
│  ├─train2017
│  └─val2017
├─LLaVA-CC3M-Pretrain-595K
│  └─images
├─saiapr_tc-12
│  ├─00
│  └─01
└─vlpart
    ├─paco
    │  └─annotations
    └─pascal-part
        ├─Annotations_Part
        ├─examples
        └─VOCdevkit

where ade20k is extracted from ADEChallengeData2016.zip and cocostuff is extracted from stuffthingmaps_trainval2017.zip, respectively.

Stage I: Pre-training

Dataset	Images/Videos	Annotations
LLaVA CC3M	LLaVA-CC3M-Pretrain-595K/image.zip	chat.json
TGIF	TGIF - Quark Drive	tgif.json

Note: We have renamed the TGIF dataset and removed invalid samples to facilitate training, but please follow the original LICENSE.

Stage II: Fine-tuning

Dataset	Images	Annotations
LLaVA Instruction 150K	coco2017	llava_instruct_150k.json
RefCOCO	coco2014	refcoco_train.json
RefCOCOg	coco2014	refcocog_train.json
RefCOCO+	coco2014	refcoco+_train.json
RefCLEF	saiapr_tc-12	refclef_train.json
ADE20K	ade20k	ade20k.json
COCO Stuff	cocostuff	cocostuff.json
VOC2010	voc2010	pascal_part.json
PACO LVIS	paco	paco_lvis.json
Salient 15K	msra	ullava_salinet_15k.json

Note: Please download the images of MSRA-10K and MSRA-B from the official site, thanks the authors for sharing.

Dataset config example

dataset:
  llava:
    data_type: 'image'
    image_token_len: 256
    build_info:
      anno_dir: '/path_to_annotations/llava_instruct_150k.json'
      image_dir: '/path_to_image_root/coco2017/train2017'
      portion: 1.0
    vis_processor: 'clip_image'

  refcoco+:
    data_type: 'image'
    image_token_len: 256
    build_info:
      anno_dir: '/path_to_annotations/refcoco+_train.json'
      image_dir: '/path_to_image_root/coco2014'
      template_root: './datasets/templates/SEG.json'
      portion: 1.0
    vis_processor: 'clip_image'

Note:

1. We re-organize most of the dataset annotations for easier training, but all of us must follow the rules that the original datasets require.

Training

Stage I: Pre-training

1. Prepare Open-Source LLaMA models

Foundation model	Version	Path
Vicuna 7B HF	V1.1	vicuna_7b_v1.1
LLaMA2 7B HF	-	meta-llama/Llama-2-7b-hf
SAM	ViT-H	sam_vit_h_4b8939.pth
GroundingDINO	swint_ogc	groundingdino_swint_ogc.pth

Note:

- LLaMA2 is trained with bf16, convergence error may happen when stage 1 training with fp16.

- The default tokenizer.legacy of Llama-2 is False, and may rise tokenization mismatch error with some conversation template.

- Errata: The base LLM used in the paper is Vicuna-v1.1, not LLaMA2. Sorry about the mistake.

2. Prepare datasets
3. Set config in

configs/train/ullava_core_stage1.yaml

Note set all datasets path or output path according to your experiments. 4. Train Stage I with multi GPUs

./shells/pretrain.sh

or python train_ullava_core.py --cfg_path './configs/train/ullava_core_stage1.yaml' for 1 GPU.

The first stage with 4 A100 80G with bf16 costs ~6hours for 1 epoch. Then you can find the trained model at the output_dir, for example, './exp/ullava_core_7b'

Stage II: Fine-tuning

After Stage I training finished, we can go through the following step, that is, fine-tuning.

1. Prepare datasets
2. Set config in

configs/train/ullava_stage2_lora.yaml (for lora)
configs/train/ullava_stage2.yaml (for non lora)

3. Train Stage II with multi GPUs

./shells/finetune.sh

or python train_ullava.py --cfg_path './configs/train/ullava_stage2_lora.yaml' for 1 GPU.

Common Question

Q1: What conv_tpye used in training?

A1: Stage I: 'conv_simple'. Stage II: 'conv_sep2'

Q2: When LoRA used?

A2: Stage I: We have not used in this stage. Stage II: According to your devices.

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Evaluation

Batch evaluation

1. Set config

configs/eval/eval_res.ymal (for RES task)
configs/eval/eval_rec.ymal (for REC task)
configs/eval/eval_salient.ymal (for Salinet segmentation task)

2. Run

python evaluation/eval_ullava.py --cfg_path './configs/eval/eval_res.yaml' (for RES)
python evaluation/eval_ullava_grounding.py --cfg_path './configs/eval/eval_rec.yaml' (for REC)
python evaluation/eval_ullava.py --cfg_path './configs/eval/eval_salient.yaml' (for Salinet)

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Qualitative inference

Modify the parser in the evaluation/inference_ullava_core.py and evaluation/inference_ullava.py for stage I and stage II, respectively.

python evaluation/eval_ullava.py
python evaluation/eval_ullava_grounding.py 

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License

Distributed under the Apache License. See LICENSE for more information.

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Citation

@inproceedings{xu2024ullava,
  title={u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model},
  author={Xu, Jinjin and Xu, Liwu and Yang, Yuzhe and Li, Xiang and Wang, Fanyi and Xie, Yanchun and Huang, Yi-Jie and Li, Yaqian},
  booktitle={Proceedings of the 27th European Conference on Artificial Intelligence},
  year={2024}
}

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TODO

• Visual Segmentation
  • Instance Segmentation

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Acknowledgments

We sincerely thank the open source community for their contributions. And this work is sponsored by Shanghai Pujiang Program (23PJ1421800).

• LLaVA
• NExT-Chat
• LISA
• VideoLLaMA
• Shikra
• SAM
• GroundingDINO

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See the open issues for a full list of proposed features (and known issues).

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