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BigData-KSU/
RS-llava-v1.5-7b-LoRA

Name: RS-llava-v1.5-7b-LoRA
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RS-LLaVA: Large Vision Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery

Repository: https://github.com/BigData-KSU/RS-LLaVA
Paper: https://www.mdpi.com/2072-4292/16/9/1477
Demo: Soon.

How to Get Started with the Model

Install

Clone this repository and navigate to RS-LLaVA folder

git clone https://github.com/BigData-KSU/RS-LLaVA.git
cd RS-LLaVA

Install Package

conda create -n rs-llava python=3.10 -y
conda activate rs-llava
pip install --upgrade pip  # enable PEP 660 support

Install additional packages

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers==4.35
pip install einops
pip inastall SentencePiece
pip install accelerate
pip install peft

Inference

Use the code below to get started with the model.

python

import torch
import os
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import math

######## model here.................
model_path = 'BigData-KSU/RS-llava-v1.5-7b-LoRA'

model_base = 'Intel/neural-chat-7b-v3-3'

#### Further instrcutions here..........
conv_mode = 'llava_v1'
disable_torch_init()

model_name = get_model_name_from_path(model_path)
print('model name', model_name)
print('model base', model_base)


tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name)


def chat_with_RS_LLaVA(cur_prompt,image_name):
    # Prepare the input text, adding image-related tokens if needed
    image_mem = Image.open(image_name)
    image_tensor = image_processor.preprocess(image_mem, return_tensors='pt')['pixel_values'][0]

    if model.config.mm_use_im_start_end:
        cur_prompt = f"{DEFAULT_IM_START_TOKEN} {DEFAULT_IMAGE_TOKEN} {DEFAULT_IM_END_TOKEN}\n{cur_prompt}"
    else:
        cur_prompt = f"{DEFAULT_IMAGE_TOKEN}\n{cur_prompt}"

    # Create a copy of the conversation template
    conv = conv_templates[conv_mode].copy()
    conv.append_message(conv.roles[0], cur_prompt)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()

    # Process image inputs if provided
    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0) .cuda()
    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor.unsqueeze(0).half().cuda(),
            do_sample=True,
            temperature=0.2,
            top_p=None,
            num_beams=1,
            no_repeat_ngram_size=3,
            max_new_tokens=2048,
            use_cache=True)

    input_token_len = input_ids.shape[1]
    n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
    if n_diff_input_output > 0:
        print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
    outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
    outputs = outputs.strip()

    return outputs


if __name__ == "__main__":


    print('Model input...............')
    cur_prompt='Generate three questions and answers about the content of this image. Then, compile a summary.'
    image_name='assets/example_images/parking_lot_010.jpg'


    outputs=chat_with_RS_LLaVA(cur_prompt,image_name)
    print('Model Response.....')
    print(outputs)

Training Details

Training RS-LLaVa is carried out in three stages:

Stage 1: Pretraining (Feature alignment) stage:

Using LAION/CC/SBU BLIP-Caption Concept-balanced 558K dataset, and two RS datasets, NWPU and RSICD.

Dataset	Size	Link
CC-3M Concept-balanced 595K	211 MB	Link
NWPU-RSICD-Pretrain	16.6 MB	Link

Stage 2: Visual Instruction Tuning:

To teach the model to follow instructions, we used the proposed RS-Instructions Dataset plus LLaVA-Instruct-150K dataset.

Dataset	Size	Link
RS-Instructions	91.3 MB	Link
llava_v1_5_mix665k	1.03 GB	Link

Stage 3: Downstram Task Tuning:

In this stage, the model is fine-tuned on one of the downstream tasks (e.g., RS image captioning or VQA)

Citation

BibTeX:

bibtex

@Article{rs16091477,
AUTHOR = {Bazi, Yakoub and Bashmal, Laila and Al Rahhal, Mohamad Mahmoud and Ricci, Riccardo and Melgani, Farid},
TITLE = {RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery},
JOURNAL = {Remote Sensing},
VOLUME = {16},
YEAR = {2024},
NUMBER = {9},
ARTICLE-NUMBER = {1477},
URL = {https://www.mdpi.com/2072-4292/16/9/1477},
ISSN = {2072-4292},
DOI = {10.3390/rs16091477}
}