kth-rpl / drivecmd_llm Goto Github PK

Task: In-Cabin User Command Understanding (UCU), workshop in WACV2024

Here is our solution code. Please check the report for more detail.

Here we introduced how to setup and the way to downloaded their model:

1. Send request to their form Apply here in Meta official page and you will receive an email with some details.

2. Dependencies: sudo apt install wget ucommon-utils

3. run ./scripts/llama/download.sh to download Llama models.

4. mamba create --name llc python=3.8 && mamba activate llc && pip install -r requirements.txt

5. Run the example:

   torchrun --nproc_per_node 1 scripts/main_codellama.py

copy your OPENAI_API_KEY and save it in .env. OPENAI_API_KEY='xxxx'

install below

pip install openai
pip install -U python-dotenv

Run the example:

python scripts/main_gpt.py --provide_few_shots True --step_by_step True

You will have a result .json file finally. Then run the eval.py For each task-level accuracy:

python scripts/eval.py -g assets/ucu.csv -e assets/result/test.json

Here is the demo output:

Evaluating assets/result/gpt-4_best.json ...
| Task                |   Accuracy |
|---------------------+------------|
| Perception          |   0.931756 |
| In-cabin monitoring |   0.748863 |
| Localization        |   0.915378 |
| Vehicle control     |   0.88626  |
| Entertainment       |   0.944495 |
| Personal data       |   0.859873 |
| Network access      |   0.919927 |
| Traffic laws        |   0.915378 |
| Overall             |   0.890241 |

Here is official evaluate with -o:

python3 scripts/llvm_ad/official_eval.py -o -g assets/ucu.csv -e assets/result/gpt4_best.csv

We attach the raw .json output files (with explainations and output), and its corresponding .cvs files (binary output only) under assets/result folder.

Here is demo output:

Since the input file is .json, we save the prediction to .csv file at assets/result/gpt-4_best.csv
Evaluating assets/result/gpt-4_best.json ...
Following is the evaluation result in official way:

Command-level acc: 0.38034576888080074
Question-level acc: 0.8902411282984531

This work was funded by Vinnova, Sweden (research grant). The computations were enabled by the supercomputing resource Berzelius provided by National Supercomputer Centre at Linköping University and the Knut and Alice Wallenberg foundation, Sweden.

This implementation is based on codes from several repositories. Thanks for these authors who kindly open-sourcing their work to the community. Please see our paper reference part to get more information.

❤️: llvm-ad, llama2

@article{yi2023drivecmdllm,
  author={Yang, Yi and Zhang, Qingwen and Li, Ci and Simões Marta, Daniel and Batool, Nazre and Folkesson, John},
  title={Human-Centric Autonomous Systems With LLMs for User Command Reasoning},
  journal={arXiv preprint arXiv:2311.08206},
  year={2023}
}