How to Implement UniTR: A Unified Multi-modal Transformer for 3D Perception

In the rapidly evolving world of artificial intelligence, handling data from diverse sensors is crucial for achieving reliable and robust autonomous driving systems. The UniTR – Unified Multi-modal Transformer, is a breakthrough approach that efficiently integrates varying modalities such as Cameras and LiDARs, exhibiting state-of-the-art performance. This article serves as a user-friendly guide for implementing UniTR, along with troubleshooting tips to tackle common challenges.

Overview of the UniTR Model

The UniTR leverages a weight-sharing multi-modal backbone, revolutionizing the way we approach 3D perception tasks. Imagine you’re planning a dinner party; you wouldn’t prepare each dish separately and then try to coordinate service times, right? Instead, you’d use a unified approach. Similarly, UniTR processes different sensing modalities—reducing overhead and enhancing coordination between data streams. This results in more efficient processing and improved accuracy.

Quick Start Guide

To get started with the UniTR implementation, follow these essential steps:

Installation

• First, create an environment:

conda create -n unitr python=3.8

• Install PyTorch (adjust version to suit your system):

pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 -f https://download.pytorch.org/whl/torch_stable.html

• Clone the UniTR repository:

git clone https://github.com/Haiyang-W/UniTR cd UniTR

• Install extra dependencies:

pip install -r requirements.txt

• Finally, install the NuScenes development kit:

pip install nuscenes-devkit==1.0.5

Dataset Preparation

To utilize the NuScenes dataset, you need to download it and organize the downloaded files in the prescribed format. Here’s how:

• Download the official NuScenes 3D object detection dataset.
• Organize the files accordingly (detailed structure provided in the README).
• Generate data information using the following command:

python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos --cfg_file tool/cfgs/dataset_configs/nuscenes_dataset.yaml --version v1.0-trainval --with_cam --with_cam_gt

Training and Testing

The training process requires specific scripts. Here’s an example to train the 3D object detection model:

cd tools bash scripts/dist_train.sh 8 --cfg_file cfgs/nuscenes_models/unitr.yaml --sync_bn --pretrained_model ../unitr_pretrain.pth --logger_iter_interval 1000

Similarly, execute the appropriate testing scripts to evaluate model performance.

Troubleshooting Tips

While working with UniTR, you may encounter some issues. Here are some common ones along with solutions:

• If your model gradients become NaN during FP16 training, this could be an unresolved support issue. Make sure you’re using the correct software versions.
• If you face difficulties, check the open and closed issues on our GitHub issues page for potential solutions.
• To avoid slow training due to constant recalculations during training, consider utilizing cache mode for samples where camera and LiDAR parameters remain consistent.
• If you’re still experiencing issues, feel free to open a new issue in our GitHub repository. Our average turnaround time for addressing inquiries is a couple of days.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.