Getting Started with V2X-ViT: Vehicle-to-Everything Cooperative Perception

The V2X-ViT project is an impressive stride toward enhancing vehicle-to-everything (V2X) cooperative perception through the utilization of the Vision Transformer (ViT) framework. This blog will guide you through the installation, configuration, and training processes associated with this innovative system, all while remaining user-friendly.

Step 1: Installation

First and foremost, let’s get your environment set up for V2X-ViT.

• Clone the Repository:

git clone https://github.com/DerrickXuNu/v2x-vit cd v2x-vit

• Create a Conda Environment:

conda create -y --name v2xvit python=3.7 conda activate v2xvit

• Install PyTorch (Need version 1.8.1 or newer):

conda install -y pytorch torchvision cudatoolkit=11.3 -c pytorch

• Install spconv:

pip install spconv-cu113

• Install Dependencies:

pip install -r requirements.txt

• Install BBX NMS Calculation:

python v2xvit/utils/setup.py build_ext --inplace

• Install V2X-ViT into the Environment:

python setup.py develop

Step 2: Data Downloading and Preparation

Next, you’ll need to gather the data necessary for training and validation.

• Download the dataset from this URL. It’s a large file, so you might prefer to download the chunks individually using:

cat train.zip.part* > train.zip unzip train.zip

• Ensure that the file structure is organized as follows:
• shv2x-vit
• v2xset
• train
• validate
• test
• v2xvit

Step 3: Visualize Data Sequence

To visualize the LiDAR stream in the V2XSet dataset:

• Modify validate_dir in your v2xvit/hypes_yaml/visualization.yaml to point to the V2XSet data path.
• Run the following command:

python v2xvit/visualization/vis_data_sequence.py [--color_mode $COLOR_RENDERING_MODE]

• Arguments Explanation:
  • color_mode: Choose from constant, intensity, or z-value for LiDAR rendering.

Step 4: Model Testing and Training

Testing the pretrained model is crucial before embarking on training:

• Download the model file from Google Drive and place it in the v2x-vit/logs/v2x-vit directory.
• Modify validate_path in your configuration file to v2xset/test and set relevant parameters.
• Run the test command:

python v2xvit/tools/inference.py --model_dir $CHECKPOINT_FOLDER --fusion_method $FUSION_STRATEGY [--show_vis] [--show_sequence]

• Adjust the parameters as necessary for your testing environment.

Step 5: Troubleshooting

While working with V2X-ViT, you might encounter some issues. Here are a few troubleshooting tips:

• Ensure that all dependencies are installed correctly.
• Check that your CUDA version matches the specifications outlined for both PyTorch and spconv.
• If you encounter data loading errors, revisit your data structure and confirm the paths are correct.
• For any unresolved issues, consult the project’s documentation or community forums for assistance.

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

Final Thoughts

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.

With these steps, you should be well on your way to utilizing V2X-ViT for your vehicle-to-everything cooperative perception projects. Embrace this exciting technology and tap into the world of autonomous driving!