VitaminQRQ/VectorNet_NuPlan

Still unfinished

Implement VectorNet using the NuPlan Dataset

This guide outlines the steps to implement VectorNet using the NuPlan dataset.

Requirements

In addition to the dependencies required by the NuPlan-devkit, the only additional library needed is pyG. It is recommended to use torch_geometric version 2.0.3.

Installation Steps:

1. Download the .whl files for pyG from this link.
2. Choose the files tagged with cp39-linux and the appropriate version for your setup.
3. Install the downloaded .whl files using pip:

   pip install <path_to_downloaded_file>.whl

4. Install torch_geometric:

   pip install torch_geometric==2.0.3

Workflow

Step 1: Process Data for Training

Run the following command to prepare the data for training:

python3 process_data.py

Step 2: Train VectorNet

To start training VectorNet, execute:

python3 train.py

Step 3: Visualize the Prediction Result

Follow the instructions in visual.ipynb to visualize the prediction results. The visualization notebook allows you to see the outcomes interactively within a Jupyter environment.

Additional Information

• Configuration settings can be modified in utils/config.py.
• Example of a prediction result: