THE MOST THANKFUL THINGS

https://github.com/StanfordASL/Trajectron-plus-plus.

Without this repo, I could not complete my whole project!

All of my project code is based on this repo.

Directory

The Directory of our project.

${Trajectron root}
├── data
├── experiments
├── Transformer
├── README.md
└── requirements.txt

Data preparation

For dataset please download into the direct Directory.

The setting of nuScenes dataset is quite complicate. So we zip the FULL Dataset and store in nas, you can downloaded by the command below or download by official website https://www.nuscenes.org/. Then unzip the file at correspond folder.

${Trajectron root}
├── data
    `-- ├── sets --> unzip here!

Create directory :

mkdir -p ~/data/sets

Download nuScene dataset:

wget --no-check-certificate -O dataset.tar.xz "https://140.115.54.247/share.cgi?ssid=29b79213862b4827b5e6bacfcd784041&fid=29b79213862b4827b5e6bacfcd784041&filename=nuscenes.tar.xz&openfolder=forcedownload&ep=="

Create Training Environment

For the following process, we first need to create a fresh environment, and there are two method to choose conda or virtualenv.

For virtualenv

Notice!! /usr/bin/python3.6 is default path, please modify the path according to your Environment!

virtualenv -p /usr/bin/python3.6 py3.6_env 
source py3.6_env/bin/activate
pip install -r requirements_3.6.txt

For conda

conda create --name Fusion_trans python=3.6 -y
source activate Fusion_trans
pip install -r requirements.txt

Install devkit for dataset

For nuScenes devkit.

pip install nuscenes-devkit

Training Progress

Data Preprocessing

${Trajectron root}
`-- experiments
    `--nuScene

To preprocess the nuScenes data, you can execute the command below in nuScene directory, depending on the data version you desire.

Typo preprocess	Command
Map preprocess	python3 nuScenes_process_data.py --data=../../data/sets/nuscenes/v1.0/ --version=v1.0-trainval --output_path=../processed_data
Lane preprocess	python3 nuScenes_process_data.py --data=../../data/sets/nuscenes/v1.0/ --version=v1.0-trainval --output_path=../processed_data --lane_process

Training

${Trajectron root}
├── Transformer

To train a model with nuScene data, you can execute the command below in Transformer directory, depending on the model version you desire.

Note that the device parameter depends on your machine if machine don't have gpu use "cpu".

Model	Command
Basic	python3 train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScene/config/config.json --data_name nuScenes --preprocess_workers 8 --batch_size 128 --log_dir ../experiments/nuScene/models --train_epochs 20 --node_freq_mult_train --log_tag basic_transformer --augment --autoregressive --device "cuda:1"
Map_encoding_CNN	python3 train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScene/config/config.json --data_name nuScenes --preprocess_workers 8 --batch_size 128 --log_dir ../experiments/nuScene/models --train_epochs 20 --node_freq_mult_train --log_tag CNN_transformer --augment --autoregressive --map_cnn_encoding --device "cuda:1"
Map_encoding_ViT	python3 train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScene/config/config.json --data_name nuScenes --preprocess_workers 8 --batch_size 128 --log_dir ../experiments/nuScene/models --train_epochs 20 --node_freq_mult_train --log_tag ViT_transformer --augment --autoregressive --map_vit_encoding --device "cuda:1"
Lane_encoding_CNN	python3 train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScene/config/config.json --data_name nuScenes --preprocess_workers 8 --batch_size 128 --log_dir ../experiments/nuScene/models --train_epochs 20 --node_freq_mult_train --log_tag Lane_transformer --augment --autoregressive --lane_cnn_encoding --device "cuda:1"

Evaluate Progress

${Trajectron root}
├── experiments
    `-- nuScene

If you want to evaluate the trained model to generate trajectories and plot them, you can use NuScenes Qualitative.ipynb norebook.

To evaluate a trained model's performance on forecasting vehicles, you can execute a one of the following commands from within the experiments/nuScenes directory. 1. that the testing dataset will be slightly different between map and lane we filter more data in lane.

2. Lane encoding CNN can not use map data but other method could use both data to evaluate.

Model	Command
Basic	python evaluate.py --model models/basic_transformer --checkpoint=20 --data ../processed_data/nuScenes_test_map_full.pkl --output_path results --output_tag basic_transformer --node_type VEHICLE --prediction_horizon 12
Map_encoding_CNN	python evaluate.py --model models/CNN_transformer --checkpoint=20 --data ../processed_data/nuScenes_test_map_full.pkl --output_path results --output_tag CNN_transformer --node_type VEHICLE --prediction_horizon 12
Map_encoding_ViT	python evaluate.py --model models/ViT_transformer --checkpoint=20 --data ../processed_data/nuScenes_test_map_full.pkl --output_path results --output_tag ViT_transformer --node_type VEHICLE --prediction_horizon 12
Lane_encoding_CNN	python evaluate.py --model models/Lane_transformer --checkpoint=20 --data ../processed_data/nuScenes_test_lane_full.pkl --output_path results --output_tag Lane_transformer --node_type VEHICLE --prediction_horizon 12

TODO

• Rename Folders and command.
• Debug Lane preprocess.
• Coding plot code for evaluation.
• Update the data link with new zip file.
• Update the requirment txt to the latest version.
• Update the python version.
• Update the code structure :
  • Remove unused part.(Pedestrian)
• Integrate Unit test to repo.