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This repository is for task-incremental-learning in LiDAR-based 3D object detection.

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# README

This repository is for task-incremental-learning in LiDAR-based 3D object detection.

## Requirements

- python 3.7.3
- CUDA 10.1+
- PyTorch 1.4.0+
- Open3D 0.9
- [det3](https://github.com/pyun-ram/det3)

## Dockerfile

```
# e.g. CUDA10.1
cd Dockerfiles
docker build ./peng-incdet3-pytorch1.4_cu10.1_spconv -t <dockerimage-tag>/incdet3:latest
docker run -it --name incdet3 --gpus all -p 8001:8888 -v <root-dir>:/usr/app <dockerimage-tag>/incdet3:latest
# inside your docker container, run
cd /usr/app/incdet3
./run_scripts/setup.sh
```

## Usage

The run_scripts provide the shell scripts of exp-kitti-2+3 and exp-kitti-2+seq.
You can run it inside the docker container for both training and evaluation.

You can download our [weights](http://gofile.me/4jm56/DbSl0zfvs) for reproducing the experimental results. The steps are:

```
1.Download weights from the http://gofile.me/4jm56/DbSl0zfvs

2.Unzip it in the saved_weights/collections/

3.Run the scripts in the docker container, like
./run_scripts/exp-kitti-2+seq/eval_on_old_tasks.sh 0 # Use GPUID=0
./run_scripts/exp-kitti-2+seq/eval_on_new_tasks.sh 0 # Use GPUID=0
./run_scripts/exp-kitti-2+seq/eval_on_all_tasks.sh 0 # Use GPUID=0

4.Collect the results using the jupyter script: 
jupyterscripts/20210118-collect_eval_results_kitti.ipynb

```

The **run_scripts/** also provides the running scripts for training models with different TIL schemes.

Here we provide some representative examples.

e.g. Train task-1..2 ("Car", "Pedestrian"), task-3 ("Cyclist"), task-4 ("Van"), task-5("Truck) in sequence with the finetune scheme.
```
# Download the well-trained weight of task-1..2
./run_scripts/exp-kitti-2+seq/download_weights.sh

# Incremental learning (You may need to put the checkpoints into appropriate directories, if it raises an error of FileNotFound.)
./run_scripts/exp-kitti-2+seq/finetuning3.sh 0
./run_scripts/exp-kitti-2+seq/finetuning4.sh 0
./run_scripts/exp-kitti-2+seq/finetuning5.sh 0
```

e.g. Train task-1..2 ("Car", "Pedestrian"), task-3 ("Cyclist"), task-4 ("Van"), task-5("Truck) in sequence with the C-KD(MAS) scheme.
```
# Download the well-trained weight of task-1..2
./run_scripts/exp-kitti-2+seq/download_weights.sh

# Incremental learning (You may need to put the checkpoints into appropriate directories, if it raises an error of FileNotFound.)
./run_scripts/exp-kitti-2+seq/kdmas3.sh 0
./run_scripts/exp-kitti-2+seq/compute_mas_weights_kdmas3.sh 0
./run_scripts/exp-kitti-2+seq/kdmas4.sh 0
./run_scripts/exp-kitti-2+seq/compute_mas_weights_kdmas4.sh 0
./run_scripts/exp-kitti-2+seq/kdmas5.sh 0
./run_scripts/exp-kitti-2+seq/compute_mas_weights_kdmas5.sh 0
```

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