kentanabe / second.pytorch

PointPillars for KITTI object detection

PointPillars

Welcome to PointPillars.

This repo demonstrates how to reproduce the results from PointPillars: Fast Encoders for Object Detection from Point Clouds (to be published at CVPR 2019) on the KITTI dataset by making the minimum required changes from the preexisting open source codebase SECOND.

This is not an official nuTonomy codebase, but it can be used to match the published PointPillars results.

WARNING: This code is not being actively maintained. This code can be used to reproduce the results in the first version of the paper, https://arxiv.org/abs/1812.05784v1. For an actively maintained repository that can also reproduce PointPillars results on nuScenes, we recommend using SECOND. We are not the owners of the repository, but we have worked with the author and endorse his code.

Getting Started

This is a fork of SECOND for KITTI object detection and the relevant subset of the original README is reproduced here.

Docker Environments

If you do not waste time on pointpillars envs, please pull my docker virtual environments :

Jetson Xavier NX

docker pull tanabeken/public:pointpillars.ubuntu18.04.arm64.cuda10_2.jetson-xaviernx.20200715

AMD64(x86_64) PC with NVIDIA GPU

T.B.D.

Code Support

ONLY supports python 3.7, pytorch 1.5.0. Code has only been tested on Ubuntu 18.04.

Install

1. Clone code

git clone https://github.com/nutonomy/second.pytorch.git

2. Install Python packages

It is recommend to use the Anaconda package manager.

First, install packages via apt.

sudo apt-get update
sudo apt-get install -y --no-install-recommends \
	build-essential \
	ca-certificates \
	cmake \
	wget \
	curl \
	git \
	bash \
	vim \
	libsparsehash-dev \
	make \
	llvm-9-dev \
	libboost-all-dev \
	libgeos-dev \
	time
sudo apt-get install -y --no-install-recommends \
	software-properties-common
sudo apt-get install -y --no-install-recommends \
	software-properties-common
        python3 \
        python3-dev \
        python3-pip \
	libfreetype6-dev \
	libpng-dev

Then use pip for python packages.

python3 -m pip --no-cache-dir install --upgrade --user \
	-U pip
python3 -m pip --no-cache-dir install --upgrade --user \
	setuptools
python3 -m pip --no-cache-dir install --upgrade --user \
	numpy \
        scipy \
        matplotlib \
	Cython
LLVM_CONFIG=/usr/lib/llvm-9/bin/llvm-config \
	python3 -m pip --no-cache-dir install --upgrade --user \
	        fire \
		numba==0.43.1 \
		pillow==7.1.2 \
		protobuf \
	        pybind11 \
	        scikit-image==0.16.2 \
	        shapely \
		sparsehash \
	        tensorboardX

Finally, install SparseConvNet. This is not required for PointPillars, but the general SECOND code base expects this to be correctly configured.

git clone https://github.com/kentanabe/SparseConvNet.git
cd ./SparseConvNet
git pull
git checkout a0b9bbbbee335eeb98aba55377b8835bfde1fb26
python3 setup.py install
cd ..
rm -rf SparseConvNet

4. PYTHONPATH

Add second.pytorch/ to your PYTHONPATH.

export PYTHONPATH=${HOME}/second.pytorch:${PYTHONPATH}

Prepare dataset

1. Dataset preparation

Download KITTI dataset and create some directories first:

└── KITTI_DATASET_ROOT
       ├── training    <-- 7481 train data
       |   ├── image_2 <-- for visualization
       |   ├── calib
       |   ├── label_2
       |   ├── velodyne
       |   └── velodyne_reduced <-- empty directory
       └── testing     <-- 7580 test data
           ├── image_2 <-- for visualization
           ├── calib
           ├── velodyne
           └── velodyne_reduced <-- empty directory

Note: PointPillar's protos use KITTI_DATASET_ROOT=/data/sets/kitti_second/.

export KITTI_DATASET_ROOT=/data/sets/kitti_second/

2. Create kitti infos:

python3 create_data.py create_kitti_info_file --data_path=${KITTI_DATASET_ROOT}

3. Create reduced point cloud:

python3 create_data.py create_reduced_point_cloud --data_path=${KITTI_DATASET_ROOT}

4. Create groundtruth-database infos:

python3 create_data.py create_groundtruth_database --data_path=${KITTI_DATASET_ROOT}

5. Modify config file

The config file needs to be edited to point to the above datasets:

train_input_reader: {
  ...
  database_sampler {
    database_info_path: "/path/to/kitti_dbinfos_train.pkl"
    ...
  }
  kitti_info_path: "/path/to/kitti_infos_train.pkl"
  kitti_root_path: "KITTI_DATASET_ROOT"
}
...
eval_input_reader: {
  ...
  kitti_info_path: "/path/to/kitti_infos_val.pkl"
  kitti_root_path: "KITTI_DATASET_ROOT"
}

Train

cd ~/second.pytorch/second
python3 -W ignore::UserWarning ./pytorch/train.py train --config_path=./configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir

• If you want to train a new model, make sure "/path/to/model_dir" doesn't exist.
• If "/path/to/model_dir" does exist, training will be resumed from the last checkpoint.
• Training only supports a single GPU.
• Training uses a batchsize=2 which should fit in memory on most standard GPUs.
• On a single 1080Ti, training xyres_16 requires approximately 20 hours for 160 epochs.

Evaluate

cd ~/second.pytorch/second/
python3 -W ignore::UserWarning pytorch/train.py evaluate --config_path= configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir

• Detection result will saved in model_dir/eval_results/step_xxx.
• By default, results are stored as a result.pkl file. To save as official KITTI label format use --pickle_result=False.

Build Docker image

1. Set nvidia docker runtime as default to use cuda tools from docker build.

*** daemon.json~        2018-03-07 13:06:38.000000000 +0900
--- daemon.json 2020-06-28 14:55:33.341543078 +0900
***************
*** 1,4 ****
--- 1,5 ----
  {
+     "default-runtime": "nvidia",
      "runtimes": {
          "nvidia": {
              "path": "nvidia-container-runtime",

sudo service docker restart

2. Build docker image

Jetson Xavier NX

docker build -t <TAG NAME> docker/pointpillars.18.04.arm64.cuda10_2/

AMD64(x86_64) PC with NVIDIA GPU

docker build -t <TAG NAME> docker/pointpillars.18.04.amd64.cuda10_2/

3. Add swap for Jetson Xavier NX

sudo dd if=/dev/zero of=/swap bs=1G count=8
sudo chmod 600 /swap
sudo mkswap /swap
sudo swapon /swap

Modify fstab

sudo sh -c "echo '/swap none swap sw 0 0' >> /etc/fstab"

Disable swappiness

sudo sh -c "echo 'vm.swappiness=0' >> /etc/sysctl.conf"
sudo sysctl -p

4. Run docker image

docker run --gpus all --network host \
    -it -e DISPLAY=$DISPLAY \
    -v /tmp/.X11-unix/:/tmp/.X11-unix \
    -v <KITTI_DATASET_ROOT>:/data/sets/kitti_second/ \
    -v ${HOME}/pointpillars/model:/root/model \
    <TAG NAME>