# second.pytorch

**Repository Path**: leox24/second.pytorch

## Basic Information

- **Project Name**: second.pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-08-13
- **Last Updated**: 2022-10-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PointPillars

Welcome to PointPillars.

This repo demonstrates how to reproduce the results from
[_PointPillars: Fast Encoders for Object Detection from Point Clouds_](https://arxiv.org/abs/1812.05784) (to be published at CVPR 2019) on the
[KITTI dataset](http://www.cvlibs.net/datasets/kitti/) by making the minimum required changes from the preexisting
open source codebase [SECOND](https://github.com/traveller59/second.pytorch). 

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](https://github.com/traveller59/second.pytorch). We are not the owners of the repository, but we have worked with the author and endorse his code.**

![Example Results](https://raw.githubusercontent.com/nutonomy/second.pytorch/master/images/pointpillars_kitti_results.png)


## Getting Started

This is a fork of [SECOND for KITTI object detection](https://github.com/traveller59/second.pytorch) and the relevant
subset of the original README is reproduced here.

### Code Support

ONLY supports python 3.6+, pytorch 0.4.1+. Code has only been tested on Ubuntu 16.04/18.04.

### Install

#### 1. Clone code

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

#### 2. Install Python packages

It is recommend to use the Anaconda package manager.

First, use Anaconda to configure as many packages as possible.
```bash
conda create -n pointpillars python=3.7 anaconda
source activate pointpillars
conda install shapely pybind11 protobuf scikit-image numba pillow
conda install pytorch torchvision -c pytorch
conda install google-sparsehash -c bioconda
```

Then use pip for the packages missing from Anaconda.
```bash
pip install --upgrade pip
pip install fire tensorboardX
```

Finally, install SparseConvNet. This is not required for PointPillars, but the general SECOND code base expects this
to be correctly configured. 
```bash
git clone git@github.com:facebookresearch/SparseConvNet.git
cd SparseConvNet/
bash build.sh
# NOTE: if bash build.sh fails, try bash develop.sh instead
```

Additionally, you may need to install Boost geometry:

```bash
sudo apt-get install libboost-all-dev
```


#### 3. Setup cuda for numba

You need to add following environment variables for numba to ~/.bashrc:

```bash
export NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so
export NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so
export NUMBAPRO_LIBDEVICE=/usr/local/cuda/nvvm/libdevice
```

#### 4. PYTHONPATH

Add second.pytorch/ to your PYTHONPATH.

### Prepare dataset

#### 1. Dataset preparation

Download KITTI dataset and create some directories first:

```plain
└── 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/```.

#### 2. Create kitti infos:

```bash
python create_data.py create_kitti_info_file --data_path=KITTI_DATASET_ROOT
```

#### 3. Create reduced point cloud:

```bash
python create_data.py create_reduced_point_cloud --data_path=KITTI_DATASET_ROOT
```

#### 4. Create groundtruth-database infos:

```bash
python 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:

```bash
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

```bash
cd ~/second.pytorch/second
python ./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


```bash
cd ~/second.pytorch/second/
python 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.