Get Started

Prerequisites

In this section, we demonstrate how to prepare an environment with PyTorch.

MMDetection3D works on Linux, Windows (experimental support) and macOS. It requires Python 3.7+, CUDA 10.0+, and PyTorch 1.8+.

Note

If you are experienced with PyTorch and have already installed it, just skip this part and jump to the next section. Otherwise, you can follow these steps for the preparation.

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment and activate it.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab

Step 2. Install PyTorch following official instructions, e.g.

On GPU platforms:

conda install pytorch torchvision -c pytorch

On CPU platforms:

conda install pytorch torchvision cpuonly -c pytorch

Installation

We recommend that users follow our best practices to install MMDetection3D. However, the whole process is highly customizable. See Customize Installation section for more information.

Best Practices

Step 0. Install MMEngine, MMCV and MMDetection using MIM.

pip install -U openmim
mim install mmengine
mim install 'mmcv>=2.0.0rc4'
mim install 'mmdet>=3.0.0'

Note: In MMCV-v2.x, mmcv-full is renamed to mmcv, if you want to install mmcv without CUDA ops, you can use mim install "mmcv-lite>=2.0.0rc4" to install the lite version.

Step 1. Install MMDetection3D.

Case a: If you develop and run mmdet3d directly, install it from source:

git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
# "-b dev-1.x" means checkout to the `dev-1.x` branch.
cd mmdetection3d
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in edtiable mode,
# thus any local modifications made to the code will take effect without reinstallation.

Case b: If you use mmdet3d as a dependency or third-party package, install it with MIM:

mim install "mmdet3d>=1.1.0"

Note:

1. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

2. Some dependencies are optional. Simply running pip install -v -e . will only install the minimum runtime requirements. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional]). Valid keys for the extras field are: all, tests, build, and optional.

   We have supported spconv 2.0. If the user has installed spconv 2.0, the code will use spconv 2.0 first, which will take up less GPU memory than using the default mmcv spconv. Users can use the following commands to install spconv 2.0:

   pip install cumm-cuxxx
   pip install spconv-cuxxx
   

   Where xxx is the CUDA version in the environment.

   For example, using CUDA 10.2, the command will be pip install cumm-cu102 && pip install spconv-cu102.

   Supported CUDA versions include 10.2, 11.1, 11.3, and 11.4. Users can also install it by building from the source. For more details please refer to spconv v2.x.

   We also support Minkowski Engine as a sparse convolution backend. If necessary please follow original installation guide or use pip to install it:

   conda install openblas-devel -c anaconda
   export CPLUS_INCLUDE_PATH=CPLUS_INCLUDE_PATH:${YOUR_CONDA_ENVS_DIR}/include
   # replace ${YOUR_CONDA_ENVS_DIR} to your anaconda environment path e.g. `/home/username/anaconda3/envs/openmmlab`.
   pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=/opt/conda/include" --install-option="--blas=openblas"
   

   We also support Torchsparse as a sparse convolution backend. If necessary please follow original installation guide or use pip to install it:

   sudo apt-get install libsparsehash-dev
   pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
   

   or omit sudo install by following command:

   conda install -c bioconda sparsehash
   export CPLUS_INCLUDE_PATH=CPLUS_INCLUDE_PATH:${YOUR_CONDA_ENVS_DIR}/include
   # replace ${YOUR_CONDA_ENVS_DIR} to your anaconda environment path e.g. `/home/username/anaconda3/envs/openmmlab`.
   pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
   

3. The code can not be built for CPU only environment (where CUDA isn’t available) for now.

Verify the Installation

To verify whether MMDetection3D is installed correctly, we provide some sample codes to run an inference demo.

Step 1. We need to download config and checkpoint files.

mim download mmdet3d --config pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car --dest .

The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py and hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth in your current folder.

Step 2. Verify the inference demo.

Case a: If you install MMDetection3D from source, just run the following command.

python demo/pcd_demo.py demo/data/kitti/000008.bin pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth --show

You will see a visualizer interface with point cloud, where bounding boxes are plotted on cars.

Note:

If you install MMDetection3D on a remote server without display device, you can leave out the --show argument. Demo will still save the predictions to outputs/pred/000008.json file.

Note:

If you want to input a .ply file, you can use the following function and convert it to .bin format. Then you can use the converted .bin file to run demo. Note that you need to install pandas and plyfile before using this script. This function can also be used for data preprocessing for training ply data.

import numpy as np
import pandas as pd
from plyfile import PlyData

def convert_ply(input_path, output_path):
    plydata = PlyData.read(input_path)  # read file
    data = plydata.elements[0].data  # read data
    data_pd = pd.DataFrame(data)  # convert to DataFrame
    data_np = np.zeros(data_pd.shape, dtype=np.float)  # initialize array to store data
    property_names = data[0].dtype.names  # read names of properties
    for i, name in enumerate(
            property_names):  # read data by property
        data_np[:, i] = data_pd[name]
    data_np.astype(np.float32).tofile(output_path)

Examples:

convert_ply('./test.ply', './test.bin')

If you have point clouds in other format (.off, .obj, etc.), you can use trimesh to convert them into .ply.

import trimesh

def to_ply(input_path, output_path, original_type):
    mesh = trimesh.load(input_path, file_type=original_type)  # read file
    mesh.export(output_path, file_type='ply')  # convert to ply

Examples:

to_ply('./test.obj', './test.ply', 'obj')

Case b: If you install MMDetection3D with MIM, open your python interpreter and copy&paste the following codes.

from mmdet3d.apis import init_model, inference_detector

config_file = 'pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py'
checkpoint_file = 'hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth'
model = init_model(config_file, checkpoint_file)
inference_detector(model, 'demo/data/kitti/000008.bin')

You will see a list of Det3DDataSample, and the predictions are in the pred_instances_3d, indicating the detected bounding boxes, labels, and scores.

Customize Installation

CUDA Versions

When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:

• For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.

• For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.

Please make sure the GPU driver satisfies the minimum version requirements. See this table for more information.

Note

Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA’s website, and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in conda install command.

Install MMEngine without MIM

To install MMEngine with pip instead of MIM, please follow MMEngine installation guides.

For example, you can install MMEngine by the following command:

pip install mmengine

Install MMCV without MIM

MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.

To install MMCV with pip instead of MIM, please follow MMCV installation guides. This requires manually specifying a find-url based on PyTorch version and its CUDA version.

For example, the following command install MMCV built for PyTorch 1.12.x and CUDA 11.6:

pip install "mmcv>=2.0.0rc4" -f https://download.openmmlab.com/mmcv/dist/cu116/torch1.12.0/index.html

Install on Google Colab

Google Colab usually has PyTorch installed, thus we only need to install MMEngine, MMCV, MMDetection, and MMDetection3D with the following commands.

Step 1. Install MMEngine, MMCV and MMDetection using MIM.

!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.0rc4,<2.1.0"
!mim install "mmdet>=3.0.0,<3.1.0"

Step 2. Install MMDetection3D from source.

!git clone https://github.com/open-mmlab/mmdetection3d.git -b dev-1.x
%cd mmdetection3d
!pip install -e .

Step 3. Verification.

import mmdet3d
print(mmdet3d.__version__)
# Example output: 1.1.0, or an another version.

Note

Within Jupyter, the exclamation mark ! is used to call external executables and %cd is a magic command to change the current working directory of Python.

Using MMDetection3D with Docker

We provide a Dockerfile to build an image. Ensure that your docker version >= 19.03.

# build an image with PyTorch 1.9, CUDA 11.1
# If you prefer other versions, just modified the Dockerfile
docker build -t mmdetection3d docker/

Run it with:

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection3d/data mmdetection3d

Troubleshooting

If you have some issues during the installation, please first view the FAQ page. You may open an issue on GitHub if no solution is found.

Use Multiple Versions of MMDetection3D in Development

Training and testing scripts have already been modified in PYTHONPATH in order to make sure the scripts are using their own versions of MMDetection3D.

To install the default version of MMDetection3D in your environment, you can exclude the following code in the related scripts:

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH