Prerequisites¶

In this section we demonstrate how to prepare an environment with PyTorch. MMDection3D works on Linux, Windows (experimental support) and macOS and requires the following packages:

Python 3.6+
PyTorch 1.3+
CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
GCC 5+
MMCV

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¶

Assuming that you already have CUDA 11.0 installed, here is a full script for quick installation of MMDetection3D with conda. Otherwise, you should refer to the step-by-step installation instructions in the next section.

pip install openmim
mim install mmcv-full
mim install mmdet
mim install mmsegmentation
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -e .

Step 0. Install MMCV using MIM.

Step 1. Install MMDetection.

pip install mmdet

Optionally, you could also build MMDetection from source in case you want to modify the code:

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
git checkout v2.24.0  # switch to v2.24.0 branch
pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

Step 2. Install MMSegmentation.

pip install mmsegmentation

Optionally, you could also build MMSegmentation from source in case you want to modify the code:

git clone https://github.com/open-mmlab/mmsegmentation.git
cd mmsegmentation
git checkout v0.20.0  # switch to v0.20.0 branch
pip install -e .  # or "python setup.py develop"

Step 3. Clone the MMDetection3D repository.

git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d

Step 4. Install build requirements and then install MMDetection3D.

pip install -v -e .  # or "python setup.py develop"

Note:

The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

Important: Be sure to remove the ./build folder if you reinstall mmdet with a different CUDA/PyTorch version.
```
pip uninstall mmdet3d
rm -rf ./build
find . -name "*.so" | xargs rm
```
Following the above instructions, MMDetection3D is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.
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 spconv2.0. If the user has installed spconv2.0, the code will use spconv2.0 first, which will take up less GPU memory than using the default mmcv spconv. Users can use the following commands to install spconv2.0:
```
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:
```
conda install openblas-devel -c anaconda
pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=/opt/conda/include" --install-option="--blas=openblas"
```
The code can not be built for CPU only environment (where CUDA isn’t available) for now.

Verification¶

Verify with point cloud demo¶

We provide several demo scripts to test a single sample. Pre-trained models can be downloaded from model zoo. To test a single-modality 3D detection on point cloud scenes:

python demo/pcd_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}]

Examples:

python demo/pcd_demo.py demo/data/kitti/kitti_000008.bin configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth

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 generate 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')

More demos about single/multi-modality and indoor/outdoor 3D detection can be found in demo.

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 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-full built for PyTorch 1.10.x and CUDA 11.3.

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html

Using MMDetection3D with Docker¶

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.6, CUDA 10.1
docker build -t mmdetection3d -f docker/Dockerfile .

Run it with

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

A from-scratch setup script¶

Here is a full script for setting up MMdetection3D with conda.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

# install latest PyTorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y

# install mmcv
pip install mmcv-full

# install mmdetection
pip install git+https://github.com/open-mmlab/mmdetection.git

# install mmsegmentation
pip install git+https://github.com/open-mmlab/mmsegmentation.git

# install mmdetection3d
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
pip install -v -e .

Trouble shooting¶

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.