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Prerequisites

  • Linux | macOS | Windows
  • Python 3.6+
  • PyTorch 1.3+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • MMCV
  • MMDetection

The compatible MMTracking, MMCV, and MMDetection versions are as below. Please install the correct version to avoid installation issues.

MMTracking version MMCV version MMDetection version
master mmcv-full>=1.3.17, <1.6.0 MMDetection>=2.19.1
0.13.0 mmcv-full>=1.3.17, <1.6.0 MMDetection>=2.19.1
0.12.0 mmcv-full>=1.3.17, <1.5.0 MMDetection>=2.19.1
0.11.0 mmcv-full>=1.3.17, <1.5.0 MMDetection>=2.19.1
0.10.0 mmcv-full>=1.3.17, <1.5.0 MMDetection>=2.19.1
0.9.0 mmcv-full>=1.3.17, <1.5.0 MMDetection>=2.19.1
0.8.0 mmcv-full>=1.3.8, <1.4.0 MMDetection>=2.14.0
0.7.0 mmcv-full>=1.3.8, <1.4.0 MMDetection>=2.14.0
0.6.0 mmcv-full>=1.3.8, <1.4.0 MMDetection>=2.14.0

Installation

Detailed Instructions

  1. Create a conda virtual environment and activate it.

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

  2. Install PyTorch and torchvision following the official instructions, e.g.,

    conda install pytorch torchvision -c pytorch

    Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

    E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

    conda install pytorch==1.5 cudatoolkit=10.1 torchvision -c pytorch

    E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

    conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

    If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.

  3. Install extra dependencies for VOT evaluation (optional)

    If you need to evaluate on VOT Challenge, please install the vot-toolkit before the installation of mmcv and mmdetection to avoid possible numpy version requirement conflict among some dependencies.

    pip install git+https://github.com/votchallenge/toolkit.git

  4. Install mmcv-full, we recommend you to install the pre-build package as below.

    # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10.0/index.html

    mmcv-full is only compiled on PyTorch 1.x.0 because the compatibility usually holds between 1.x.0 and 1.x.1. If your PyTorch version is 1.x.1, you can install mmcv-full compiled with PyTorch 1.x.0 and it usually works well.

    # We can ignore the micro version of PyTorch
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.10/index.html

    See here for different versions of MMCV compatible to different PyTorch and CUDA versions. Optionally you can choose to compile mmcv from source by the following command

    git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full will be installed after this step
cd ..

  5. Install MMDetection

    pip install mmdet

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

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

  6. Clone the MMTracking repository.

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

  7. Install build requirements and then install MMTracking.

    pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

  8. Install extra dependencies

  • For MOTChallenge evaluation:

    pip install git+https://github.com/JonathonLuiten/TrackEval.git

  • For LVIS evaluation:

    pip install git+https://github.com/lvis-dataset/lvis-api.git

  • For TAO evaluation:

    pip install git+https://github.com/TAO-Dataset/tao.git

Note:

a. Following the above instructions, MMTracking is installed on dev mode , any local modifications made to the code will take effect without the need to reinstall it.

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

A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMTracking with conda.

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

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y

pip install git+https://github.com/votchallenge/toolkit.git (optional)
# install the latest mmcv
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html

# install mmdetection
pip install mmdet

# install mmtracking
git clone https://github.com/open-mmlab/mmtracking.git
cd mmtracking
pip install -r requirements/build.txt
pip install -v -e .
pip install git+https://github.com/JonathonLuiten/TrackEval.git
pip install git+https://github.com/lvis-dataset/lvis-api.git
pip install git+https://github.com/TAO-Dataset/tao.git

Developing with multiple MMTracking versions

The train and test scripts already modify the PYTHONPATH to ensure the script use the MMTracking in the current directory.

To use the default MMTracking installed in the environment rather than that you are working with, you can remove the following line in those scripts

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

Verification

To verify whether MMTracking and the required environment are installed correctly, we can run MOT, VID, SOT demo script.

For example, run MOT demo and you will see a output video named mot.mp4:

python demo/demo_mot_vis.py configs/mot/deepsort/sort_faster-rcnn_fpn_4e_mot17-private.py --input demo/demo.mp4 --output mot.mp4