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Installation

Requirements

  • Linux (Windows is not officially supported)

  • Python 3.6+

  • PyTorch 1.3+

  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)

  • GCC 5+

  • mmcv (Please install the latest version of mmcv-full)

  • Numpy

  • cv2

  • json_tricks

  • xtcocotools

Optional:

  • mmdet (to run pose demos)

  • mmtrack (to run pose tracking demos)

  • pyrender (to run 3d mesh demos)

  • smplx (to run 3d mesh demos)

Prepare environment

a. Create a conda virtual environment and activate it.

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

b. 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 10.2 installed under /usr/local/cuda and would like to install PyTorch 1.8.0, you need to install the prebuilt PyTorch with CUDA 10.2.

conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch

E.g.2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.7.0., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=9.2 -c pytorch

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

Install MMPose

a. Install mmcv, we recommend you to install the pre-built mmcv as below.

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

Please replace {cu_version} and {torch_version} in the url to your desired one. For example, to install the latest mmcv-full with CUDA 10.2 and PyTorch 1.8.0, use the following command:

pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/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, which contains cuda ops, will be installed after this step
# OR pip install -e .  # package mmcv, which contains no cuda ops, will be installed after this step
cd ..

Important: You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

b. Clone the mmpose repository

git clone git@github.com:open-mmlab/mmpose.git # or git clone https://github.com/open-mmlab/mmpose
cd mmpose

c. Install build requirements and then install mmpose

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

If you build MMPose on macOS, replace the last command with

CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .

d. Install optional modules

  • mmdet (to run pose demos)

  • mmtrack (to run pose tracking demos)

  • pyrender (to run 3d mesh demos)

  • smplx (to run 3d mesh demos)

Note

  1. The git commit id will be written to the version number with step c, 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.

  2. Following the above instructions, mmpose 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).

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

  4. If you have mmcv installed, you need to firstly uninstall mmcv, and then install mmcv-full.

  5. Some dependencies are optional. Running python setup.py develop will only install the minimum runtime requirements. To use optional dependencies like smplx, either install them 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 [optional] field are all, tests, build, and optional) like pip install -v -e .[tests,build].

Install with CPU only

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

In CPU mode you can run the demo/demo.py for example.

A from-scratch setup script

Here is a full script for setting up mmpose with conda and link the dataset path (supposing that your COCO dataset path is $COCO_ROOT).

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 the latest mmcv-full
# Please replace ``{cu_version}`` and ``{torch_version}`` in the url to your desired one.
# See [here](https://github.com/open-mmlab/mmcv#installation) for different versions of MMCV compatible to different PyTorch and CUDA versions.
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html

# install mmpose
git clone git@github.com:open-mmlab/mmpose.git
cd mmpose
pip install -r requirements.txt
python setup.py develop

mkdir data
ln -s $COCO_ROOT data/coco

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.6.0, CUDA 10.1, CUDNN 7.
docker build -f ./docker/Dockerfile --rm -t mmpose .

Important: Make sure you’ve installed the nvidia-container-toolkit.

Run the following cmd:

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

Developing with multiple MMPose versions

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

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

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