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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:

Install mmpose

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 10.1 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 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.

c. Clone the mmpose repository

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

d. Install build requirements and then install mmpose

pip install -r requirements.txt
python setup.py develop

Note:

  1. 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.

  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.

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

conda install -c pytorch pytorch torchvision -y
git clone git@github.com:open-mmlab/mmpose.git
cd mmpose
pip install -r requirements.txt
python setup.py develop

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