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Installation

Requirements

  • Nvidia device with CUDA, example for Ubuntu 20.04 (if you have no nvidia device, delete this line from setup.py
  • Python 3.7+
  • Cython
  • PyTorch 1.11+, for users who want to use 1.5 < PyTorch < 1.11, please switch to the pytorch<1.11 branch by: git checkout "pytorch<1.11"; for users who want to use PyTorch < 1.5, please switch to the pytorch<1.5 branch by: git checkout "pytorch<1.5"
  • torchvision 0.12.0+
  • numpy
  • python-package setuptools >= 40.0, reported by this issue
  • Linux

Code installation

Install with pip

# 0. Create install directory
mkdir install
export PYTHONUSERBASE=`pwd`/install

# 1. Install Cuda 11
wget https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_510.47.03_linux.run -O cuda.run
chmod +x cuda.run
./cuda.run --silent --toolkitpath=${PYTHONUSERBASE} --toolkit --override --defaultroot=${PYTHONUSERBASE} --no-man-page 

# 2. Install PyTorch
mkdir build
pip3 install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116

# 3. Get AlphaPose
git clone https://github.com/MVIG-SJTU/AlphaPose.git
cd AlphaPose

# 4. Install
export PATH=${PYTHONUSERBASE}/bin/:$PATH
export LD_LIBRARY_PATH=${PYTHONUSERBASE}/lib64/:$LD_LIBRARY_PATH
pip install cython
pip install .

Models

  1. Download the object detection model manually: yolov3-spp.weights(Google Drive | Baidu pan). Place it into pretrained_models/yolo/.
  2. (Optional) If you want to use YOLOX as the detector, you can download the weights here, and place them into pretrained_models/yolox. We recommend yolox-l and yolox-x.
  3. Download our pose models. Place them into pretrained_models. All models and details are available in our Model Zoo.
  4. For pose tracking, please refer to our tracking docments for model download

Prepare dataset (optional)

MSCOCO

If you want to train the model by yourself, please download data from MSCOCO (train2017 and val2017). Download and extract them under ./data, and make them look like this:

|-- json
|-- exp
|-- alphapose
|-- configs
|-- test
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   `-- person_keypoints_val2017.json
        |-- train2017
        |   |-- 000000000009.jpg
        |   |-- 000000000025.jpg
        |   |-- 000000000030.jpg
        |   |-- ... 
        `-- val2017
            |-- 000000000139.jpg
            |-- 000000000285.jpg
            |-- 000000000632.jpg
            |-- ...

MPII

Please download images from MPII. We also provide the annotations in json format [annot_mpii.zip]. Download and extract them under ./data, and make them look like this:

|-- data
`-- |-- mpii
    `-- |-- annot_mpii.json
        `-- images
            |-- 027457270.jpg
            |-- 036645665.jpg
            |-- 045572740.jpg
            |-- ...

Halpe-FullBody

If you want to train the model by yourself, please download data from Halpe-FullBody. Download and extract them under ./data, and make them look like this:

|-- json
|-- exp
|-- alphapose
|-- configs
|-- test
|-- data
`-- |-- halpe
    `-- |-- annotations
        |   |-- halpe_train_v1.json
        |   `-- halpe_val_v1.json
        |-- images
        `-- |-- train2015
             |   |-- HICO_train2015_00000001.jpg
             |   |-- HICO_train2015_00000002.jpg
             |   |-- HICO_train2015_00000003.jpg
             |   |-- ... 
             `-- val2017
                 |-- 000000000139.jpg
                 |-- 000000000285.jpg
                 |-- 000000000632.jpg
                 |-- ...