Solutions for Tiger2019 Pose Track Challenge

Note:

• Flip test is used.
• Input size is 384x288
• pose_unetplus is our work.

Environment

The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 8 TITAN V GPU cards. Other platforms or GPU cards are not fully tested.

Quick start

Installation

1. Install pytorch >= v1.0.0 following official instruction. Note that if you use pytorch's version < v1.0.0, you should following the instruction at https://github.com/Microsoft/human-pose-estimation.pytorch to disable cudnn's implementations of BatchNorm layer. We encourage you to use higher pytorch's version(>=v1.0.0)

2. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.

3. Install dependencies:

   pip install -r requirements.txt
   

4. Make libs:

   cd ${POSE_ROOT}/lib
   make
   

5. Install COCOAPI:

   # COCOAPI=/path/to/clone/cocoapi
   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
   cd $COCOAPI/PythonAPI
   # Install into global site-packages
   make install
   # Alternatively, if you do not have permissions or prefer
   # not to install the COCO API into global site-packages
   python3 setup.py install --user
   

   Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.

6. Init output(training model output directory) and log(tensorboard log directory) directory:

   mkdir output 
   mkdir log
   

   Your directory tree should look like this:

   ${POSE_ROOT}
   ├── data
   ├── experiments
   ├── lib
   ├── log
   ├── models
   ├── output
   ├── tools 
   ├── Tiger2019
   ├     ├──coco
   ├     ├──pose
   ├     ├──inference.sh
   ├     ├──train.sh
   ├── README.md
   └── requirements.txt
   

7. Download pretrained models from our model zoo(GoogleDrive or OneDrive)

   ${POSE_ROOT}
    `-- models
        `-- pytorch
            |-- imagenet
            |   |-- resnet50-19c8e357.pth
            |   |-- resnet101-5d3b4d8f.pth
            |   `-- resnet152-b121ed2d.pth
   
   

Data preparation

For CVWC 2019 Tiger Pose Track data, please make them look like this:

${POSE_ROOT}
|-- Tiger2019
`-- |-- pose
    `-- |-- annotations
        |   |-- keypoint_train.json
        |   |-- keypoint_val.json
        |   |-- image_info_test.json
        `-- train
            |-- 001163.jpg
            |-- 003072.jpg
        `-- val
            |-- 001163.jpg
            |-- 003072.jpg
        `-- test
            |-- 001163.jpg
            |-- 003072.jpg

you can get our handled json files from dir 'pose/annotations', and then put them in dir 'pose/annotations/' like this:

pose/annotations/tiger_keypoints_train.json

Training

bash train.sh or . ./train.sh

Testing on Tiger pose dataset using model zoo's models here

(BaiduDrive) (Extract Code: r916) download our trained model and put it in dir '/share/db/Pose-Estimation-Baseline/Tiger2019/work_dir' like this:

/share/db/Pose-Estimation-Baseline/Tiger2019/work_dir/unetplus_r152_256x192/tiger/pose_unetplus/res152_384x288_d256x3_adam_lr1e-3/model_best.pth

Testing

bash inference.sh or . ./inference.sh