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

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)
Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.
Install dependencies:
```
pip install -r requirements.txt
```
Make libs:
```
cd ${POSE_ROOT}/lib
make
```

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.

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

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

dbofseuofhust/Pose-Track-Solutions