ythhy/human-pose-estimation

simple and lightweight humanpose estimation

Simple and Lightwight Human Pose Estimation

Introduction

This repo is the reimplementation of paper: Simple and Lightwight Human Pose Estimation.On COCO keypoints valid dataset, if with_gcb module achieves 66.5 of mAP, else 64.4 of mAp

Main Results

Results on COCO val2017 dataset

Arch	with_GCB	AP	Ap .5	AP .75	AP (M)	AP (L)	AR	AR .5	AR .75	AR (M)	AR (L)
256x192_lp_net_50_d256d256	yes	0.665	0.903	0.746	0.644	0.697	0.700	0.911	0.771	0.672	0.743
256x192_lp_net_50_d256d256	no	0.644	0.885	0.715	0.619	0.685	0.679	0.898	0.742	0.647	0.725

Note:

• Flip test is used.

Environment

The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA P100 GPU cards. Other platforms or GPU cards are not fully tested.

Quick start

Installation

1. Install pytorch >= v0.4.0 following official instruction.

2. Disable cudnn for batch_norm:

   # PYTORCH=/path/to/pytorch
   # for pytorch v0.4.0
   sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   # for pytorch v0.4.1
   sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   

   Note that instructions like # PYTORCH=/path/to/pytorch indicate that you should pick a path where you'd like to have pytorch installed and then set an environment variable (PYTORCH in this case) accordingly.

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

4. Install dependencies:

   pip install -r requirements.txt
   

5. Make libs:

   cd ${POSE_ROOT}/lib
   make
   

6. 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.

7. coco pretrained models under ${POSE_ROOT}/models/pytorch, and it looks like this:

   ${POSE_ROOT}
    `-- models
        `-- lp_coco
                |-- lp_net_50_256x192_with_gcb.pth.tar
                `-- lp_net_50_256x192_without_gcb.pth.tar
   

8. 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
   ├── images
   ├── experiments
   ├── lib
   ├── log
   ├── models
   ├── output
   ├── pose_estimation
   ├── README.md
   └── requirements.txt
   

Data preparation

For COCO data, please download from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. We also provide person detection result of COCO val2017 to reproduce our multi-person pose estimation results. Please download from OneDrive or GoogleDrive. Download and extract them under {POSE_ROOT}/data, and make them look like this:

${POSE_ROOT}
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   `-- person_keypoints_val2017.json
        |-- person_detection_results
        |   |-- COCO_val2017_detections_AP_H_56_person.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ... 

Valid on COCO val2017 using pretrained models

python pose_estimation/valid.py \
    --cfg experiments/coco/lp_net50/256x192_d256x2_adam_lr1e-3_lp.yaml \
    --flip-test \
    --model-file models/lp_coco/lp_net_50_256x192_with_gcb.pth.tar

Training on COCO train2017

python pose_estimation/train.py \
    --cfg experiments/coco/lp_net50/256x192_d256x2_adam_lr1e-3_lp.yaml

Demo

The region of human need to be given in demo.py

python pose_estimation/demo.py \
    --cfg experiments/coco/lp_net50/256x192_d256x2_adam_lr1e-3_lp.yaml \
    --model-file ./models/lp_coco/lp_net_50_256x192_with_gcb.pth.tar
    --img-file ./images/0.jpg