humanpose.pytorch

Introduction

This is an human pose estimation pytorch implementation derivated from deep-high-resolution-net.pytorch, aims to achieve lightweight real-time application.

Features

• It support Distributed DataParallel training, much faster than origin repo.
• support lightweight pose backbones.
• support lightweight mobile hunman detector yolov3mobile.

Main Results

Results on MPII val

Arch	Head	Shoulder	Elbow	Wrist	Hip	Knee	Ankle	Mean	[email protected]
pose_hrnet_w32	97.067	95.686	90.21	85.644	89.077	85.795	82.711	89.927	37.931
pose_hrnet_w48	96.930	95.771	90.864	86.329	88.731	86.862	82.829	90.208	38.002

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch	Input size	#Params	FLOPs	Weight size	AP	Ap .5	AP .75	AP (M)	AP (L)	AR	AR .5	AR .75	AR (M)	AR (L)
pose_hrnet_w18_v1	256x192	1.3M	0.68G	5.3M	0.572	0.863	0.644	0.545	0.614	0.612	0.876	0.687	0.579	0.661
pose_hrnet_w18_v2	256x192	3.7M	1.8G	15M	0.710	0.916	0.784	0.685	0.753	0.740	0.922	0.806	0.710	0.786
pose_hrnet_w18_v2_softargmax	256x192	3.7M	1.8G	15M	0.713	0.916	0.783	0.685	0.758	0.743	0.923	0.809	0.711	0.792
pose_hrnet_w32	256x192	28.5M	7.1G	110M	0.765	0.936	0.838	0.740	0.810	0.794	0.945	0.858	0.763	0.842
lpn_18	256x192	0.47M	0.42G	1.9M	0.445	0.773	0.445	0.434	0.467	0.497	0.798	0.519	0.474	0.531
lpn_18h	256x192	0.50M	0.43G	2.1M	0.486	0.806	0.506	0.472	0.511	0.533	0.821	0.567	0.508	0.570
lpn_34	256x192	0.59M	0.43G	2.5M	0.493	0.808	0.522	0.478	0.515	0.538	0.825	0.577	0.514	0.573
lpn_34h	256x192	0.66M	0.46G	2.7M	0.536	0.830	0.579	0.520	0.564	0.579	0.849	0.630	0.552	0.618
lpn_50	256x192	2.9M	1.0G	12M	0.684	0.904	0.762	0.659	0.724	0.717	0.914	0.789	0.687	0.763
lpn_100	256x192	6.7M	1.8G	27M	0.721	0.915	0.805	0.699	0.764	0.754	0.929	0.825	0.725	0.799

Iterative training strategy

lpn18h	AP	Ap .5	AP .75	AP (M)	AP (L)	AR	AR .5	AR .75	AR (M)	AR (L)
stage0	0.486	0.806	0.506	0.472	0.511	0.533	0.821	0.567	0.508	0.570
stage1	0.496	0.807	0.521	0.483	0.521	0.541	0.822	0.577	0.517	0.577
stage2	0.505	0.808	0.540	0.491	0.529	0.549	0.825	0.591	0.524	0.586
stage3	0.510	0.819	0.542	0.497	0.536	0.555	0.832	0.598	0.530	0.591
stage4	0.514	0.819	0.543	0.500	0.538	0.558	0.832	0.599	0.533	0.595
stage5	0.517	0.819	0.553	0.500	0.544	0.559	0.834	0.602	0.533	0.598
stage6	0.520	0.820	0.557	0.503	0.546	0.563	0.836	0.607	0.537	0.601

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 NVIDIA V100 GPU cards. Other platforms or GPU cards are not fully tested.

Quick start

Installation

1. Install pytorch >= v1.0.0 following official instruction.

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 
   ├── README.md
   └── requirements.txt
   

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

   ${POSE_ROOT}
    `-- models
        `-- pytorch
            |-- imagenet
            |   |-- hrnet_w32-36af842e.pth
            |   |-- hrnet_w48-8ef0771d.pth
            |   |-- resnet50-19c8e357.pth
            |-- pose_coco
            |   |-- pose_hrnet_w32_256x192.pth
            |   |-- pose_hrnet_w32_384x288.pth
            |   |-- pose_hrnet_w48_256x192.pth
            |   |-- pose_hrnet_w48_384x288.pth
            |   |-- pose_resnet_50_256x192.pth
            |   `-- pose_resnet_50_384x288.pth
            `-- pose_mpii
                |-- pose_hrnet_w32_256x256.pth
                |-- pose_hrnet_w48_256x256.pth
                `-- pose_resnet_50_256x256.pth
   
   

Data preparation

For MPII data, please download from MPII Human Pose Dataset. The original annotation files are in matlab format. We have converted them into json format, you also need to download them from OneDrive or GoogleDrive. Extract them under {POSE_ROOT}/data, and make them look like this:

${POSE_ROOT}
|-- data
`-- |-- mpii
    `-- |-- annot
        |   |-- gt_valid.mat
        |   |-- test.json
        |   |-- train.json
        |   |-- trainval.json
        |   `-- valid.json
        `-- images
            |-- 000001163.jpg
            |-- 000003072.jpg

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 and test-dev2017 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
        |   |-- COCO_test-dev2017_detections_AP_H_609_person.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ... 

Training and Testing

Testing on MPII dataset using model zoo's models(GoogleDrive or OneDrive)

python tools/test.py \
    --cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pytorch/pose_mpii/pose_hrnet_w32_256x256.pth

Training on MPII dataset

python tools/train.py \
    --cfg experiments/mpii/hrnet/w32_256x256_adam_lr1e-3.yaml

Testing on COCO val2017 dataset using model zoo's models(GoogleDrive or OneDrive)

python tools/test.py \
    --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \
    TEST.MODEL_FILE models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth \
    TEST.USE_GT_BBOX False

Training on COCO train2017 dataset

python tools/train.py \
    --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \

Visualization

Visualizing predictions on COCO val

python visualization/plot_coco.py \
    --prediction output/coco/w48_384x288_adam_lr1e-3/results/keypoints_val2017_results_0.json \
    --save-path visualization/results

Acknowledgement

• This repo is modified and adapted on these great repositories deep-high-resolution-net.pytorch

Contact

[email protected]