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.

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	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	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
pose_hrnet_w32	256x192	28.5M	7.1G	0.765	0.936	0.838	0.740	0.810	0.794	0.945	0.858	0.763	0.842

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

Install pytorch >= v1.0.0 following official instruction.
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 
├── README.md
└── requirements.txt

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]

felixzhang7 / humanpose.pytorch Goto Github PK