Multi Branch Deformable Deconvluation Net for Pose Estimation

Introduction

This is an official pytorch implementation of mypaper. I just find my idea is amost same as the paper(Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression). But I did this job earlier.(lol)

Quick start

Installation

1. Install pytorch >= v1.4.0 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
   
   cd ${POSE_ROOT}/Deformable
   sh make.sh
   

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. Download pytorch imagenet pretrained models from pytorch model zoo and caffe-style pretrained models from GoogleDrive.

7. 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
   ├── pose_estimation
   ├── README.md
   └── requirements.txt
   

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 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
                |-- ... 

Training on MPII

python pose_estimation/train.py \
    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3_multi_branch_pose_resnet_deform_mpii.yaml

Training on COCO train2017

python pose_estimation/train.py \
    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3_multi_branch_pose_resnet_deform_mpii.yaml

Valid on COCO val2017

python pose_estimation/valid \
    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3_multi_branch_pose_resnet_deform_mpii.yaml \
    --flip-test