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Pytorch implementation of Human Pose Estimation with Parsing Induced Learner (CVPR'18)
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README.md

Human Pose Estimation with Parsing Induced Learner

This repository contains the code and pretrained models of

Human Pose Estimation with Parsing Induced Learner [PDF]
Xuecheng Nie, Jiashi Feng, Yiming Zuo, and Shuicheng Yan
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

Prerequisites

  • Python 3.5
  • Pytorch 0.2.0
  • OpenCV 3.0 or higher

Installation

  1. Install Pytorch: Please follow the official instruction on installation of Pytorch.
  2. Clone the repository
    git clone --recursive https://github.com/NieXC/pytorch-pil.git
    
  3. Download Look into Person (LIP) dataset and create symbolic links to the following directories
    ln -s PATH_TO_LIP_TRAIN_IMAGES_DIR dataset/lip/train_images   
    ln -s PATH_TO_LIP_VAL_IMAGES_DIR dataset/lip/val_images      
    ln -s PATH_TO_LIP_TEST_IMAGES_DIR dataset/lip/testing_images   
    ln -s PATH_TO_LIP_TRAIN_SEGMENTATION_ANNO_DIR dataset/lip/train_segmentations   
    ln -s PATH_TO_LIP_VAL_SEGMENTATION_ANNO_DIR dataset/lip/val_segmentations   
    

Usage

Training

Run the following command to train the model from scratch (Default: 8-stack of Hourglass network as pose network and 1-stack of Hourglass network as parsing induced learner):

sh run_train.sh

or

CUDA_VISIBLE_DEVICES=0,1 python main.py -b 24 --lr 0.0015

A simple way to record the training log by adding the following command:

2>&1 | tee exps/logs/pil_lip.log

Some configurable parameters in training phase:

  • --arch network architecture (HG (Hourglass) or VGG)
  • -b mini-batch size
  • --lr initial learning rate (0.0015 for HG based model and 0.0001 for VGG based model)
  • --epochs total number of epochs for training
  • --snapshot-fname-prefix prefix of file name for snapshot, e.g. if set '--snapshot-fname-prefix exps/snapshots/pil_lip', then 'pil_lip.pth.tar' (latest model) and 'pil_lip_best.pth.tar' (model with best validation accuracy) will be generated in the folder 'exps/snapshots'
  • --resume path to the model for recovering training
  • -j number of workers for loading data
  • --print-freq print frequency

Testing

Run the following command to evaluate the model on LIP validation set:

sh run_test.sh

or

CUDA_VISIBLE_DEVICES=0 python main.py --evaluate True --calc-pck True --resume exps/snapshots/pil_lip_best.pth.tar

Run the following command to evaluate the model on LIP testing set:

CUDA_VISIBLE_DEVICES=0 python main.py --evaluate True --resume exps/snapshots/pil_lip_best.pth.tar --eval-data dataset/lip/testing_images --eval-anno dataset/lip/jsons/LIP_SP_TEST_annotations.json

In particular, results will be saved as a .csv file followed the official evaluation format of LIP dataset for single-person human pose estimation. An example is provided in exps/preds/csv_results/pred_keypoints_lip.csv.

Some configurable parameters in testing phase:

  • --evaluate True for testing and false for training
  • --resume path to the model for evaluation
  • --calc-pck calculate PCK or not
  • --pred-path path to the csv file for saving the evaluation results
  • --visualization visualize evaluation or not
  • --vis-dir directory for saving the visualization results

Citation

If you use our code in your work or find it is helpful, please cite the paper:

@inproceedings{nie2018pil,
  title={Human Pose Estimation with Parsing Induced Learner},
  author={Nie, Xuecheng and Feng, Jiashi and Zuo, Yiming and Yan, Shuicheng},
  booktitle={CVPR},
  year={2018}
}
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