peternara/pytorch-pil-pose

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

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}
}