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Description

Code for "Learning Feature Pyramids for Human Pose Estimation" (ICCV 2017)

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Learning Feature Pyramids for Human Pose Estimation

Training and testing code for the paper

Learning Feature Pyramids for Human Pose Estimation
Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, Xiaogang Wang
ICCV, 2017

This code is based on stacked hourglass networks and fb.resnet.torch. Thanks to the authors.

Install

  1. Install Torch.

  2. Install dependencies.

    luarocks install hdf5
    luarocks install matio
    luarocks install optnet
    
  3. (Optional) Install nccl for better performance when training with multi-GPUs.

    git clone https://github.com/NVIDIA/nccl.git
    cd nccl
    make 
    make install
    luarocks install nccl
    
    set
    LD_LIBRARY_PATH
    in file
    ~/.bashrc
    if
    libnccl.so
    is not found.
  4. Prepare dataset.
    Create a symbolic link to the images directory of the MPII dataset:

    ln -s PATH_TO_MPII_IMAGES_DIR data/mpii/images
    
    Create a symbolic link to the images directory of the LSP dataset (images are stored in
    PATH_TO_LSP_DIR/images
    ):
    ln -s PATH_TO_LSP_DIR data/lsp/lsp_dataset
    
    Create a symbolic link to the images directory of the LSP extension dataset (images are stored in
    PATH_TO_LSPEXT_DIR/images
    ):
    ln -s PATH_TO_LSPEXT_DIR data/lsp/lspet_dataset
    

Training and Testing

Quick Start

Testing from our pretrained model

Download our pretrained model to

./pretrained
folder from Google Drive. Test on the MPII validation set by running the following command
qlua main.lua -batchSize 1 -nGPU 1 -nStack 8 -minusMean true -loadModel pretrained/model_250.t7 -testOnly true -debug true

Example

For multi-scale testing, run

qlua evalPyra.lua -batchSize 1 -nGPU 1 -nStack 8 -minusMean true -loadModel pretrained/model_250.t7 -testOnly true -debug true

Note: * If you DO NOT want to visualize the training results. Set

-debug false
and use
th
instead of
qlua
. * you may set the number of scales in
evalPyra.lua
(Line 22 ). Use fewer number of scales or multiple GPUs if "out of memory" occurs. * use
-loadModel MODEL_PATH
to load a specific model for testing or training

Train a two-stack hourglass model

Train an example two-stack hourglass model on the MPII dataset with the proposed Pyramids Residual Modules (PRMs)

sh ./experiments/mpii/hg-prm-stack2.sh 

Customize your own training and testing procedure

A sample script for training on the MPII dataset with 8-stack hourglass model.

#!/usr/bin/env sh
expID=mpii/mpii_hg8   # snapshots and log file will save in checkpoints/$expID
dataset=mpii          # mpii | mpii-lsp | lsp |
gpuID=0,1             # GPUs visible to program
nGPU=2                # how many GPUs will be used to train the model
batchSize=16          
LR=6.7e-4
netType=hg-prm        # network architecture
nStack=2
nResidual=1
nThreads=4            # how many threads will be used to load data
minusMean=true
nClasses=16
nEpochs=200           
snapshot=10           # save models for every $snapshot

OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=$gpuID th main.lua
-dataset $dataset
-expID $expID
-batchSize $batchSize
-nGPU $nGPU
-LR $LR
-momentum 0.0
-weightDecay 0.0
-netType $netType
-nStack $nStack
-nResidual $nResidual
-nThreads $nThreads
-minusMean $minusMean
-nClasses $nClasses
-nEpochs $nEpochs
-snapshot $snapshot \

-resume checkpoints/$expID \ # uncomment this line to resume training

-testOnly true \ # uncomment this line to test on validation data

-testRelease true \ # uncomment this line to test on test data (MPII dataset)

Evaluation

You may evaluate the PCKh score of your model on the MPII validation set. To get start, download our prediction

pred_multiscale_250.h5
to
./pretrained
from Google Drive, and run the MATLAB script
evaluation/eval_PCKh.m
. You'll get the following results
      Head , Shoulder , Elbow , Wrist , Hip , Knee  , Ankle , Mean , 
name , 97.41 , 96.16 , 91.10 , 86.88 , 90.05 , 86.00 , 83.89 , 90.27

Citation

If you find this code useful in your research, please consider citing:

@inproceedings{yang2017pyramid,
    Title = {Learning Feature Pyramids for Human Pose Estimation},
    Author = {Yang, Wei and Li, Shuang and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},
    Booktitle = {arXiv preprint arXiv:1708.01101},
    Year = {2017}
}

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