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 Torch.
Install dependencies.
luarocks install hdf5
luarocks install matio
luarocks install optnet
(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.
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
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
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:
-debug false
and use th
instead of qlua
.evalPyra.lua
(Line 22 ). Use fewer number of scales or multiple GPUs if "out of memory" occurs.-loadModel MODEL_PATH
to load a specific model for testing or trainingTrain an example two-stack hourglass model on the MPII dataset with the proposed Pyramids Residual Modules (PRMs)
sh ./experiments/mpii/hg-prm-stack2.sh
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)
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
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}
}