기본 설정 설치

opencv install with opencv_contrib on cuda (기본적으로 3대 버전을 추천, cmake 설정이 3대 버전으로 되어있음)
cuda version 7.0 이상설치
CUDA install , CUDNN install

딥러닝 train을 위한 전용 lib 설치

1. git clone https://github.com/CMU-Perceptual-Computing-Lab/caffe_train

2. make파일 python 설정 
자신의 python 버전에 맞게 설정해주어야함
PYTHON_LIBRARIES ?= boost_python python2.7
or
PYTHON_LIBRARIES ?= boost_python-py35 python3.5m
or
PYTHON_LIBRARIES ?= boost_python-py35 python3.5

3. Makefile.config 설정 (고쳐야될 부분만 적었습니다)

CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
    -gencode arch=compute_20,code=sm_21 \
    -gencode arch=compute_30,code=sm_30 \
    -gencode arch=compute_35,code=sm_35 \
    -gencode arch=compute_50,code=sm_50 \
    -gencode arch=compute_52,code=sm_52 \
    -gencode arch=compute_60,code=sm_60 \
    -gencode arch=compute_61,code=sm_61 \
    -gencode arch=compute_61,code=compute_61

PYTHON_INCLUDE := /usr/include/python3.5m \
    /usr/lib/python3.5/dist-packages/numpy/core/include

4. build
mkdir build
cd build
cmake ..
make all
make install
make runtest

그리고 사용할 때는 코드에(setlayer.py)
caffe_path = '/media/vit/DATA/Tensor/caffe_train' 이런식으로 설치된 경로를 적어줘야한다.

Caffe_train

Our modified caffe for training multi-person pose estimator. The original caffe version is in July 2016. This repository at least runs on Ubuntu 16.04, OpenCV 2.4.10, CUDA 7.5/8.0, and CUDNN 5.

The full project repo includes detailed training steps and the testing code in matlab, C++ and python.

We add customized caffe layer for data augmentation: cpm_data_transformer.cpp, including scale augmentation e.g., in the range of 0.7 to 1.3, rotation augmentation, e.g., in the range of -40 to 40 degrees, flip augmentation and image cropping. This augmentation strategy makes the method capable of dealing with a large range of scales and orientations. You can set the augmentation parameters in setLayers.py. Example data layer parameters in the training prototxt is:

layer {
  name: "data"
  type: "CPMData"
  top: "data"
  top: "label"
  data_param {
    source: "/home/zhecao/COCO_kpt/lmdb_trainVal"
    batch_size: 10
    backend: LMDB
  }
  cpm_transform_param {
    stride: 8
    max_rotate_degree: 40
    visualize: false
    crop_size_x: 368
    crop_size_y: 368
    scale_prob: 1
    scale_min: 0.5
    scale_max: 1.1
    target_dist: 0.6
    center_perterb_max: 40
    do_clahe: false
    num_parts: 56
    np_in_lmdb: 17
  }
}

This project is licensed under the terms of the GPL v3 license . We will merge it with the caffe testing version (https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose) later.

Citation

Please cite the paper in your publications if it helps your research:

@article{cao2016realtime,
  title={Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  author={Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  journal={arXiv preprint arXiv:1611.08050},
  year={2016}
  }

@inproceedings{wei2016cpm,
  author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
  booktitle = {CVPR},
  title = {Convolutional pose machines},
  year = {2016}
  }

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

and step-by-step examples.

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}