Как скомпилировать caffe_rtpose на OSX?

Я недавно наткнулся на пятнистый caffe_rtpose и я попытался скомпилировать и запустить пример. К сожалению, у меня большой опыт работы с c ++, поэтому я столкнулся с множеством проблем компиляции и компоновки.

Я попытался настроить конфиг Makefile (модифицированный из существующий конфиг Ubuntu). (Я использую систему под управлением OSX 10.11.5 с nVidia GeForce 750M, и я установил CUDA 7.5 и libcudnn):

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#   You should not set this flag if you will be reading LMDBs with any
#   possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
# OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_50,code=compute_50 \
-gencode arch=compute_52,code=sm_52 \
# -gencode arch=compute_60,code=sm_60 \
# -gencode arch=compute_61,code=sm_61
# Deprecated
#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_50,code=compute_50

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib
BLAS_INCLUDE := /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/Headers/
BLAS_LIB := /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
# WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
# INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
# LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial

INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
# Q ?= @

И это модифицированная версия install_caffe_and_cpm_osx.sh сценарий:

#!/bin/bashecho "------------------------- INSTALLING CAFFE AND CPM -------------------------"echo "NOTE: This script assumes that CUDA and cuDNN are already installed on your machine. Otherwise, it might fail."function exitIfError {
if [[ $? -ne 0 ]] ; then
echo ""echo "------------------------- -------------------------"echo "Errors detected. Exiting script. The software might have not been successfully installed."echo "------------------------- -------------------------"exit 1
fi
}# echo "------------------------- Checking Ubuntu Version -------------------------"# ubuntu_version="$(lsb_release -r)"# echo "Ubuntu $ubuntu_version"# if [[ $ubuntu_version == *"14."* ]]; then
#     ubuntu_le_14=true
# elif [[ $ubuntu_version == *"16."* || $ubuntu_version == *"15."* || $ubuntu_version == *"17."* || $ubuntu_version == *"18."* ]]; then
#     ubuntu_le_14=false
# else
#     echo "Ubuntu release older than version 14. This installation script might fail."#     ubuntu_le_14=true
# fi
# exitIfError
# echo "------------------------- Ubuntu Version Checked -------------------------"# echo ""echo "------------------------- Checking Number of Processors -------------------------"NUM_CORES=$(grep -c ^processor /proc/cpuinfo 2>/dev/null || sysctl -n hw.ncpu)
echo "$NUM_CORES cores"exitIfError
echo "------------------------- Number of Processors Checked -------------------------"echo ""echo "------------------------- Installing some Caffe Dependencies -------------------------"# Basic
# sudo apt-get --assume-yes update
# sudo apt-get --assume-yes install build-essential
#General dependencies
brew install protobuf leveldb snappy hdf5
# with Python pycaffe needs dependencies built from source - from http://caffe.berkeleyvision.org/install_osx.html
# brew install --build-from-source --with-python -vd protobuf
# brew install --build-from-source -vd boost boost-python
# without Python the usual installation suffices
brew install boost
# sudo apt-get --assume-yes install libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler
# sudo apt-get --assume-yes install --no-install-recommends libboost-all-dev
# Remaining dependencies, 14.04
brew install gflags glog lmdb
# if [[ $ubuntu_le_14 == true ]]; then
#     sudo apt-get --assume-yes install libgflags-dev libgoogle-glog-dev liblmdb-dev
# fi
# OpenCV 2.4
# sudo apt-get --assume-yes install libopencv-dev
exitIfError
echo "------------------------- Some Caffe Dependencies Installed -------------------------"echo ""echo "------------------------- Compiling Caffe & CPM -------------------------"cp Makefile.config.OSX.10.11.5.example Makefile.config
make all -j$NUM_CORES
# make test -j$NUM_CORES
# make runtest -j$NUM_CORES
exitIfError
echo "------------------------- Caffe & CPM Compiled -------------------------"echo ""

# echo "------------------------- Installing CPM -------------------------"# echo "Compiled"# exitIfError
# echo "------------------------- CPM Installed -------------------------"# echo ""echo "------------------------- Downloading CPM Models -------------------------"models_folder="./model/"# COCO
coco_folder="$models_folder"coco/""coco_model="$coco_folder"pose_iter_440000.caffemodel""if [ ! -f $coco_model ]; then
wget http://posefs1.perception.cs.cmu.edu/Users/tsimon/Projects/coco/data/models/coco/pose_iter_440000.caffemodel -P $coco_folder
fi
exitIfError
# MPI
mpi_folder="$models_folder"mpi/""mpi_model="$mpi_folder"pose_iter_160000.caffemodel""if [ ! -f $mpi_model ]; then
wget http://posefs1.perception.cs.cmu.edu/Users/tsimon/Projects/coco/data/models/mpi/pose_iter_160000.caffemodel -P $mpi_folder
fi
exitIfError
echo "Models downloaded"echo "------------------------- CPM Models Downloaded -------------------------"echo ""echo "------------------------- CAFFE AND CPM INSTALLED -------------------------"echo ""

Но я получаю эту ошибку:

examples/rtpose/rtpose.cpp:1088:22: error: variable length array of non-POD element type 'Frame'
Frame frame_batch[BATCH_SIZE];

Я попытался поменять массив для вектора:

std::vector<Frame> frame_batch;
std::cout << "allocating " << BATCH_SIZE << " frames" << std::endl;
frame_batch.reserve(BATCH_SIZE);

Это, кажется, заботится об этой ошибке компиляции, но теперь я получаю ошибку компоновщика:
ld: библиотека не найдена для -lgomp
clang: error: сбой команды компоновщика с кодом выхода 1 (используйте -v для просмотра вызова)

Я искал lib lib gomp и нашел несколько связанных постов по caffe и OpenMP, в которых упоминались проблемы с clang для OSX и OpenMP.
Что я пробовал:

1. Следующий эта почта Я установил gcc 4.9 с помощью homebrew (так как формула homebrew для gcc 5 устанавливает 5.9, что может быть слишком высоким?)
2. Я поставил -fopenmp=libomp основанный на Ответ Андрея Боханко: это не сработало для меня ++-4.9: error: unrecognized command line option '-fopenmp=libomp'

Я мог бы скачать и собрать Caffe отдельно, используя официальные инструкции, но я не могу понять, как скомпилировать эту потрясающую демонстрацию.
К сожалению, у меня нет опыта работы с c ++ и OpenMP, поэтому я действительно могу использовать ваши предложения здесь. Спасибо

Обновить: Я попробовал полезное предложение Марка Сетчелла об установке llvm через clang. Я обновил конфигурацию Makefile для использования

CUSTOM_CXX := /usr/local/opt/llvm/bin/clang++

но CUDA это не нравится

nvcc fatal   : The version ('30801') of the host compiler ('clang') is not supported

Я пытался скомпилировать с CPU_ONLY но я все еще получаю ошибки CUDA:

examples/rtpose/rtpose.cpp:235:5: error: use of undeclared identifier 'cudaMalloc'
cudaMalloc(&net_copies[device_id].canvas, DISPLAY_RESOLUTION_WIDTH * DISPLAY_RESOLUTION_HEIGHT * 3 * sizeof(float));
^
examples/rtpose/rtpose.cpp:236:5: error: use of undeclared identifier 'cudaMalloc'
cudaMalloc(&net_copies[device_id].joints, MAX_NUM_PARTS*3*MAX_PEOPLE * sizeof(float) );
^
examples/rtpose/rtpose.cpp:1130:146: error: use of undeclared identifier 'cudaMemcpyHostToDevice'
cudaMemcpy(net_copies[tid].canvas, frame.data_for_mat, DISPLAY_RESOLUTION_WIDTH * DISPLAY_RESOLUTION_HEIGHT * 3 * sizeof(float), cudaMemcpyHostToDevice);
^
examples/rtpose/rtpose.cpp:1136:108: error: use of undeclared identifier 'cudaMemcpyHostToDevice'
cudaMemcpy(pointer + 0 * offset, frame_batch[0].data, BATCH_SIZE * offset * sizeof(float), cudaMemcpyHostToDevice);
^
examples/rtpose/rtpose.cpp:1178:13: error: use of undeclared identifier 'cudaMemcpyHostToDevice'
cudaMemcpyHostToDevice);
^
examples/rtpose/rtpose.cpp:1192:155: error: use of undeclared identifier 'cudaMemcpyDeviceToHost'
cudaMemcpy(frame_batch[n].data_for_mat, net_copies[tid].canvas, DISPLAY_RESOLUTION_HEIGHT * DISPLAY_RESOLUTION_WIDTH * 3 * sizeof(float), cudaMemcpyDeviceToHost);
^
examples/rtpose/rtpose.cpp:1202:155: error: use of undeclared identifier 'cudaMemcpyDeviceToHost'
cudaMemcpy(frame_batch[n].data_for_mat, net_copies[tid].canvas, DISPLAY_RESOLUTION_HEIGHT * DISPLAY_RESOLUTION_WIDTH * 3 * sizeof(float), cudaMemcpyDeviceToHost);

Я не эксперт, но, имея быстрый просмотр кода, я не вижу, как версия CPU_ONLY будет работать с вызовами cuda.

Имея другой взгляд на caffe OSX Руководство по установке, Я могу попробовать маршрут> не для слабонервных