版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/fengbingchun/article/details/85162936
MXNet源码版本号为1.3.0，其它依赖库的版本号可参考：https://blog.csdn.net/fengbingchun/article/details/84997490
build.sh脚本内容为：
#! /bin/bash
real_path=$(realpath $0)
dir_name=`dirname "${real_path}"`
echo "real_path: ${real_path}, dir_name: ${dir_name}"
data_dir="data"
if [ -d ${dir_name}/${data_dir} ]; then
rm -rf ${dir_name}/${data_dir}
fi
ln -s ${dir_name}/./../../${data_dir} ${dir_name}
new_dir_name=${dir_name}/build
mkdir -p ${new_dir_name}
cd ${new_dir_name}
echo "pos: ${new_dir_name}"
if [ "$(ls -A ${new_dir_name})" ]; then
echo "directory is not empty: ${new_dir_name}"
#rm -r *
else
echo "directory is empty: ${new_dir_name}"
fi
cd -
# build openblas
echo "========== start build openblas =========="
openblas_path=${dir_name}/../../src/openblas
if [ -f ${openblas_path}/build/lib/libopenblas.so ]; then
echo "openblas dynamic library already exists without recompiling"
else
mkdir -p ${openblas_path}/build
cd ${openblas_path}/build
cmake -DBUILD_SHARED_LIBS=ON ..
make
fi
ln -s ${openblas_path}/build/lib/libopenblas* ${new_dir_name}
echo "========== finish build openblas =========="
cd -
# build dmlc-core
echo "========== start build dmlc-core =========="
dmlc_path=${dir_name}/../../src/dmlc-core
if [ -f ${dmlc_path}/build/libdmlc.a ]; then
echo "dmlc static library already exists without recompiling"
else
mkdir -p ${dmlc_path}/build
cd ${dmlc_path}/build
cmake ..
make
fi
ln -s ${dmlc_path}/build/libdmlc.a ${new_dir_name}
echo "========== finish build dmlc-core =========="
rc=$?
if [[ ${rc} != 0 ]]; then
echo "########## Error: some of thess commands have errors above, please check"
exit ${rc}
fi
cd -
cd ${new_dir_name}
cmake ..
make
cd -
CMakeLists.txt文件内容为：
PROJECT(MXNet_Test)
CMAKE_MINIMUM_REQUIRED(VERSION 3.0)
# support C++11
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -std=c11")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
# support C++14, when gcc version > 5.1, use -std=c++14 instead of c++1y
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++1y")
IF(NOT CMAKE_BUILD_TYPE)
SET(CMAKE_BUILD_TYPE "Release")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O2")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O2")
ELSE()
SET(CMAKE_BUILD_TYPE "Debug")
SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -g -Wall -O2")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -Wall -O2")
ENDIF()
MESSAGE(STATUS "cmake build type: ${CMAKE_BUILD_TYPE}")
MESSAGE(STATUS "cmake current source dir: ${CMAKE_CURRENT_SOURCE_DIR}")
SET(PATH_TEST_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../demo/MXNet_Test)
SET(PATH_SRC_DMLC_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/dmlc-core)
SET(PATH_SRC_MSHADOW_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/mshadow)
SET(PATH_SRC_OPENBLAS_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/openblas)
SET(PATH_SRC_MXNET_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/mxnet)
SET(PATH_SRC_TVM_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/tvm)
SET(PATH_SRC_DLPACK_FILES ${CMAKE_CURRENT_SOURCE_DIR}/./../../src/dlpack)
MESSAGE(STATUS "path test files: ${PATH_TEST_FILES}")
# don't use opencv in mxnet
ADD_DEFINITIONS(-DMXNET_USE_OPENCV=0)
ADD_DEFINITIONS(-DMSHADOW_USE_F16C=0)
SET(PATH_OPENCV /opt/opencv3.4.2)
IF(EXISTS ${PATH_OPENCV})
MESSAGE(STATUS "Found OpenCV: ${PATH_OPENCV}")
ELSE()
MESSAGE(FATAL_ERROR "Can not find OpenCV in ${PATH_OPENCV}")
ENDIF()
# head file search path
INCLUDE_DIRECTORIES(
${PATH_SRC_OPENBLAS_FILES}
${PATH_SRC_OPENBLAS_FILES}/build # include openblas config.h
${PATH_SRC_DLPACK_FILES}/include
${PATH_SRC_MSHADOW_FILES}
${PATH_SRC_DMLC_FILES}/include
${PATH_SRC_TVM_FILES}/include
${PATH_SRC_TVM_FILES}/nnvm/include
${PATH_SRC_MXNET_FILES}/src
${PATH_SRC_MXNET_FILES}/include
${PATH_SRC_MXNET_FILES}/cpp-package/include
${PATH_OPENCV}/include
${PATH_TEST_FILES}
)
# build mxnet dynamic library
SET(MXNET_SRC_LIST )
# tvm
FILE(GLOB_RECURSE SRC_TVM_NNVM_C_API ${PATH_SRC_TVM_FILES}/nnvm/src/c_api/*.cc)
FILE(GLOB_RECURSE SRC_TVM_NNVM_CORE ${PATH_SRC_TVM_FILES}/nnvm/src/core/*.cc)
FILE(GLOB_RECURSE SRC_TVM_NNVM_PASS ${PATH_SRC_TVM_FILES}/nnvm/src/pass/*.cc)
FILE(GLOB_RECURSE SRC_MXNET ${PATH_SRC_MXNET_FILES}/src/*.cc)
LIST(APPEND MXNET_SRC_LIST
${SRC_TVM_NNVM_C_API}
${SRC_TVM_NNVM_CORE}
${SRC_TVM_NNVM_PASS}
${SRC_MXNET}
)
#MESSAGE(STATUS "mxnet src: ${MXNET_SRC_LIST}")
ADD_LIBRARY(mxnet SHARED ${MXNET_SRC_LIST})
# find opencv library
FIND_LIBRARY(opencv_core NAMES opencv_core PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_imgproc NAMES opencv_imgproc PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_highgui NAMES opencv_highgui PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_imgcodecs NAMES opencv_imgcodecs PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_video NAMES opencv_video PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_videoio NAMES opencv_videoio PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_objdetect NAMES opencv_objdetect PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
FIND_LIBRARY(opencv_ml NAMES opencv_ml PATHS ${PATH_OPENCV}/lib NO_DEFAULT_PATH)
MESSAGE(STATUS "opencv libraries: ${opencv_core} ${opencv_imgproc} ${opencv_highgui} ${opencv_imgcodecs} ${opencv_video}" ${opencv_videoio} ${opencv_objdetect} ${opencv_ml})
# find dep library
SET(DEP_LIB_DIR ${CMAKE_CURRENT_SOURCE_DIR}/build CACHE PATH "dep library path")
MESSAGE(STATUS "dep library dir: ${DEP_LIB_DIR}")
LINK_DIRECTORIES(${DEP_LIB_DIR})
# recursive query match files :*.cpp
FILE(GLOB_RECURSE TEST_CPP_LIST ${PATH_TEST_FILES}/*.cpp)
FILE(GLOB_RECURSE TEST_CC_LIST ${PATH_TEST_FILES}/*.cc)
MESSAGE(STATUS "test cpp list: ${TEST_CPP_LIST} ${TEST_C_LIST}")
# build executable program
ADD_EXECUTABLE(MXNet_Test ${TEST_CPP_LIST} ${TEST_CC_LIST})
# add dependent library: static and dynamic
TARGET_LINK_LIBRARIES(MXNet_Test
mxnet
${DEP_LIB_DIR}/libdmlc.a
${DEP_LIB_DIR}/libopenblas.so
pthread
rt # undefined reference to shm_open
${opencv_core}
${opencv_imgproc}
${opencv_highgui}
${opencv_imgcodecs}
${opencv_video}
${opencv_videoio}
${opencv_objdetect}
${opencv_ml}
)
注：对源码有两处修改：
1. OpenBLAS注释掉common.h中的第681行；
2. dmlc-core再用CMake编译时，关闭OpenMP的支持，即将CMakeLists.txt中的第18行由ON调整为OFF.
以下是MNIST train的测试代码：
#include "funset.hpp"
#include <chrono>
#include <string>
#include <fstream>
#include <vector>
#include "mxnet-cpp/MxNetCpp.h"
namespace {
bool isFileExists(const std::string &filename)
{
std::ifstream fhandle(filename.c_str());
return fhandle.good();
}
bool check_datafiles(const std::vector<std::string> &data_files)
{
for (size_t index = 0; index < data_files.size(); index++) {
if (!(isFileExists(data_files[index]))) {
LG << "Error: File does not exist: " << data_files[index];
return false;
}
}
return true;
}
bool setDataIter(mxnet::cpp::MXDataIter *iter, std::string useType, const std::vector<std::string> &data_files, int batch_size)
{
if (!check_datafiles(data_files))
return false;
iter->SetParam("batch_size", batch_size);
iter->SetParam("shuffle", 1);
iter->SetParam("flat", 1);
if (useType == "Train") {
iter->SetParam("image", data_files[0]);
iter->SetParam("label", data_files[1]);
} else if (useType == "Label") {
iter->SetParam("image", data_files[2]);
iter->SetParam("label", data_files[3]);
}
iter->CreateDataIter();
return true;
}
} // namespace
////////////////////////////// mnist ////////////////////////
/* reference:
https://mxnet.incubator.apache.org/tutorials/c%2B%2B/basics.html
mxnet_source/cpp-package/example/mlp_cpu.cpp
*/
namespace {
mxnet::cpp::Symbol mlp(const std::vector<int> &layers)
{
auto x = mxnet::cpp::Symbol::Variable("X");
auto label = mxnet::cpp::Symbol::Variable("label");
std::vector<mxnet::cpp::Symbol> weights(layers.size());
std::vector<mxnet::cpp::Symbol> biases(layers.size());
std::vector<mxnet::cpp::Symbol> outputs(layers.size());
for (size_t i = 0; i < layers.size(); ++i) {
weights[i] = mxnet::cpp::Symbol::Variable("w" + std::to_string(i));
biases[i] = mxnet::cpp::Symbol::Variable("b" + std::to_string(i));
mxnet::cpp::Symbol fc = mxnet::cpp::FullyConnected(i == 0 ? x : outputs[i - 1], weights[i], biases[i], layers[i]);
outputs[i] = i == layers.size() - 1 ? fc : mxnet::cpp::Activation(fc, mxnet::cpp::ActivationActType::kRelu);
}
return mxnet::cpp::SoftmaxOutput(outputs.back(), label);
}
} // namespace
int test_mnist_train()
{
const int image_size = 28;
const std::vector<int> layers{ 128, 64, 10 };
const int batch_size = 100;
const int max_epoch = 10;
const float learning_rate = 0.1;
const float weight_decay = 1e-2;
#ifdef _MSC_VER
std::vector<std::string> data_files = { "E:/GitCode/MXNet_Test/data/mnist/train-images.idx3-ubyte",
"E:/GitCode/MXNet_Test/data/mnist/train-labels.idx1-ubyte",
"E:/GitCode/MXNet_Test/data/mnist/t10k-images.idx3-ubyte",
"E:/GitCode/MXNet_Test/data/mnist/t10k-labels.idx1-ubyte"};
#else
std::vector<std::string> data_files = { "data/mnist/train-images.idx3-ubyte",
"data/mnist/train-labels.idx1-ubyte",
"data/mnist/t10k-images.idx3-ubyte",
"data/mnist/t10k-labels.idx1-ubyte"};
#endif
auto train_iter = mxnet::cpp::MXDataIter("MNISTIter");
setDataIter(&train_iter, "Train", data_files, batch_size);
auto val_iter = mxnet::cpp::MXDataIter("MNISTIter");
setDataIter(&val_iter, "Label", data_files, batch_size);
auto net = mlp(layers);
mxnet::cpp::Context ctx = mxnet::cpp::Context::cpu(); // Use CPU for training
std::map<std::string, mxnet::cpp::NDArray> args;
args["X"] = mxnet::cpp::NDArray(mxnet::cpp::Shape(batch_size, image_size*image_size), ctx);
args["label"] = mxnet::cpp::NDArray(mxnet::cpp::Shape(batch_size), ctx);
// Let MXNet infer shapes other parameters such as weights
net.InferArgsMap(ctx, &args, args);
// Initialize all parameters with uniform distribution U(-0.01, 0.01)
auto initializer = mxnet::cpp::Uniform(0.01);
for (auto& arg : args) {
// arg.first is parameter name, and arg.second is the value
initializer(arg.first, &arg.second);
}
// Create sgd optimizer
mxnet::cpp::Optimizer* opt = mxnet::cpp::OptimizerRegistry::Find("sgd");
opt->SetParam("rescale_grad", 1.0 / batch_size)->SetParam("lr", learning_rate)->SetParam("wd", weight_decay);
// Create executor by binding parameters to the model
auto *exec = net.SimpleBind(ctx, args);
auto arg_names = net.ListArguments();
// Start training
for (int iter = 0; iter < max_epoch; ++iter) {
int samples = 0;
train_iter.Reset();
auto tic = std::chrono::system_clock::now();
while (train_iter.Next()) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();
// Set data and label
data_batch.data.CopyTo(&args["X"]);
data_batch.label.CopyTo(&args["label"]);
// Compute gradients
exec->Forward(true);
exec->Backward();
// Update parameters
for (size_t i = 0; i < arg_names.size(); ++i) {
if (arg_names[i] == "X" || arg_names[i] == "label") continue;
opt->Update(i, exec->arg_arrays[i], exec->grad_arrays[i]);
}
}
auto toc = std::chrono::system_clock::now();
mxnet::cpp::Accuracy acc;
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args["X"]);
data_batch.label.CopyTo(&args["label"]);
// Forward pass is enough as no gradient is needed when evaluating
exec->Forward(false);
acc.Update(data_batch.label, exec->outputs[0]);
}
float duration = std::chrono::duration_cast<std::chrono::milliseconds>
(toc - tic).count() / 1000.0;
LG << "Epoch: " << iter << " " << samples / duration << " samples/sec Accuracy: " << acc.Get();
}
#ifdef _MSC_VER
std::string json_file{ "E:/GitCode/MXNet_Test/data/mnist.json" };
std::string param_file{"E:/GitCode/MXNet_Test/data/mnist.params"};
#else
std::string json_file{ "data/mnist.json" };
std::string param_file{"data/mnist.params"};
#endif
net.Save(json_file);
mxnet::cpp::NDArray::Save(param_file, exec->arg_arrays);
delete exec;
MXNotifyShutdown();
return 0;
}执行结果如下：
