caffe interface --- python

#include <Python.h>  // NOLINT(build/include_alpha)

// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION

#include <boost/make_shared.hpp>
#include <boost/python.hpp>
#include <boost/python/raw_function.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include <numpy/arrayobject.h>

// these need to be included after boost on OS X
#include <string>  // NOLINT(build/include_order)
#include <vector>  // NOLINT(build/include_order)
#include <fstream>  // NOLINT

#include "caffe/caffe.hpp"

/*     caffe.hpp
*****************************************
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/net.hpp"
#include "caffe/parallel.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/solver.hpp"
#include "caffe/solver_factory.hpp"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/upgrade_proto.hpp"
********************************************
*/

#include "caffe/layers/memory_data_layer.hpp"

/*     memory_data_layer.hpp
*****************************************
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
********************************************
*/

#include "caffe/layers/python_layer.hpp"

/*     python_layer.hpp
*****************************************
#include <boost/python.hpp>
#include <vector>
#include "caffe/layer.hpp"
********************************************
*/

#include "caffe/sgd_solvers.hpp"

/*     sgd_solvers.hpp
*****************************************
#include "caffe/solver.hpp"
********************************************
*/

// Temporary solution for numpy < 1.7 versions: old macro, no promises.
// You're strongly advised to upgrade to >= 1.7.
#ifndef NPY_ARRAY_C_CONTIGUOUS
#define NPY_ARRAY_C_CONTIGUOUS NPY_C_CONTIGUOUS
#define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
#endif

namespace bp = boost::python;

namespace caffe {

// For Python, for now, we'll just always use float as the type.
typedef float Dtype;
const int NPY_DTYPE = NPY_FLOAT32;

// Selecting mode.

// set_mode_gpu在Python中的使用方法为： caffe.set_mode_gpu()  caffe.set_device(0)
void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }

// check file，文件打开检测语句
// For convenience, check that input files can be opened, and raise an
// exception that boost will send to Python if not (caffe could still crash
// later if the input files are disturbed before they are actually used, but
// this saves frustration in most cases).
static void CheckFile(const string& filename) {
std::ifstream f(filename.c_str());
if (!f.good()) {
f.close();
throw std::runtime_error("Could not open file " + filename);
}
f.close();
}

void CheckContiguousArray(PyArrayObject* arr, string name,
int channels, int height, int width) {
if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
throw std::runtime_error(name + " must be C contiguous");
}
if (PyArray_NDIM(arr) != 4) {
throw std::runtime_error(name + " must be 4-d");
}
if (PyArray_TYPE(arr) != NPY_FLOAT32) {
throw std::runtime_error(name + " must be float32");
}
if (PyArray_DIMS(arr)[1] != channels) {
throw std::runtime_error(name + " has wrong number of channels");
}
if (PyArray_DIMS(arr)[2] != height) {
throw std::runtime_error(name + " has wrong height");
}
if (PyArray_DIMS(arr)[3] != width) {
throw std::runtime_error(name + " has wrong width");
}
}

/******************************************************************************/

/******************      Net           ******************/

/******************initialization / check file/ load file          ******************/

/*****************************************************************************/

//python::Net initialization
// Net constructor for passing phase as int
shared_ptr<Net<Dtype> > Net_Init(
string param_file, int phase) {
CheckFile(param_file);

shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
static_cast<Phase>(phase)));
return net;
}

// Net construct-and-load convenience constructor
shared_ptr<Net<Dtype> > Net_Init_Load(
string param_file, string pretrained_param_file, int phase) {
CheckFile(param_file);
CheckFile(pretrained_param_file);

shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
static_cast<Phase>(phase)));
net->CopyTrainedLayersFrom(pretrained_param_file);
return net;
}

void Net_Save(const Net<Dtype>& net, string filename) {
NetParameter net_param;
net.ToProto(&net_param, false);
WriteProtoToBinaryFile(net_param, filename.c_str());
}

void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
bp::object labels_obj) {
// check that this network has an input MemoryDataLayer
shared_ptr<MemoryDataLayer<Dtype> > md_layer =
boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
if (!md_layer) {
throw std::runtime_error("set_input_arrays may only be called if the"
" first layer is a MemoryDataLayer");
}

// check that we were passed appropriately-sized contiguous memory
PyArrayObject* data_arr =
reinterpret_cast<PyArrayObject*>(data_obj.ptr());
PyArrayObject* labels_arr =
reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
CheckContiguousArray(data_arr, "data array", md_layer->channels(),
md_layer->height(), md_layer->width());
CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
throw std::runtime_error("data and labels must have the same first"
" dimension");
}
if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
throw std::runtime_error("first dimensions of input arrays must be a"
" multiple of batch size");
}

md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
static_cast<Dtype*>(PyArray_DATA(labels_arr)),
PyArray_DIMS(data_arr)[0]);
}

/******************************************************************************/

/******************      Solver           ******************/

/*****************************************************************************/

Solver<Dtype>* GetSolverFromFile(const string& filename) {
SolverParameter param;
ReadSolverParamsFromTextFileOrDie(filename, ¶m);
return SolverRegistry<Dtype>::CreateSolver(param);
}

/******************************************************************************/

/******************      NdarrayConverterGenerator           ******************/

/******************使用下面的结构体将数组转换成blobs ******************/

/*****************************************************************************/

struct NdarrayConverterGenerator {
template <typename T> struct apply;
};

template <>
struct NdarrayConverterGenerator::apply<Dtype*> {
struct type {
PyObject* operator() (Dtype* data) const {
// Just store the data pointer, and add the shape information in postcall.
return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
}
const PyTypeObject* get_pytype() {
return &PyArray_Type;
}
};
};

struct NdarrayCallPolicies : public bp::default_call_policies {
typedef NdarrayConverterGenerator result_converter;
PyObject* postcall(PyObject* pyargs, PyObject* result) {
bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
shared_ptr<Blob<Dtype> > blob =
bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
// Free the temporary pointer-holding array, and construct a new one with
// the shape information from the blob.
void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
Py_DECREF(result);
const int num_axes = blob->num_axes();
vector<npy_intp> dims(blob->shape().begin(), blob->shape().end());
PyObject *arr_obj = PyArray_SimpleNewFromData(num_axes, dims.data(),
NPY_FLOAT32, data);
// SetBaseObject steals a ref, so we need to INCREF.
Py_INCREF(pyblob.ptr());
PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
pyblob.ptr());
return arr_obj;
}
};

/******************************************************************************/

/******************    Blob          ******************/

/****************** ******************/

/*****************************************************************************/

bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
if (bp::len(kwargs) > 0) {
throw std::runtime_error("Blob.reshape takes no kwargs");
}
Blob<Dtype>* self = bp::extract<Blob<Dtype>*>(args[0]);
vector<int> shape(bp::len(args) - 1);
for (int i = 1; i < bp::len(args); ++i) {
shape[i - 1] = bp::extract<int>(args[i]);
}
self->Reshape(shape);
// We need to explicitly return None to use bp::raw_function.
return bp::object();
}

bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
if (bp::len(kwargs) > 0) {
throw std::runtime_error("BlobVec.add_blob takes no kwargs");
}
typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
BlobVec* self = bp::extract<BlobVec*>(args[0]);
vector<int> shape(bp::len(args) - 1);
for (int i = 1; i < bp::len(args); ++i) {
shape[i - 1] = bp::extract<int>(args[i]);
}
self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
// We need to explicitly return None to use bp::raw_function.
return bp::object();
}

/******************************************************************************/

/****************** Using Boost.python wrapping the modules    ******************/

/****************生成_caffe命名空间下的python interface模块 ******************/

/*****************************************************************************/

BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);

BOOST_PYTHON_MODULE(_caffe) {
// below, we prepend an underscore to methods that will be replaced
// in Python

bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);

// Caffe utility functions
bp::def("set_mode_cpu", &set_mode_cpu);
bp::def("set_mode_gpu", &set_mode_gpu);
bp::def("set_device", &Caffe::SetDevice);

bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);

//使用boost::python::class_类将C++函数导出为python函数，def()导出类function，add_property()导出python的property

/********************************************/
/******************  Net  ******************/
/******************************************/
bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",bp::no_init)
.def("__init__", bp::make_constructor(&Net_Init))
.def("__init__", bp::make_constructor(&Net_Init_Load))
.def("_forward", &Net<Dtype>::ForwardFromTo)
.def("_backward", &Net<Dtype>::BackwardFromTo)
.def("reshape", &Net<Dtype>::Reshape)
// The cast is to select a particular overload.
.def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(&Net<Dtype>::CopyTrainedLayersFrom))
.def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
.add_property("_blob_loss_weights", bp::make_function(&Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
.def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,bp::return_value_policy<bp::copy_const_reference>()))
.def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,bp::return_value_policy<bp::copy_const_reference>()))
.add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,bp::return_internal_reference<>()))
.add_property("layers", bp::make_function(&Net<Dtype>::layers,bp::return_internal_reference<>()))
.add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,bp::return_value_policy<bp::copy_const_reference>()))
.add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,bp::return_value_policy<bp::copy_const_reference>()))
.add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,bp::return_value_policy<bp::copy_const_reference>()))
.add_property("_outputs",bp::make_function(&Net<Dtype>::output_blob_indices,bp::return_value_policy<bp::copy_const_reference>()))
.def("_set_input_arrays", &Net_SetInputArrays,bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
.def("save", &Net_Save);

/********************************************/
/******************  Blob  ******************/
/******************************************/
bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>("Blob", bp::no_init)
.add_property("shape",bp::make_function
(
static_cast<const vector<int>& (Blob<Dtype>::*)() const>(&Blob<Dtype>::shape),bp::return_value_policy<bp::copy_const_reference>())
)
.add_property("num",      &Blob<Dtype>::num)
.add_property("channels", &Blob<Dtype>::channels)
.add_property("height",   &Blob<Dtype>::height)
.add_property("width",    &Blob<Dtype>::width)
.add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(&Blob<Dtype>::count))
.def("reshape",           bp::raw_function(&Blob_Reshape))
.add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,NdarrayCallPolicies()))
.add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,NdarrayCallPolicies()));

/********************************************/
/******************  Layer  ******************/
/******************************************/
bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
.add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,bp::return_internal_reference<>()))
.def("setup", &Layer<Dtype>::LayerSetUp)
.def("reshape", &Layer<Dtype>::Reshape)
.add_property("type", bp::make_function(&Layer<Dtype>::type));

bp::register_ptr_to_python<shared_ptr<Layer<Dtype> > >();

bp::class_<LayerParameter>("LayerParameter", bp::no_init);

/********************************************/
/******************  Solver  ******************/
/******************************************/
bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
"Solver", bp::no_init)
.add_property("net", &Solver<Dtype>::net)
.add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,bp::return_internal_reference<>()))
.add_property("iter", &Solver<Dtype>::iter)
.def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(&Solver<Dtype>::Solve), SolveOverloads())
.def("step", &Solver<Dtype>::Step)
.def("restore", &Solver<Dtype>::Restore)
.def("snapshot", &Solver<Dtype>::Snapshot);

/********************************************/
/******************  SGDSolver  ******************/
/******************************************/
bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>("SGDSolver", bp::init<string>());
bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>("NesterovSolver", bp::init<string>());
bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>("AdaGradSolver", bp::init<string>());
bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>("RMSPropSolver", bp::init<string>());
bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>("AdaDeltaSolver", bp::init<string>());
bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>("AdamSolver", bp::init<string>());

bp::def("get_solver", &GetSolverFromFile,bp::return_value_policy<bp::manage_new_object>());

/********************************************/
/******************  vector  ******************/
/******************************************/
// vector wrappers for all the vector types we use
bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
.def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
.def("add_blob", bp::raw_function(&BlobVec_add_blob));
bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
.def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
.def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
bp::class_<vector<string> >("StringVec")
.def(bp::vector_indexing_suite<vector<string> >());
bp::class_<vector<int> >("IntVec")
.def(bp::vector_indexing_suite<vector<int> >());
bp::class_<vector<Dtype> >("DtypeVec")
.def(bp::vector_indexing_suite<vector<Dtype> >());
bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
.def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
bp::class_<vector<bool> >("BoolVec")
.def(bp::vector_indexing_suite<vector<bool> >());

// boost python expects a void (missing) return value, while import_array
// returns NULL for python3. import_array1() forces a void return value.
import_array1();
}

}  // namespace caffe