CaffeOnACL/python/caffe/_caffe.cpp at master · finley-/CaffeOnACL · GitHub

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#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"

#include "caffe/layers/memory_data_layer.hpp"

#include "caffe/layers/python_layer.hpp"

#include "caffe/sgd_solvers.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

/* Fix to avoid registration warnings in pycaffe (#3960) */

#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \

const boost::python::type_info info = \

boost::python::type_id<shared_ptr<PTR > >(); \

const boost::python::converter::registration* reg = \

boost::python::converter::registry::query(info); \

if (reg == NULL) { \

bp::register_ptr_to_python<shared_ptr<PTR > >(); \

} else if ((*reg).m_to_python == NULL) { \

bp::register_ptr_to_python<shared_ptr<PTR > >(); \

} \

} while (0)

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.

void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }

void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }

void InitLog(int level) {

FLAGS_logtostderr = 1;

FLAGS_minloglevel = level;

::google::InitGoogleLogging("");

::google::InstallFailureSignalHandler();

}

void InitLogInfo() {

InitLog(google::INFO);

}

void Log(const string& s) {

LOG(INFO) << s;

}

void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }

// 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 constructor

shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,

const int level, const bp::object& stages,

const bp::object& weights) {

CheckFile(network_file);

// Convert stages from list to vector

vector<string> stages_vector;

if (!stages.is_none()) {

for (int i = 0; i < len(stages); i++) {

stages_vector.push_back(bp::extract<string>(stages[i]));

}

// Initialize net

shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,

static_cast<Phase>(phase), level, &stages_vector));

// Load weights

if (!weights.is_none()) {

std::string weights_file_str = bp::extract<std::string>(weights);

CheckFile(weights_file_str);

net->CopyTrainedLayersFrom(weights_file_str);

}

return net;

}

// Legacy Net construct-and-load convenience constructor

shared_ptr<Net<Dtype> > Net_Init_Load(

string param_file, string pretrained_param_file, int phase) {

LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";

LOG(WARNING) << "Use this instead (with the named \"weights\""

<< " parameter):";

LOG(WARNING) << "Net('" << param_file << "', " << phase

<< ", weights='" << pretrained_param_file << "')";

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_SaveHDF5(const Net<Dtype>& net, string filename) {

net.ToHDF5(filename);

}

void Net_LoadHDF5(Net<Dtype>* net, string filename) {

net->CopyTrainedLayersFromHDF5(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<Dtype>* GetSolverFromFile(const string& filename) {

SolverParameter param;

ReadSolverParamsFromTextFileOrDie(filename, &param);

return SolverRegistry<Dtype>::CreateSolver(param);

}

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;

}

};

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();

}

template<typename Dtype>

class SolverCallback: public Solver<Dtype>::Callback {

protected:

bp::object on_start_, on_gradients_ready_;

public:

SolverCallback(bp::object on_start, bp::object on_gradients_ready)

: on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }

virtual void on_gradients_ready() {

on_gradients_ready_();

}

virtual void on_start() {

on_start_();

}

};

template<typename Dtype>

void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,

bp::object on_gradients_ready) {

solver->add_callback(new SolverCallback<Dtype>(on_start, on_gradients_ready));

}

// Seems boost cannot call the base method directly

void Solver_add_nccl(Solver<Dtype>* solver

#ifdef USE_NCCL

, NCCL<Dtype>* nccl

#endif

) {

#ifdef USE_NCCL

solver->add_callback(nccl);

#endif

}

template<typename Dtype>

class NetCallback: public Net<Dtype>::Callback {

public:

explicit NetCallback(bp::object run) : run_(run) {}

protected:

virtual void run(int layer) {

run_(layer);

}

bp::object run_;

};

void Net_before_forward(Net<Dtype>* net, bp::object run) {

net->add_before_forward(new NetCallback<Dtype>(run));

}

void Net_after_forward(Net<Dtype>* net, bp::object run) {

net->add_after_forward(new NetCallback<Dtype>(run));

}

void Net_before_backward(Net<Dtype>* net, bp::object run) {

net->add_before_backward(new NetCallback<Dtype>(run));

}

void Net_after_backward(Net<Dtype>* net, bp::object run) {

net->add_after_backward(new NetCallback<Dtype>(run));

}

void Net_add_nccl(Net<Dtype>* net

#ifdef USE_NCCL

, NCCL<Dtype>* nccl

#endif

) {

#ifdef USE_NCCL

net->add_after_backward(nccl);

#endif

}

#ifndef USE_NCCL

template<typename Dtype>

class NCCL {

public:

NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid) {}

};

#endif

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("init_log", &InitLog);

bp::def("init_log", &InitLogInfo);

bp::def("log", &Log);

bp::def("set_mode_cpu", &set_mode_cpu);

bp::def("set_mode_gpu", &set_mode_gpu);

bp::def("set_random_seed", &set_random_seed);

bp::def("set_device", &Caffe::SetDevice);

bp::def("solver_count", &Caffe::solver_count);

bp::def("set_solver_count", &Caffe::set_solver_count);

bp::def("solver_rank", &Caffe::solver_rank);

bp::def("set_solver_rank", &Caffe::set_solver_rank);

bp::def("set_multiprocess", &Caffe::set_multiprocess);

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

bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",

bp::no_init)

// Constructor

.def("__init__", bp::make_constructor(&Net_Init,

bp::default_call_policies(), (bp::arg("network_file"), "phase",

bp::arg("level")=0, bp::arg("stages")=bp::object(),

bp::arg("weights")=bp::object())))

// Legacy constructor

.def("__init__", bp::make_constructor(&Net_Init_Load))

.def("_forward", &Net<Dtype>::ForwardFromTo)

.def("_backward", &Net<Dtype>::BackwardFromTo)

.def("reshape", &Net<Dtype>::Reshape)

.def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)

// 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)

.def("save_hdf5", &Net_SaveHDF5)

.def("load_hdf5", &Net_LoadHDF5)

.def("before_forward", &Net_before_forward)

.def("after_forward", &Net_after_forward)

.def("before_backward", &Net_before_backward)

.def("after_backward", &Net_after_backward)

.def("after_backward", &Net_add_nccl);

BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);

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()));

BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);

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_SHARED_PTR_TO_PYTHON(Layer<Dtype>);

bp::class_<SolverParameter>("SolverParameter", bp::no_init)

.add_property("max_iter", &SolverParameter::max_iter)

.add_property("display", &SolverParameter::display)

.add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce);

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

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("add_callback", &Solver_add_callback<Dtype>)

.def("add_callback", &Solver_add_nccl)

.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)

.add_property("param", bp::make_function(&Solver<Dtype>::param,

bp::return_value_policy<bp::copy_const_reference>()));

BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);

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 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> >());

bp::class_<NCCL<Dtype>, shared_ptr<NCCL<Dtype> >,

boost::noncopyable>("NCCL",

bp::init<shared_ptr<Solver<Dtype> >, const string&>())

#ifdef USE_NCCL

.def("new_uid", &NCCL<Dtype>::new_uid).staticmethod("new_uid")

.def("bcast", &NCCL<Dtype>::Broadcast)

#endif

/* NOLINT_NEXT_LINE(whitespace/semicolon) */

;

BP_REGISTER_SHARED_PTR_TO_PYTHON(NCCL<Dtype>);

bp::class_<Timer, shared_ptr<Timer>, boost::noncopyable>(

"Timer", bp::init<>())

.def("start", &Timer::Start)

.def("stop", &Timer::Stop)

.add_property("ms", &Timer::MilliSeconds);

BP_REGISTER_SHARED_PTR_TO_PYTHON(Timer);

// 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

CaffeOnACL/python/caffe/_caffe.cpp at master · finley-/CaffeOnACL · GitHub

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