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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() {
::google::InitGoogleLogging("");
::google::InstallFailureSignalHandler();
}
void InitLogLevel(int level) {
FLAGS_minloglevel = level;
InitLog();
}
void InitLogLevelPipe(int level, bool stderr) {
FLAGS_minloglevel = level;
FLAGS_logtostderr = stderr;
InitLog();
}
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
}
void share_weights(Solver<Dtype>* solver, Net<Dtype>* net) {
net->ShareTrainedLayersWith(solver->net().get());
}
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
bool HasNCCL() {
#ifdef USE_NCCL
return true;
#else
return false;
#endif
}
#ifdef USE_NCCL
bp::object NCCL_New_Uid() {
std::string uid = NCCL<Dtype>::new_uid();
#if PY_MAJOR_VERSION >= 3
// Convert std::string to bytes so that Python does not
// try to decode the string using the current locale.
// Since boost 1.53 boost.python will convert str and bytes
// to std::string but will convert std::string to str. Here we
// force a bytes object to be returned. When this object
// is passed back to the NCCL constructor boost.python will
// correctly convert the bytes to std::string automatically
PyObject* py_uid = PyBytes_FromString(uid.c_str());
return bp::object(bp::handle<>(py_uid));
#else
// automatic conversion is correct for python 2.
return bp::object(uid);
#endif
}
#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", &InitLogLevel);
bp::def("init_log", &InitLogLevelPipe);
bp::def("log", &Log);
bp::def("has_nccl", &HasNCCL);
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))
#ifndef CPU_ONLY
.add_property("_gpu_data_ptr",
reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
&Blob<Dtype>::mutable_gpu_data))
.add_property("_gpu_diff_ptr",
reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
&Blob<Dtype>::mutable_gpu_diff))
#endif
.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)
.add_property("base_lr", &SolverParameter::base_lr,
&SolverParameter::set_base_lr);
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)
.def("share_weights", &share_weights)
.def("apply_update", &Solver<Dtype>::ApplyUpdate)
.add_property("param", bp::make_function(&Solver<Dtype>::param,
bp::return_internal_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>())
.add_property("lr", &SGDSolver<Dtype>::GetLearningRate);
bp::class_<NesterovSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
"NesterovSolver", bp::init<string>());
bp::class_<AdaGradSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
"AdaGradSolver", bp::init<string>());
bp::class_<RMSPropSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
"RMSPropSolver", bp::init<string>());
bp::class_<AdaDeltaSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
"AdaDeltaSolver", bp::init<string>());
bp::class_<AdamSolver<Dtype>, bp::bases<SGDSolver<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_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
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