summaryrefslogtreecommitdiff
path: root/python/caffe/_caffe.cpp
blob: 7fc06c08f73d172e7a3b557f78f2bd8567924273 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
#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 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))
    .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)
    .def("share_weights", &share_weights)
    .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_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