Skip to content
Navigation Menu
{{ message }}
forked from BVLC/caffe
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathcaffe.cpp
More file actions
405 lines (371 loc) · 12.9 KB
/
Copy pathcaffe.cpp
File metadata and controls
405 lines (371 loc) · 12.9 KB
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
#ifdef WITH_PYTHON_LAYER
#include "boost/python.hpp"
namespace bp = boost::python;
#endif
#include <glog/logging.h>
#include <cstring>
#include <map>
#include <string>
#include <vector>
#include "boost/algorithm/string.hpp"
#include "caffe/caffe.hpp"
#include "caffe/util/signal_handler.h"
using caffe::Blob;
using caffe::Caffe;
using caffe::Net;
using caffe::Layer;
using caffe::Solver;
using caffe::shared_ptr;
using caffe::string;
using caffe::Timer;
using caffe::vector;
using std::ostringstream;
DEFINE_string(gpu, "",
"Optional; run in GPU mode on given device IDs separated by ','."
"Use '-gpu all' to run on all available GPUs. The effective training "
"batch size is multiplied by the number of devices.");
DEFINE_string(solver, "",
"The solver definition protocol buffer text file.");
DEFINE_string(model, "",
"The model definition protocol buffer text file..");
DEFINE_string(snapshot, "",
"Optional; the snapshot solver state to resume training.");
DEFINE_string(weights, "",
"Optional; the pretrained weights to initialize finetuning, "
"separated by ','. Cannot be set simultaneously with snapshot.");
DEFINE_int32(iterations, 50,
"The number of iterations to run.");
DEFINE_string(sigint_effect, "stop",
"Optional; action to take when a SIGINT signal is received: "
"snapshot, stop or none.");
DEFINE_string(sighup_effect, "snapshot",
"Optional; action to take when a SIGHUP signal is received: "
"snapshot, stop or none.");
// A simple registry for caffe commands.
typedef int (*BrewFunction)();
typedef std::map<caffe::string, BrewFunction> BrewMap;
BrewMap g_brew_map;
#define RegisterBrewFunction(func) \
namespace { \
class __Registerer_##func { \
public: /* NOLINT */ \
__Registerer_##func() { \
g_brew_map[#func] = &func; \
} \
}; \
__Registerer_##func g_registerer_##func; \
}
static BrewFunction GetBrewFunction(const caffe::string& name) {
if (g_brew_map.count(name)) {
return g_brew_map[name];
} else {
LOG(ERROR) << "Available caffe actions:";
for (BrewMap::iterator it = g_brew_map.begin();
it != g_brew_map.end(); ++it) {
LOG(ERROR) << "\t" << it->first;
}
LOG(FATAL) << "Unknown action: " << name;
return NULL; // not reachable, just to suppress old compiler warnings.
}
}
// Parse GPU ids or use all available devices
static void get_gpus(vector<int>* gpus) {
if (FLAGS_gpu == "all") {
int count = 0;
#ifndef CPU_ONLY
CUDA_CHECK(cudaGetDeviceCount(&count));
#else
NO_GPU;
#endif
for (int i = 0; i < count; ++i) {
gpus->push_back(i);
}
} else if (FLAGS_gpu.size()) {
vector<string> strings;
boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
for (int i = 0; i < strings.size(); ++i) {
gpus->push_back(boost::lexical_cast<int>(strings[i]));
}
} else {
CHECK_EQ(gpus->size(), 0);
}
}
// caffe commands to call by
// caffe <command> <args>
//
// To add a command, define a function "int command()" and register it with
// RegisterBrewFunction(action);
// Device Query: show diagnostic information for a GPU device.
int device_query() {
LOG(INFO) << "Querying GPUs " << FLAGS_gpu;
vector<int> gpus;
get_gpus(&gpus);
for (int i = 0; i < gpus.size(); ++i) {
caffe::Caffe::SetDevice(gpus[i]);
caffe::Caffe::DeviceQuery();
}
return 0;
}
RegisterBrewFunction(device_query);
// Load the weights from the specified caffemodel(s) into the train and
// test nets.
void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
std::vector<std::string> model_names;
boost::split(model_names, model_list, boost::is_any_of(",") );
for (int i = 0; i < model_names.size(); ++i) {
LOG(INFO) << "Finetuning from " << model_names[i];
solver->net()->CopyTrainedLayersFrom(model_names[i]);
for (int j = 0; j < solver->test_nets().size(); ++j) {
solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);
}
}
}
// Translate the signal effect the user specified on the command-line to the
// corresponding enumeration.
caffe::SolverAction::Enum GetRequestedAction(
const std::string& flag_value) {
if (flag_value == "stop") {
return caffe::SolverAction::STOP;
}
if (flag_value == "snapshot") {
return caffe::SolverAction::SNAPSHOT;
}
if (flag_value == "none") {
return caffe::SolverAction::NONE;
}
LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified";
}
// Train / Finetune a model.
int train() {
CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size())
<< "Give a snapshot to resume training or weights to finetune "
"but not both.";
caffe::SolverParameter solver_param;
caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
// If the gpus flag is not provided, allow the mode and device to be set
// in the solver prototxt.
if (FLAGS_gpu.size() == 0
&& solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
if (solver_param.has_device_id()) {
FLAGS_gpu = "" +
boost::lexical_cast<string>(solver_param.device_id());
} else { // Set default GPU if unspecified
FLAGS_gpu = "" + boost::lexical_cast<string>(0);
}
}
vector<int> gpus;
get_gpus(&gpus);
if (gpus.size() == 0) {
LOG(INFO) << "Use CPU.";
Caffe::set_mode(Caffe::CPU);
} else {
ostringstream s;
for (int i = 0; i < gpus.size(); ++i) {
s << (i ? ", " : "") << gpus[i];
}
LOG(INFO) << "Using GPUs " << s.str();
solver_param.set_device_id(gpus[0]);
Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
Caffe::set_solver_count(gpus.size());
}
caffe::SignalHandler signal_handler(
GetRequestedAction(FLAGS_sigint_effect),
GetRequestedAction(FLAGS_sighup_effect));
shared_ptr<caffe::Solver<float> >
solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
solver->SetActionFunction(signal_handler.GetActionFunction());
if (FLAGS_snapshot.size()) {
LOG(INFO) << "Resuming from " << FLAGS_snapshot;
solver->Restore(FLAGS_snapshot.c_str());
} else if (FLAGS_weights.size()) {
CopyLayers(solver.get(), FLAGS_weights);
}
if (gpus.size() > 1) {
caffe::P2PSync<float> sync(solver, NULL, solver->param());
sync.run(gpus);
} else {
LOG(INFO) << "Starting Optimization";
solver->Solve();
}
LOG(INFO) << "Optimization Done.";
return 0;
}
RegisterBrewFunction(train);
// Test: score a model.
int test() {
CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to score.";
CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
// Set device id and mode
vector<int> gpus;
get_gpus(&gpus);
if (gpus.size() != 0) {
LOG(INFO) << "Use GPU with device ID " << gpus[0];
Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
} else {
LOG(INFO) << "Use CPU.";
Caffe::set_mode(Caffe::CPU);
}
// Instantiate the caffe net.
Net<float> caffe_net(FLAGS_model, caffe::TEST);
caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
vector<Blob<float>* > bottom_vec;
vector<int> test_score_output_id;
vector<float> test_score;
float loss = 0;
for (int i = 0; i < FLAGS_iterations; ++i) {
float iter_loss;
const vector<Blob<float>*>& result =
caffe_net.Forward(bottom_vec, &iter_loss);
loss += iter_loss;
int idx = 0;
for (int j = 0; j < result.size(); ++j) {
const float* result_vec = result[j]->cpu_data();
for (int k = 0; k < result[j]->count(); ++k, ++idx) {
const float score = result_vec[k];
if (i == 0) {
test_score.push_back(score);
test_score_output_id.push_back(j);
} else {
test_score[idx] += score;
}
const std::string& output_name = caffe_net.blob_names()[
caffe_net.output_blob_indices()[j]];
LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;
}
}
}
loss /= FLAGS_iterations;
LOG(INFO) << "Loss: " << loss;
for (int i = 0; i < test_score.size(); ++i) {
const std::string& output_name = caffe_net.blob_names()[
caffe_net.output_blob_indices()[test_score_output_id[i]]];
const float loss_weight = caffe_net.blob_loss_weights()[
caffe_net.output_blob_indices()[test_score_output_id[i]]];
std::ostringstream loss_msg_stream;
const float mean_score = test_score[i] / FLAGS_iterations;
if (loss_weight) {
loss_msg_stream << " (* " << loss_weight
<< " = " << loss_weight * mean_score << " loss)";
}
LOG(INFO) << output_name << " = " << mean_score << loss_msg_stream.str();
}
return 0;
}
RegisterBrewFunction(test);
// Time: benchmark the execution time of a model.
int time() {
CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
// Set device id and mode
vector<int> gpus;
get_gpus(&gpus);
if (gpus.size() != 0) {
LOG(INFO) << "Use GPU with device ID " << gpus[0];
Caffe::SetDevice(gpus[0]);
Caffe::set_mode(Caffe::GPU);
} else {
LOG(INFO) << "Use CPU.";
Caffe::set_mode(Caffe::CPU);
}
// Instantiate the caffe net.
Net<float> caffe_net(FLAGS_model, caffe::TRAIN);
// Do a clean forward and backward pass, so that memory allocation are done
// and future iterations will be more stable.
LOG(INFO) << "Performing Forward";
// Note that for the speed benchmark, we will assume that the network does
// not take any input blobs.
float initial_loss;
caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
LOG(INFO) << "Initial loss: " << initial_loss;
LOG(INFO) << "Performing Backward";
caffe_net.Backward();
const vector<shared_ptr<Layer<float> > >& layers = caffe_net.layers();
const vector<vector<Blob<float>*> >& bottom_vecs = caffe_net.bottom_vecs();
const vector<vector<Blob<float>*> >& top_vecs = caffe_net.top_vecs();
const vector<vector<bool> >& bottom_need_backward =
caffe_net.bottom_need_backward();
LOG(INFO) << "*** Benchmark begins ***";
LOG(INFO) << "Testing for " << FLAGS_iterations << " iterations.";
Timer total_timer;
total_timer.Start();
Timer forward_timer;
Timer backward_timer;
Timer timer;
std::vector<double> forward_time_per_layer(layers.size(), 0.0);
std::vector<double> backward_time_per_layer(layers.size(), 0.0);
double forward_time = 0.0;
double backward_time = 0.0;
for (int j = 0; j < FLAGS_iterations; ++j) {
Timer iter_timer;
iter_timer.Start();
forward_timer.Start();
for (int i = 0; i < layers.size(); ++i) {
timer.Start();
layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
forward_time_per_layer[i] += timer.MicroSeconds();
}
forward_time += forward_timer.MicroSeconds();
backward_timer.Start();
for (int i = layers.size() - 1; i >= 0; --i) {
timer.Start();
layers[i]->Backward(top_vecs[i], bottom_need_backward[i],
bottom_vecs[i]);
backward_time_per_layer[i] += timer.MicroSeconds();
}
backward_time += backward_timer.MicroSeconds();
LOG(INFO) << "Iteration: " << j + 1 << " forward-backward time: "
<< iter_timer.MilliSeconds() << " ms.";
}
LOG(INFO) << "Average time per layer: ";
for (int i = 0; i < layers.size(); ++i) {
const caffe::string& layername = layers[i]->layer_param().name();
LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
"\tforward: " << forward_time_per_layer[i] / 1000 /
FLAGS_iterations << " ms.";
LOG(INFO) << std::setfill(' ') << std::setw(10) << layername <<
"\tbackward: " << backward_time_per_layer[i] / 1000 /
FLAGS_iterations << " ms.";
}
total_timer.Stop();
LOG(INFO) << "Average Forward pass: " << forward_time / 1000 /
FLAGS_iterations << " ms.";
LOG(INFO) << "Average Backward pass: " << backward_time / 1000 /
FLAGS_iterations << " ms.";
LOG(INFO) << "Average Forward-Backward: " << total_timer.MilliSeconds() /
FLAGS_iterations << " ms.";
LOG(INFO) << "Total Time: " << total_timer.MilliSeconds() << " ms.";
LOG(INFO) << "*** Benchmark ends ***";
return 0;
}
RegisterBrewFunction(time);
int main(int argc, char** argv) {
// Print output to stderr (while still logging).
FLAGS_alsologtostderr = 1;
// Usage message.
gflags::SetUsageMessage("command line brew\n"
"usage: caffe <command> <args>\n\n"
"commands:\n"
" train train or finetune a model\n"
" test score a model\n"
" device_query show GPU diagnostic information\n"
" time benchmark model execution time");
// Run tool or show usage.
caffe::GlobalInit(&argc, &argv);
if (argc == 2) {
#ifdef WITH_PYTHON_LAYER
try {
#endif
return GetBrewFunction(caffe::string(argv[1]))();
#ifdef WITH_PYTHON_LAYER
} catch (bp::error_already_set) {
PyErr_Print();
return 1;
}
#endif
} else {
gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
}
}
You can’t perform that action at this time.