CaffeOnACL/src/caffe/solver.cpp at master · pyknife/CaffeOnACL · GitHub

Repository navigation

History

490 lines (455 loc) · 17.1 KB

Raw

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

#include <cstdio>

#include <string>

#include <vector>

#include "caffe/solver.hpp"

#include "caffe/util/format.hpp"

#include "caffe/util/hdf5.hpp"

#include "caffe/util/io.hpp"

#include "caffe/util/upgrade_proto.hpp"

namespace caffe {

template<typename Dtype>

void Solver<Dtype>::SetActionFunction(ActionCallback func) {

action_request_function_ = func;

}

template<typename Dtype>

SolverAction::Enum Solver<Dtype>::GetRequestedAction() {

if (action_request_function_) {

// If the external request function has been set, call it.

return action_request_function_();

}

return SolverAction::NONE;

}

template <typename Dtype>

Solver<Dtype>::Solver(const SolverParameter& param)

: net_(), callbacks_(), requested_early_exit_(false) {

Init(param);

}

template <typename Dtype>

Solver<Dtype>::Solver(const string& param_file)

: net_(), callbacks_(), requested_early_exit_(false) {

SolverParameter param;

ReadSolverParamsFromTextFileOrDie(param_file, &param);

Init(param);

}

template <typename Dtype>

void Solver<Dtype>::Init(const SolverParameter& param) {

LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "

<< std::endl << param.DebugString();

param_ = param;

CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";

CheckSnapshotWritePermissions();

if (param_.random_seed() >= 0) {

Caffe::set_random_seed(param_.random_seed() + Caffe::solver_rank());

}

// Scaffolding code

InitTrainNet();

if (Caffe::root_solver()) {

InitTestNets();

LOG(INFO) << "Solver scaffolding done.";

}

iter_ = 0;

current_step_ = 0;

}

template <typename Dtype>

void Solver<Dtype>::InitTrainNet() {

const int num_train_nets = param_.has_net() + param_.has_net_param() +

param_.has_train_net() + param_.has_train_net_param();

const string& field_names = "net, net_param, train_net, train_net_param";

CHECK_GE(num_train_nets, 1) << "SolverParameter must specify a train net "

<< "using one of these fields: " << field_names;

CHECK_LE(num_train_nets, 1) << "SolverParameter must not contain more than "

<< "one of these fields specifying a train_net: " << field_names;

NetParameter net_param;

if (param_.has_train_net_param()) {

LOG_IF(INFO, Caffe::root_solver())

<< "Creating training net specified in train_net_param.";

net_param.CopyFrom(param_.train_net_param());

} else if (param_.has_train_net()) {

LOG_IF(INFO, Caffe::root_solver())

<< "Creating training net from train_net file: " << param_.train_net();

ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);

}

if (param_.has_net_param()) {

LOG_IF(INFO, Caffe::root_solver())

<< "Creating training net specified in net_param.";

net_param.CopyFrom(param_.net_param());

}

if (param_.has_net()) {

LOG_IF(INFO, Caffe::root_solver())

<< "Creating training net from net file: " << param_.net();

ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);

}

// Set the correct NetState. We start with the solver defaults (lowest

// precedence); then, merge in any NetState specified by the net_param itself;

// finally, merge in any NetState specified by the train_state (highest

// precedence).

NetState net_state;

net_state.set_phase(TRAIN);

net_state.MergeFrom(net_param.state());

net_state.MergeFrom(param_.train_state());

net_param.mutable_state()->CopyFrom(net_state);

net_.reset(new Net<Dtype>(net_param));

}

template <typename Dtype>

void Solver<Dtype>::InitTestNets() {

CHECK(Caffe::root_solver());

const bool has_net_param = param_.has_net_param();

const bool has_net_file = param_.has_net();

const int num_generic_nets = has_net_param + has_net_file;

CHECK_LE(num_generic_nets, 1)

<< "Both net_param and net_file may not be specified.";

const int num_test_net_params = param_.test_net_param_size();

const int num_test_net_files = param_.test_net_size();

const int num_test_nets = num_test_net_params + num_test_net_files;

if (num_generic_nets) {

CHECK_GE(param_.test_iter_size(), num_test_nets)

<< "test_iter must be specified for each test network.";

} else {

CHECK_EQ(param_.test_iter_size(), num_test_nets)

<< "test_iter must be specified for each test network.";

}

// If we have a generic net (specified by net or net_param, rather than

// test_net or test_net_param), we may have an unlimited number of actual

// test networks -- the actual number is given by the number of remaining

// test_iters after any test nets specified by test_net_param and/or test_net

// are evaluated.

const int num_generic_net_instances = param_.test_iter_size() - num_test_nets;

const int num_test_net_instances = num_test_nets + num_generic_net_instances;

if (param_.test_state_size()) {

CHECK_EQ(param_.test_state_size(), num_test_net_instances)

<< "test_state must be unspecified or specified once per test net.";

}

if (num_test_net_instances) {

CHECK_GT(param_.test_interval(), 0);

}

int test_net_id = 0;

vector<string> sources(num_test_net_instances);

vector<NetParameter> net_params(num_test_net_instances);

for (int i = 0; i < num_test_net_params; ++i, ++test_net_id) {

sources[test_net_id] = "test_net_param";

net_params[test_net_id].CopyFrom(param_.test_net_param(i));

}

for (int i = 0; i < num_test_net_files; ++i, ++test_net_id) {

sources[test_net_id] = "test_net file: " + param_.test_net(i);

ReadNetParamsFromTextFileOrDie(param_.test_net(i),

&net_params[test_net_id]);

}

const int remaining_test_nets = param_.test_iter_size() - test_net_id;

if (has_net_param) {

for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {

sources[test_net_id] = "net_param";

net_params[test_net_id].CopyFrom(param_.net_param());

}

if (has_net_file) {

for (int i = 0; i < remaining_test_nets; ++i, ++test_net_id) {

sources[test_net_id] = "net file: " + param_.net();

ReadNetParamsFromTextFileOrDie(param_.net(), &net_params[test_net_id]);

}

test_nets_.resize(num_test_net_instances);

for (int i = 0; i < num_test_net_instances; ++i) {

// Set the correct NetState. We start with the solver defaults (lowest

// precedence); then, merge in any NetState specified by the net_param

// itself; finally, merge in any NetState specified by the test_state

// (highest precedence).

NetState net_state;

net_state.set_phase(TEST);

net_state.MergeFrom(net_params[i].state());

if (param_.test_state_size()) {

net_state.MergeFrom(param_.test_state(i));

}

net_params[i].mutable_state()->CopyFrom(net_state);

LOG(INFO)

<< "Creating test net (#" << i << ") specified by " << sources[i];

test_nets_[i].reset(new Net<Dtype>(net_params[i]));

test_nets_[i]->set_debug_info(param_.debug_info());

}

template <typename Dtype>

void Solver<Dtype>::Step(int iters) {

const int start_iter = iter_;

const int stop_iter = iter_ + iters;

int average_loss = this->param_.average_loss();

losses_.clear();

smoothed_loss_ = 0;

iteration_timer_.Start();

while (iter_ < stop_iter) {

// zero-init the params

net_->ClearParamDiffs();

if (param_.test_interval() && iter_ % param_.test_interval() == 0

&& (iter_ > 0 || param_.test_initialization())) {

if (Caffe::root_solver()) {

TestAll();

}

if (requested_early_exit_) {

// Break out of the while loop because stop was requested while testing.

break;

}

for (int i = 0; i < callbacks_.size(); ++i) {

callbacks_[i]->on_start();

}

const bool display = param_.display() && iter_ % param_.display() == 0;

net_->set_debug_info(display && param_.debug_info());

// accumulate the loss and gradient

Dtype loss = 0;

for (int i = 0; i < param_.iter_size(); ++i) {

loss += net_->ForwardBackward();

}

loss /= param_.iter_size();

// average the loss across iterations for smoothed reporting

UpdateSmoothedLoss(loss, start_iter, average_loss);

if (display) {

float lapse = iteration_timer_.Seconds();

float per_s = (iter_ - iterations_last_) / (lapse ? lapse : 1);

LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_

<< " (" << per_s << " iter/s, " << lapse << "s/"

<< param_.display() << " iters), loss = " << smoothed_loss_;

iteration_timer_.Start();

iterations_last_ = iter_;

const vector<Blob<Dtype>*>& result = net_->output_blobs();

int score_index = 0;

for (int j = 0; j < result.size(); ++j) {

const Dtype* result_vec = result[j]->cpu_data();

const string& output_name =

net_->blob_names()[net_->output_blob_indices()[j]];

const Dtype loss_weight =

net_->blob_loss_weights()[net_->output_blob_indices()[j]];

for (int k = 0; k < result[j]->count(); ++k) {

ostringstream loss_msg_stream;

if (loss_weight) {

loss_msg_stream << " (* " << loss_weight

<< " = " << loss_weight * result_vec[k] << " loss)";

}

LOG_IF(INFO, Caffe::root_solver()) << " Train net output #"

<< score_index++ << ": " << output_name << " = "

<< result_vec[k] << loss_msg_stream.str();

}

for (int i = 0; i < callbacks_.size(); ++i) {

callbacks_[i]->on_gradients_ready();

}

ApplyUpdate();

// Increment the internal iter_ counter -- its value should always indicate

// the number of times the weights have been updated.

++iter_;

SolverAction::Enum request = GetRequestedAction();

// Save a snapshot if needed.

if ((param_.snapshot()

&& iter_ % param_.snapshot() == 0

&& Caffe::root_solver()) ||

(request == SolverAction::SNAPSHOT)) {

Snapshot();

}

if (SolverAction::STOP == request) {

requested_early_exit_ = true;

// Break out of training loop.

break;

}

template <typename Dtype>

void Solver<Dtype>::Solve(const char* resume_file) {

CHECK(Caffe::root_solver());

LOG(INFO) << "Solving " << net_->name();

LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();

// Initialize to false every time we start solving.

requested_early_exit_ = false;

if (resume_file) {

LOG(INFO) << "Restoring previous solver status from " << resume_file;

Restore(resume_file);

}

// For a network that is trained by the solver, no bottom or top vecs

// should be given, and we will just provide dummy vecs.

int start_iter = iter_;

Step(param_.max_iter() - iter_);

// If we haven't already, save a snapshot after optimization, unless

// overridden by setting snapshot_after_train := false

if (param_.snapshot_after_train()

&& (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {

Snapshot();

}

if (requested_early_exit_) {

LOG(INFO) << "Optimization stopped early.";

return;

}

// After the optimization is done, run an additional train and test pass to

// display the train and test loss/outputs if appropriate (based on the

// display and test_interval settings, respectively). Unlike in the rest of

// training, for the train net we only run a forward pass as we've already

// updated the parameters "max_iter" times -- this final pass is only done to

// display the loss, which is computed in the forward pass.

if (param_.display() && iter_ % param_.display() == 0) {

int average_loss = this->param_.average_loss();

Dtype loss;

net_->Forward(&loss);

UpdateSmoothedLoss(loss, start_iter, average_loss);

LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_;

}

if (param_.test_interval() && iter_ % param_.test_interval() == 0) {

TestAll();

}

LOG(INFO) << "Optimization Done.";

}

template <typename Dtype>

void Solver<Dtype>::TestAll() {

for (int test_net_id = 0;

test_net_id < test_nets_.size() && !requested_early_exit_;

++test_net_id) {

Test(test_net_id);

}

template <typename Dtype>

void Solver<Dtype>::Test(const int test_net_id) {

CHECK(Caffe::root_solver());

LOG(INFO) << "Iteration " << iter_

<< ", Testing net (#" << test_net_id << ")";

CHECK_NOTNULL(test_nets_[test_net_id].get())->

ShareTrainedLayersWith(net_.get());

vector<Dtype> test_score;

vector<int> test_score_output_id;

const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];

Dtype loss = 0;

for (int i = 0; i < param_.test_iter(test_net_id); ++i) {

SolverAction::Enum request = GetRequestedAction();

// Check to see if stoppage of testing/training has been requested.

while (request != SolverAction::NONE) {

if (SolverAction::SNAPSHOT == request) {

Snapshot();

} else if (SolverAction::STOP == request) {

requested_early_exit_ = true;

}

request = GetRequestedAction();

}

if (requested_early_exit_) {

// break out of test loop.

break;

}

Dtype iter_loss;

const vector<Blob<Dtype>*>& result =

test_net->Forward(&iter_loss);

if (param_.test_compute_loss()) {

loss += iter_loss;

}

if (i == 0) {

for (int j = 0; j < result.size(); ++j) {

const Dtype* result_vec = result[j]->cpu_data();

for (int k = 0; k < result[j]->count(); ++k) {

test_score.push_back(result_vec[k]);

test_score_output_id.push_back(j);

}

} else {

int idx = 0;

for (int j = 0; j < result.size(); ++j) {

const Dtype* result_vec = result[j]->cpu_data();

for (int k = 0; k < result[j]->count(); ++k) {

test_score[idx++] += result_vec[k];

}

if (requested_early_exit_) {

LOG(INFO) << "Test interrupted.";

return;

}

if (param_.test_compute_loss()) {

loss /= param_.test_iter(test_net_id);

LOG(INFO) << "Test loss: " << loss;

}

for (int i = 0; i < test_score.size(); ++i) {

const int output_blob_index =

test_net->output_blob_indices()[test_score_output_id[i]];

const string& output_name = test_net->blob_names()[output_blob_index];

const Dtype loss_weight = test_net->blob_loss_weights()[output_blob_index];

ostringstream loss_msg_stream;

const Dtype mean_score = test_score[i] / param_.test_iter(test_net_id);

if (loss_weight) {

loss_msg_stream << " (* " << loss_weight

<< " = " << loss_weight * mean_score << " loss)";

}

LOG(INFO) << " Test net output #" << i << ": " << output_name << " = "

<< mean_score << loss_msg_stream.str();

}

template <typename Dtype>

void Solver<Dtype>::Snapshot() {

CHECK(Caffe::root_solver());

string model_filename;

switch (param_.snapshot_format()) {

case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:

model_filename = SnapshotToBinaryProto();

break;

case caffe::SolverParameter_SnapshotFormat_HDF5:

model_filename = SnapshotToHDF5();

break;

default:

LOG(FATAL) << "Unsupported snapshot format.";

}

SnapshotSolverState(model_filename);

}

template <typename Dtype>

void Solver<Dtype>::CheckSnapshotWritePermissions() {

if (Caffe::root_solver() && param_.snapshot()) {

CHECK(param_.has_snapshot_prefix())

<< "In solver params, snapshot is specified but snapshot_prefix is not";

string probe_filename = SnapshotFilename(".tempfile");

std::ofstream probe_ofs(probe_filename.c_str());

if (probe_ofs.good()) {

probe_ofs.close();

std::remove(probe_filename.c_str());

} else {

LOG(FATAL) << "Cannot write to snapshot prefix '"

<< param_.snapshot_prefix() << "'. Make sure "

<< "that the directory exists and is writeable.";

}

template <typename Dtype>

string Solver<Dtype>::SnapshotFilename(const string extension) {

return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)

+ extension;

}

template <typename Dtype>

string Solver<Dtype>::SnapshotToBinaryProto() {

string model_filename = SnapshotFilename(".caffemodel");

LOG(INFO) << "Snapshotting to binary proto file " << model_filename;

NetParameter net_param;

net_->ToProto(&net_param, param_.snapshot_diff());

WriteProtoToBinaryFile(net_param, model_filename);

return model_filename;

}

template <typename Dtype>

string Solver<Dtype>::SnapshotToHDF5() {

string model_filename = SnapshotFilename(".caffemodel.h5");

LOG(INFO) << "Snapshotting to HDF5 file " << model_filename;

net_->ToHDF5(model_filename, param_.snapshot_diff());

return model_filename;

}

template <typename Dtype>

void Solver<Dtype>::Restore(const char* state_file) {

string state_filename(state_file);

if (state_filename.size() >= 3 &&

state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {

RestoreSolverStateFromHDF5(state_filename);

} else {

RestoreSolverStateFromBinaryProto(state_filename);

}

template <typename Dtype>

void Solver<Dtype>::UpdateSmoothedLoss(Dtype loss, int start_iter,

int average_loss) {

if (losses_.size() < average_loss) {

losses_.push_back(loss);

int size = losses_.size();

smoothed_loss_ = (smoothed_loss_ * (size - 1) + loss) / size;

} else {

int idx = (iter_ - start_iter) % average_loss;

smoothed_loss_ += (loss - losses_[idx]) / average_loss;

losses_[idx] = loss;

}

INSTANTIATE_CLASS(Solver);

} // namespace caffe

CaffeOnACL/src/caffe/solver.cpp at master · pyknife/CaffeOnACL · GitHub

Repository navigation

Expand file tree

Breadcrumbs

solver.cpp

Latest commit

History

Breadcrumbs

solver.cpp

File metadata and controls