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105 lines
2.9 KiB
105 lines
2.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "GradientMachine.h"
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#include <fstream>
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#include "paddle/utils/Logging.h"
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#include "NeuralNetwork.h"
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#include "hl_gpu.h"
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#ifndef PADDLE_MOBILE_INFERENCE
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#include "GradientMachineMode.h"
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#include "MultiGradientMachine.h"
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#include "MultiNetwork.h"
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#include "ParallelNeuralNetwork.h"
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#endif
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namespace paddle {
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GradientMachine* GradientMachine::create(
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const ModelConfig& config,
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int mode,
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const std::vector<ParameterType>& parameterTypes) {
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#ifndef PADDLE_MOBILE_INFERENCE
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if (auto gm = IGradientMachineMode::tryCreateGradientMachine(mode, config)) {
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return gm;
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}
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if (FLAGS_trainer_count > 1) {
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return new MultiGradientMachine(config, FLAGS_use_gpu);
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}
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#endif
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if (FLAGS_trainer_count == 1) { // single
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#ifndef PADDLE_MOBILE_INFERENCE
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NeuralNetwork* nn;
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if (config.type() == "multi_nn") {
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/* multi submodel calculate, thread(s) will be initialized inside */
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nn = new MultiNetwork("root");
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} else if (FLAGS_parallel_nn) {
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/* multi threads calculate */
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nn = new ParallelNeuralNetwork();
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} else {
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/* single thread calculate */
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nn = NeuralNetwork::create(config);
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}
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#else
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NeuralNetwork* nn = NeuralNetwork::create(config);
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#endif
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ParamInitCallback testParamInitCb = [](int paramId, Parameter* para) {
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para->enableType(PARAMETER_VALUE);
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};
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nn->init(
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config, mode == kTesting ? testParamInitCb : nullptr, parameterTypes);
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return nn;
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}
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LOG(FATAL) << "Unknown model type: " << config.type();
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return nullptr;
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}
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void GradientMachine::saveParameters(const std::string& dir) const {
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LOG(INFO) << "Saving parameters to " << dir;
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for (auto& para : parameters_) {
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std::string filename = dir + "/" + para->getName();
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if (para->isFullSize()) {
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para->save(filename);
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}
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}
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}
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void GradientMachine::loadParameters(const std::string& dir) {
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LOG(INFO) << "Loading parameters from " << dir;
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for (auto& para : parameters_) {
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std::string filename = dir + "/" + para->getName();
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if (para->isFullSize()) {
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para->load(filename);
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}
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}
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}
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void GradientMachine::randParameters() {
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LOG(INFO) << "Initing parameters..";
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for (auto& para : parameters_) {
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if (para->isFullSize()) {
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para->randomize();
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}
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}
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LOG(INFO) << "Init parameters done.";
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}
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} // namespace paddle
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