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128 lines
4.0 KiB
128 lines
4.0 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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 "parameter_optimizer.h"
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#include <cmath>
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#include <map>
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#include <vector>
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#include "gtest/gtest.h"
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#include "lr_policy.h"
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paddle::optimizer::Tensor* FillTensor(size_t size) {
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paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
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paddle::optimizer::Tensor& p = *param;
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for (size_t i = 0; i < p.size(); ++i) {
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p[i] = (float)rand() / (float)RAND_MAX;
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}
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return param;
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}
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paddle::optimizer::Tensor* FixedTensor(size_t size) {
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paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
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paddle::optimizer::Tensor& p = *param;
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for (size_t i = 0; i < p.size(); ++i) {
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p[i] = i;
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}
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return param;
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}
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class OptimizerTest : public testing::Test {
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public:
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virtual ~OptimizerTest() {}
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// init paddle::optimizer::Tensor shape
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const size_t kSize = 5;
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virtual void SetUp() {
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CreateSGD();
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CreateAdam();
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}
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virtual void TearDown() {}
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void CreateSGD() {
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paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
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config_.set_optimizer(paddle::OptimizerConfig::SGD);
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config_.mutable_sgd()->set_momentum(0.0);
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config_.mutable_sgd()->set_decay(0.0);
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config_.mutable_sgd()->set_nesterov(false);
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config_.set_lr_policy(paddle::OptimizerConfig::Const);
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config_.mutable_const_lr()->set_learning_rate(0.1);
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std::string str = config_.SerializeAsString();
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paddle::optimizer::ParameterOptimizer* opt =
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paddle::optimizer::ParameterOptimizer::Create(str, parameter);
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opts_.push_back(opt);
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}
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void CreateAdam() {
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paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
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config_.set_optimizer(paddle::OptimizerConfig::Adam);
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config_.mutable_adam()->set_beta_1(0.9);
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config_.mutable_adam()->set_beta_2(0.1);
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config_.mutable_adam()->set_epsilon(1e-3);
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config_.mutable_adam()->set_decay(0.0);
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config_.set_lr_policy(paddle::OptimizerConfig::Const);
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config_.mutable_const_lr()->set_learning_rate(0.1);
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std::string str = config_.SerializeAsString();
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paddle::optimizer::ParameterOptimizer* opt =
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paddle::optimizer::ParameterOptimizer::Create(str, parameter);
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opts_.push_back(opt);
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}
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void TestGetWeight() {
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paddle::optimizer::Tensor* p = FixedTensor(kSize);
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for (size_t i = 0; i < opts_.size(); ++i) {
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int s = 0;
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float* newp = (float*)opts_[i]->get_weight(&s);
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EXPECT_EQ(static_cast<size_t>(s), kSize);
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for (size_t j = 0; j < kSize; ++j) {
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EXPECT_EQ(newp[j], (*p)[j]);
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}
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}
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}
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void TestUpdate() {
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paddle::optimizer::Tensor* g = FixedTensor(kSize);
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for (size_t i = 0; i < opts_.size(); ++i) {
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opts_[i]->Update(g);
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}
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}
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void TestCheckPoint() {
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paddle::optimizer::Tensor* p = FixedTensor(kSize);
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for (size_t i = 0; i < opts_.size(); ++i) {
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auto state = opts_[i]->SerializeState();
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opts_[i]->DeserializeState(state);
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auto state1 = opts_[i]->SerializeState();
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opts_[i]->DeserializeState(state);
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EXPECT_EQ(state, state1);
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int s = 0;
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float* newp = (float*)opts_[i]->get_weight(&s);
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EXPECT_EQ(static_cast<size_t>(s), kSize);
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for (size_t j = 0; j < kSize; ++j) {
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EXPECT_EQ(newp[j], (*p)[j]);
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}
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}
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}
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private:
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std::vector<paddle::optimizer::ParameterOptimizer*> opts_;
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paddle::OptimizerConfig config_;
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};
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TEST_F(OptimizerTest, TestGetWeight) { TestGetWeight(); }
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TEST_F(OptimizerTest, TestUpdate) { TestUpdate(); }
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TEST_F(OptimizerTest, TestCheckPoint) { TestCheckPoint(); }
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