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119 lines
3.2 KiB
119 lines
3.2 KiB
#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 "adadelta_optimizer.h"
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#include "adagrad_optimizer.h"
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#include "adam_optimizer.h"
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#include "gtest/gtest.h"
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#include "sgd_optimizer.h"
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using namespace paddle;
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using namespace paddle::optimizer;
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Tensor* FillTensor(size_t size) {
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Tensor* param = new Tensor(size);
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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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Tensor* FixedTensor(size_t size) {
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Tensor* param = new Tensor(size);
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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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// init 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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Tensor* parameter = FillTensor(kSize);
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config_.set_optimizer(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(OptimizerConfig::ConstLr);
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config_.mutable_const_lr()->set_learning_rate(0.1);
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ParameterOptimizer* opt =
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ParameterOptimizer::Create(config_.SerializeAsString(), parameter);
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opts_.push_back(opt);
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opts_table_[opts_.size()] = OptimizerConfig::SGD;
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}
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void CreateAdam() {
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Tensor* parameter = FixedTensor(kSize);
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config_.set_optimizer(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(OptimizerConfig::ConstLr);
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config_.mutable_const_lr()->set_learning_rate(0.1);
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ParameterOptimizer* opt =
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ParameterOptimizer::Create(config_.SerializeAsString(), parameter);
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opts_.push_back(opt);
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opts_table_[opts_.size()] = OptimizerConfig::Adam;
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}
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void TestGetWeight() {
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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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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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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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std::map<OptimizerConfig::Optimizer, int> expected_state_len = {
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{OptimizerConfig::SGD, kSize}, {OptimizerConfig::Adam, kSize * 3},
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};
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for (size_t i = 0; i < opts_.size(); ++i) {
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int state_len = 0;
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std::string state = opts_[i]->SerializeState(&state_len);
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EXPECT_EQ(state_len, expected_state_len[opts_table_[i]]);
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opts_[i]->DeserializeState(state);
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}
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}
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private:
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std::vector<ParameterOptimizer*> opts_;
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std::map<int, OptimizerConfig::Optimizer> opts_table_;
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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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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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