提交 569f7e83 编写于 作者: L liaogang

FIX: cppint code style

上级 620575b6
......@@ -117,8 +117,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
......@@ -217,8 +216,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& input = outputs[0].shape();
......@@ -311,8 +309,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& output = inputs[0].shape();
const TensorShape& input = inputs[1].shape();
const TensorShape& filter = outputs[0].shape();
......
......@@ -90,8 +90,7 @@ public:
ConvFunctionBase::init(config);
}
virtual void check(const BufferArgs& inputs,
const BufferArgs& outputs) override {
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
......
......@@ -403,7 +403,7 @@ public:
: layerName_(layerName) {
addEvaluator(std::move(evaluator));
}
virtual void eval(const NeuralNetwork& nn) override {
void eval(const NeuralNetwork& nn) override {
const LayerPtr& layer = nn.getLayer(layerName_);
CHECK(layer) << "Nonexisted layer: " << layerName_ << " in submodel "
<< nn.getName();
......
......@@ -636,7 +636,7 @@ void lenToStarts(std::vector<int>& starts) {
}
starts.back() = pos;
}
}
} // namespace
void RecurrentGradientMachine::calcSequenceStartPositions() {
std::vector<int> starts(commonSeqInfo_.size() + 1);
......
......@@ -124,7 +124,7 @@ void copyElements(const IVector& srcVec,
dest[index[i]] = src[i];
}
}
}
} // namespace
void GatherAgentLayer::forwardIds(PassType passType) {
IVectorPtr realId = realLayers_[0]->getOutputLabel();
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/framework/op_registry.h>
#include <paddle/framework/tensor.h>
#include <paddle/operators/add_op.h>
......@@ -36,7 +50,7 @@ The equation is: Out = X + Y
)DOC");
}
};
} // namespace op
} // namespace operators
} // namespace paddle
REGISTER_OP(add_two, paddle::operators::AddOp, paddle::operators::AddOpMaker);
......
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "parameter_optimizer.h"
#include <cmath>
#include <map>
......@@ -5,21 +21,18 @@
#include "gtest/gtest.h"
#include "lr_policy.h"
using namespace paddle;
using namespace paddle::optimizer;
Tensor* FillTensor(size_t size) {
Tensor* param = new Tensor(size);
Tensor& p = *param;
paddle::optimizer::Tensor* FillTensor(size_t size) {
paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
paddle::optimizer::Tensor& p = *param;
for (size_t i = 0; i < p.size(); ++i) {
p[i] = (float)rand() / (float)RAND_MAX;
}
return param;
}
Tensor* FixedTensor(size_t size) {
Tensor* param = new Tensor(size);
Tensor& p = *param;
paddle::optimizer::Tensor* FixedTensor(size_t size) {
paddle::optimizer::Tensor* param = new paddle::optimizer::Tensor(size);
paddle::optimizer::Tensor& p = *param;
for (size_t i = 0; i < p.size(); ++i) {
p[i] = i;
}
......@@ -28,7 +41,8 @@ Tensor* FixedTensor(size_t size) {
class OptimizerTest : public testing::Test {
public:
// init tensor shape
virtual ~OptimizerTest();
// init paddle::optimizer::Tensor shape
const size_t kSize = 5;
virtual void SetUp() {
......@@ -38,34 +52,36 @@ public:
virtual void TearDown() {}
void CreateSGD() {
Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(OptimizerConfig::SGD);
paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(paddle::OptimizerConfig::SGD);
config_.mutable_sgd()->set_momentum(0.0);
config_.mutable_sgd()->set_decay(0.0);
config_.mutable_sgd()->set_nesterov(false);
config_.set_lr_policy(OptimizerConfig::Const);
config_.set_lr_policy(paddle::OptimizerConfig::Const);
config_.mutable_const_lr()->set_learning_rate(0.1);
std::string str = config_.SerializeAsString();
ParameterOptimizer* opt = ParameterOptimizer::Create(str, parameter);
paddle::optimizer::ParameterOptimizer* opt =
paddle::optimizer::ParameterOptimizer::Create(str, parameter);
opts_.push_back(opt);
}
void CreateAdam() {
Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(OptimizerConfig::Adam);
paddle::optimizer::Tensor* parameter = FixedTensor(kSize);
config_.set_optimizer(paddle::OptimizerConfig::Adam);
config_.mutable_adam()->set_beta_1(0.9);
config_.mutable_adam()->set_beta_2(0.1);
config_.mutable_adam()->set_epsilon(1e-3);
config_.mutable_adam()->set_decay(0.0);
config_.set_lr_policy(OptimizerConfig::Const);
config_.set_lr_policy(paddle::OptimizerConfig::Const);
config_.mutable_const_lr()->set_learning_rate(0.1);
std::string str = config_.SerializeAsString();
ParameterOptimizer* opt = ParameterOptimizer::Create(str, parameter);
paddle::optimizer::ParameterOptimizer* opt =
paddle::optimizer::ParameterOptimizer::Create(str, parameter);
opts_.push_back(opt);
}
void TestGetWeight() {
Tensor* p = FixedTensor(kSize);
paddle::optimizer::Tensor* p = FixedTensor(kSize);
for (size_t i = 0; i < opts_.size(); ++i) {
int s = 0;
float* newp = (float*)opts_[i]->get_weight(&s);
......@@ -76,7 +92,7 @@ public:
}
void TestUpdate() {
Tensor* g = FixedTensor(kSize);
paddle::optimizer::Tensor* g = FixedTensor(kSize);
for (size_t i = 0; i < opts_.size(); ++i) {
opts_[i]->Update(g);
}
......@@ -91,8 +107,8 @@ public:
}
private:
std::vector<ParameterOptimizer*> opts_;
OptimizerConfig config_;
std::vector<paddle::optimizer::ParameterOptimizer*> opts_;
paddle::OptimizerConfig config_;
};
TEST_F(OptimizerTest, TestGetWeight) { TestGetWeight(); }
......
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include "serialization.h"
#include "gtest/gtest.h"
using namespace paddle;
using namespace paddle::optimizer;
TEST(TensorToProto, Case1) {
Tensor t(3), t1(3);
paddle::optimizer::Tensor t(3), t1(3);
for (size_t i = 0; i < t.size(); ++i) {
t[i] = i;
t1[i] = 0;
}
TensorProto proto;
TensorToProto(t, &proto);
ProtoToTensor(proto, &t1);
paddle::TensorProto proto;
paddle::optimizer::TensorToProto(t, &proto);
paddle::optimizer::ProtoToTensor(proto, &t1);
for (size_t i = 0; i < t1.size(); ++i) {
EXPECT_EQ(t1[i], t[i]);
}
......
......@@ -12,8 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef DYNAMIC_LOAD_H_
#define DYNAMIC_LOAD_H_
#pragma once
#include <dlfcn.h>
#include <memory>
......@@ -59,5 +58,3 @@ void GetWarpCTCDsoHandle(void** dso_handle);
*
*/
void GetLapackDsoHandle(void** dso_handle);
#endif // DYNAMIC_LOAD_H_
......@@ -51,7 +51,7 @@ template <class T>
class ThreadLocal {
public:
ThreadLocal() {
CHECK(pthread_key_create(&threadSpecificKey_, dataDestructor) == 0);
CHECK_EQ(pthread_key_create(&threadSpecificKey_, dataDestructor), 0);
}
~ThreadLocal() { pthread_key_delete(threadSpecificKey_); }
......@@ -65,7 +65,7 @@ public:
if (!p && createLocal) {
p = new T();
int ret = pthread_setspecific(threadSpecificKey_, p);
CHECK(ret == 0);
CHECK_EQ(ret, 0);
}
return p;
}
......@@ -79,7 +79,7 @@ public:
if (T* q = get(false)) {
dataDestructor(q);
}
CHECK(pthread_setspecific(threadSpecificKey_, p) == 0);
CHECK_EQ(pthread_setspecific(threadSpecificKey_, p), 0);
}
/**
......@@ -112,7 +112,7 @@ private:
template <class T>
class ThreadLocalD {
public:
ThreadLocalD() { CHECK(pthread_key_create(&threadSpecificKey_, NULL) == 0); }
ThreadLocalD() { CHECK_EQ(pthread_key_create(&threadSpecificKey_, NULL), 0); }
~ThreadLocalD() {
pthread_key_delete(threadSpecificKey_);
for (auto t : threadMap_) {
......@@ -127,7 +127,7 @@ public:
T* p = (T*)pthread_getspecific(threadSpecificKey_);
if (!p) {
p = new T();
CHECK(pthread_setspecific(threadSpecificKey_, p) == 0);
CHECK_EQ(pthread_setspecific(threadSpecificKey_, p), 0);
updateMap(p);
}
return p;
......@@ -141,7 +141,7 @@ public:
if (T* q = (T*)pthread_getspecific(threadSpecificKey_)) {
dataDestructor(q);
}
CHECK(pthread_setspecific(threadSpecificKey_, p) == 0);
CHECK_EQ(pthread_setspecific(threadSpecificKey_, p), 0);
updateMap(p);
}
......
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