提交 c8d0791a 编写于 作者: L liaogang

Add common.h and remove DisableCopy and Typedefs

上级 99e43d1d
图像分类教程
==========
在本教程中,我们将使用CIFAR-10数据集训练一个卷积神经网络,并使用这个神经网络来对图片进行分类。如下图所示,卷积神经网络可以辨识图片中的主体,并给出分类结果。
<center>![Image Classification](./image_classification.png)</center>
## 数据准备
首先下载CIFAR-10数据集。下面是CIFAR-10数据集的官方网址:
<https://www.cs.toronto.edu/~kriz/cifar.html>
我们准备了一个脚本,可以用于从官方网站上下载CIFAR-10数据集,转为jpeg文件并存入特定的目录。使用这个脚本前请确认已经安装了pillow及相关依赖模块。可以参照下面的命令进行安装:
1. 安装pillow
```bash
sudo apt-get install libjpeg-dev
pip install pillow
```
2. 下载数据集
```bash
cd demo/image_classification/data/
sh download_cifar.sh
```
CIFAR-10数据集包含60000张32x32的彩色图片。图片分为10类,每个类包含6000张。其中50000张图片作为训练集,10000张作为测试集。
下图展示了所有的图片类别,每个类别中随机抽取了10张图片。
<center>![Image Classification](./cifar.png)</center>
脚本运行完成后,我们应当会得到一个名为cifar-out的文件夹,其下子文件夹的结构如下
```
train
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
test
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
```
cifar-out下包含`train``test`两个文件夹,其中分别包含了CIFAR-10中的训练集和测试集。这两个文件夹下各自有10个子文件夹,每个子文件夹下存储相应分类的图片。将图片按照上述结构存储好之后,我们就可以着手对分类模型进行训练了。
## 预处理
数据下载之后,还需要进行预处理,将数据转换为Paddle的格式。我们可以通过如下命令进行预处理工作:
```
cd demo/image_classification/
sh preprocess.sh
```
其中`preprocess.sh` 调用 `./demo/image_classification/preprocess.py` 对图片进行预处理
```sh
export PYTHONPATH=$PYTHONPATH:../../
data_dir=./data/cifar-out
python preprocess.py -i $data_dir -s 32 -c 1
```
`./demo/image_classification/preprocess.py` 使用如下参数:
- `-i``--input` 给出输入数据所在路径;
- `-s``--size` 给出图片尺寸;
- `-c``--color` 标示图片是彩色图或灰度图
## 模型训练
在开始训练之前,我们需要先创建一个模型配置文件。下面我们给出了一个配置示例。**注意**,这里的列出的和`vgg_16_cifar.py`文件稍有差别,因为该文件可适用于预测。
```python
from paddle.trainer_config_helpers import *
data_dir='data/cifar-out/batches/'
meta_path=data_dir+'batches.meta'
args = {'meta':meta_path, 'mean_img_size': 32,
'img_size': 32, 'num_classes': 10,
'use_jpeg': 1, 'color': "color"}
define_py_data_sources2(train_list=data_dir+"train.list",
test_list=data_dir+'test.list',
module='image_provider',
obj='processData',
args=args)
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128))
img = data_layer(name='image', size=3*32*32)
lbl = data_layer(name="label", size=10)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
outputs(classification_cost(input=predict, label=lbl))
```
在第一行中我们载入用于定义网络的函数。
```python
from paddle.trainer_config_helpers import *
```
之后定义的`define_py_data_sources2`使用Python数据提供器,其中 `args`将在`image_provider.py`进行使用,该文件负责产生图片数据并传递给Paddle系统
- `meta`: 训练集平均值。
- `mean_img_size`: 平均特征图的高度及宽度。
- `img_size`:输入图片的高度及宽度。
- `num_classes`:类别个数。
- `use_jpeg`:处理过程中数据存储格式。
- `color`:标示是否为彩色图片。
`settings`用于设置训练算法。在下面的例子中,learning rate被设置为0.1除以batch size,而weight decay则为0.0005乘以batch size。
```python
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
```
`small_vgg`定义了网络结构。这里我们使用的是一个小的VGG网络。关于VGG卷积神经网络的描述可以参考:[http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/)
```python
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
```
配置创建完毕后,可以运行脚本train.sh来训练模型。
```bash
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--save_dir=$output \
2>&1 | tee $log
python -m paddle.utils.plotcurve -i $log > plot.png
```
- 这里我们使用的是GPU模式进行训练。如果你没有GPU环境,可以设置`use_gpu=0`
- `./demo/image_classification/vgg_16_cifar.py`是网络和数据配置文件。各项参数的详细说明可以在命令行参数相关文档中找到。
- 脚本`plotcurve.py`依赖于python的`matplotlib`模块。因此如果这个脚本运行失败,也许是因为需要安装`matplotlib`
在训练完成后,训练及测试误差曲线图会被`plotcurve.py`脚本保存在 `plot.png`中。下面是一个误差曲线图的示例:
<center>![Training and testing curves.](./plot.png)</center>
## 预测
在训练完成后,模型及参数会被保存在路径`./cifar_vgg_model/pass-%05d`下。例如第300个pass的模型会被保存在`./cifar_vgg_model/pass-00299`
要对一个图片的进行分类预测,我们可以使用`predict.sh`,该脚本将输出预测分类的标签:
```
sh predict.sh
```
predict.sh:
```
model=cifar_vgg_model/pass-00299/
image=data/cifar-out/test/airplane/seaplane_s_000978.png
use_gpu=1
python prediction.py $model $image $use_gpu
```
## 练习
在CUB-200数据集上使用VGG模型训练一个鸟类图片分类模型。相关的鸟类数据集可以从如下地址下载,其中包含了200种鸟类的照片(主要来自北美洲)。
<http://www.vision.caltech.edu/visipedia/CUB-200.html>
## 细节探究
### 卷积神经网络
卷积神经网络是一种使用卷积层的前向神经网络,很适合构建用于理解图片内容的模型。一个典型的神经网络如下图所示:
![Convolutional Neural Network](./lenet.png)
一个卷积神经网络包含如下层:
- 卷积层:通过卷积操作从图片或特征图中提取特征
- 池化层:使用max-pooling对特征图下采样
- 全连接层:使输入层到隐藏层的神经元是全部连接的。
卷积神经网络在图片分类上有着惊人的性能,这是因为它发掘出了图片的两类重要信息:局部关联性质和空间不变性质。通过交替使用卷积和池化处理, 卷积神经网络能够很好的表示这两类信息。
关于如何定义网络中的层,以及如何在层之间进行连接,请参考Layer文档。
...@@ -147,7 +147,7 @@ for classification. A description of VGG network can be found here [http://www.r ...@@ -147,7 +147,7 @@ for classification. A description of VGG network can be found here [http://www.r
# small_vgg is predined in trainer_config_helpers.network # small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3) predict = small_vgg(input_image=img, num_channels=3)
``` ```
After writing the config, we can train the model by running the script train.sh. Notice that the following script assumes the you run the script in the `./demo/image_classification` folder. If you run the script in a different folder, you need to change the paths of the scripts and the configuration files accordingly. After writing the config, we can train the model by running the script train.sh.
```bash ```bash
config=vgg_16_cifar.py config=vgg_16_cifar.py
......
...@@ -20,15 +20,11 @@ limitations under the License. */ ...@@ -20,15 +20,11 @@ limitations under the License. */
#include <string> #include <string>
#include <vector> #include <vector>
#include "paddle/utils/GlobalConstants.h" #include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
/// Import PaddlePaddle's enumeration into global namespace. /// Import PaddlePaddle's enumeration into global namespace.
using namespace paddle::enumeration_wrapper; // NOLINT using namespace paddle::enumeration_wrapper; // NOLINT
#define DISABLE_COPY_AND_ASSIGN(classname) \
classname(const classname& other); \
classname& operator=(const classname& other)
/** /**
* @brief Initialize paddle. * @brief Initialize paddle.
* *
...@@ -102,7 +98,7 @@ const size_t NO_SPARSE_ID = -1UL; ...@@ -102,7 +98,7 @@ const size_t NO_SPARSE_ID = -1UL;
struct MatrixPrivate; struct MatrixPrivate;
class Matrix { class Matrix {
Matrix(); // User Cannot Create Matrix. Matrix(); // User Cannot Create Matrix.
DISABLE_COPY_AND_ASSIGN(Matrix); DISABLE_COPY(Matrix);
static Matrix* createByPaddleMatrixPtr(void* sharedPtr); static Matrix* createByPaddleMatrixPtr(void* sharedPtr);
public: public:
...@@ -242,7 +238,7 @@ private: ...@@ -242,7 +238,7 @@ private:
struct VectorPrivate; struct VectorPrivate;
class Vector { class Vector {
DISABLE_COPY_AND_ASSIGN(Vector); DISABLE_COPY(Vector);
Vector(); Vector();
static Vector* createByPaddleVectorPtr(void* ptr); static Vector* createByPaddleVectorPtr(void* ptr);
...@@ -322,7 +318,7 @@ private: ...@@ -322,7 +318,7 @@ private:
struct IVectorPrivate; struct IVectorPrivate;
class IVector { class IVector {
IVector(); IVector();
DISABLE_COPY_AND_ASSIGN(IVector); DISABLE_COPY(IVector);
static IVector* createByPaddleVectorPtr(void* ptr); static IVector* createByPaddleVectorPtr(void* ptr);
public: public:
...@@ -402,7 +398,7 @@ struct ArgumentsPrivate; ...@@ -402,7 +398,7 @@ struct ArgumentsPrivate;
class Arguments { class Arguments {
private: private:
Arguments(); // Internal Create. Arguments(); // Internal Create.
DISABLE_COPY_AND_ASSIGN(Arguments); DISABLE_COPY(Arguments);
public: public:
/** /**
...@@ -472,7 +468,7 @@ enum GradientMatchineCreateMode { ...@@ -472,7 +468,7 @@ enum GradientMatchineCreateMode {
struct ParameterConfigPrivate; struct ParameterConfigPrivate;
class ParameterConfig { class ParameterConfig {
DISABLE_COPY_AND_ASSIGN(ParameterConfig); DISABLE_COPY(ParameterConfig);
ParameterConfig(); ParameterConfig();
/** /**
...@@ -502,7 +498,7 @@ private: ...@@ -502,7 +498,7 @@ private:
struct OptimizationConfigPrivate; struct OptimizationConfigPrivate;
class OptimizationConfig { class OptimizationConfig {
DISABLE_COPY_AND_ASSIGN(OptimizationConfig); DISABLE_COPY(OptimizationConfig);
OptimizationConfig(); OptimizationConfig();
public: public:
...@@ -526,7 +522,7 @@ struct ParameterPrivate; ...@@ -526,7 +522,7 @@ struct ParameterPrivate;
class Parameter { class Parameter {
private: private:
Parameter(); Parameter();
DISABLE_COPY_AND_ASSIGN(Parameter); DISABLE_COPY(Parameter);
public: public:
virtual ~Parameter(); virtual ~Parameter();
...@@ -568,7 +564,7 @@ struct ModelConfigPrivate; ...@@ -568,7 +564,7 @@ struct ModelConfigPrivate;
class ModelConfig { class ModelConfig {
private: private:
ModelConfig(); ModelConfig();
DISABLE_COPY_AND_ASSIGN(ModelConfig); DISABLE_COPY(ModelConfig);
public: public:
virtual ~ModelConfig(); virtual ~ModelConfig();
...@@ -589,7 +585,7 @@ struct TrainerConfigPrivate; ...@@ -589,7 +585,7 @@ struct TrainerConfigPrivate;
class TrainerConfig { class TrainerConfig {
private: private:
TrainerConfig(); TrainerConfig();
DISABLE_COPY_AND_ASSIGN(TrainerConfig); DISABLE_COPY(TrainerConfig);
public: public:
virtual ~TrainerConfig(); virtual ~TrainerConfig();
...@@ -629,7 +625,7 @@ public: ...@@ -629,7 +625,7 @@ public:
struct ParameterTraverseCallbackPrivate; struct ParameterTraverseCallbackPrivate;
class ParameterTraverseCallback { class ParameterTraverseCallback {
DISABLE_COPY_AND_ASSIGN(ParameterTraverseCallback); DISABLE_COPY(ParameterTraverseCallback);
ParameterTraverseCallback(); ParameterTraverseCallback();
public: public:
...@@ -651,7 +647,7 @@ private: ...@@ -651,7 +647,7 @@ private:
*/ */
struct ParameterOptimizerPrivate; struct ParameterOptimizerPrivate;
class ParameterOptimizer { class ParameterOptimizer {
DISABLE_COPY_AND_ASSIGN(ParameterOptimizer); DISABLE_COPY(ParameterOptimizer);
ParameterOptimizer(); ParameterOptimizer();
public: public:
...@@ -688,7 +684,7 @@ struct GradientMachinePrivate; ...@@ -688,7 +684,7 @@ struct GradientMachinePrivate;
class GradientMachine { class GradientMachine {
private: private:
GradientMachine(); GradientMachine();
DISABLE_COPY_AND_ASSIGN(GradientMachine); DISABLE_COPY(GradientMachine);
public: public:
virtual ~GradientMachine(); virtual ~GradientMachine();
...@@ -780,7 +776,7 @@ private: ...@@ -780,7 +776,7 @@ private:
TrainerPrivate* m; TrainerPrivate* m;
Trainer(); Trainer();
Trainer(TrainerConfig* optConfig, GradientMachine* gm); Trainer(TrainerConfig* optConfig, GradientMachine* gm);
DISABLE_COPY_AND_ASSIGN(Trainer); DISABLE_COPY(Trainer);
public: public:
virtual ~Trainer(); virtual ~Trainer();
...@@ -846,7 +842,7 @@ public: ...@@ -846,7 +842,7 @@ public:
struct SequenceGeneratorPrivate; struct SequenceGeneratorPrivate;
class SequenceGenerator { class SequenceGenerator {
DISABLE_COPY_AND_ASSIGN(SequenceGenerator); DISABLE_COPY(SequenceGenerator);
SequenceGenerator(); SequenceGenerator();
public: public:
......
...@@ -16,7 +16,31 @@ limitations under the License. */ ...@@ -16,7 +16,31 @@ limitations under the License. */
#define HL_BASE_H_ #define HL_BASE_H_
#include <cstddef> #include <cstddef>
#include "paddle/utils/TypeDefs.h"
#ifdef PADDLE_TYPE_DOUBLE
#define HL_FLOAT_MAX 3.40282347e+38F
#define HL_FLOAT_MIN 1.17549435e-38F
using real = double;
#else
#define HL_FLOAT_MAX 1.7976931348623157e+308
#define HL_FLOAT_MIN 2.2250738585072014e-308
using real = float;
#endif
/**
* The maximum input value for exp, used to avoid overflow problem.
* currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
/**
* @brief DIVUP(x, y) is similar to ceil(x / y).
* @note For CUDA, DIVUP will be used to specify
* the size of blockDim.
*/
#ifndef DIVUP
#define DIVUP(x, y) (((x) + (y)-1) / (y))
#endif
/** /**
* HPPL is an internal high performance parallel computing library * HPPL is an internal high performance parallel computing library
...@@ -181,46 +205,6 @@ typedef struct { ...@@ -181,46 +205,6 @@ typedef struct {
size_t nnz; size_t nnz;
} _hl_sparse_matrix_s, *hl_sparse_matrix_s; } _hl_sparse_matrix_s, *hl_sparse_matrix_s;
#ifndef PADDLE_TYPE_DOUBLE
/**
* HPPL data type: real (float or double)
*
* if real == float
*
* HL_FLOAT_MAX: 3.40282347e+38F
*
* HL_FLOAT_MIN: 1.17549435e-38F
*/
#define HL_FLOAT_MAX 3.40282347e+38F
/**
* if real == double
*
* HL_FLOAT_MAX: 1.7976931348623157e+308
*
* HL_FLOAT_MIN: 2.2250738585072014e-308
*/
#define HL_FLOAT_MIN 1.17549435e-38F
#else
#define HL_FLOAT_MAX 1.7976931348623157e+308
#define HL_FLOAT_MIN 2.2250738585072014e-308
#endif
/**
* The maximum input value for exp, used to avoid overflow problem.
*
* Currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
/**
* @brief DIVUP(x, y) is similar to ceil(x / y).
* @note For CUDA, DIVUP will be used to specify
* the size of blockDim.
*/
#ifndef DIVUP
#define DIVUP(x, y) (((x) + (y)-1) / (y))
#endif
#ifdef __NVCC__ #ifdef __NVCC__
#include "cuda_runtime.h" #include "cuda_runtime.h"
......
...@@ -34,8 +34,8 @@ limitations under the License. */ ...@@ -34,8 +34,8 @@ limitations under the License. */
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/Queue.h" #include "paddle/utils/Queue.h"
#include "paddle/utils/ThreadLocal.h" #include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/utils/common.h"
namespace paddle { namespace paddle {
/** /**
......
...@@ -58,6 +58,8 @@ protected: ...@@ -58,6 +58,8 @@ protected:
/// to batch, channels* imagePixels. /// to batch, channels* imagePixels.
void shrinkMat(const MatrixPtr& in, MatrixPtr& out); void shrinkMat(const MatrixPtr& in, MatrixPtr& out);
void onPassEnd() { firstTest_ = true; }
MatrixPtr tmpMat_, tmpGrad_; MatrixPtr tmpMat_, tmpGrad_;
MatrixPtr expandedIn_, expandedOut_; MatrixPtr expandedIn_, expandedOut_;
MatrixPtr expandedInGrad_, expandedOutGrad_, inGrad_; MatrixPtr expandedInGrad_, expandedOutGrad_, inGrad_;
......
...@@ -16,7 +16,7 @@ limitations under the License. */ ...@@ -16,7 +16,7 @@ limitations under the License. */
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "hl_gpu.h" #include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -16,7 +16,7 @@ limitations under the License. */ ...@@ -16,7 +16,7 @@ limitations under the License. */
#include "ModelConfig.pb.h" #include "ModelConfig.pb.h"
#include "hl_gpu.h" #include "hl_gpu.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -16,7 +16,7 @@ limitations under the License. */ ...@@ -16,7 +16,7 @@ limitations under the License. */
#include <memory> #include <memory>
#include <random> #include <random>
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -16,7 +16,7 @@ limitations under the License. */ ...@@ -16,7 +16,7 @@ limitations under the License. */
#include <stdint.h> #include <stdint.h>
#include <cstddef> #include <cstddef>
#include "TensorExpression.h" #include "TensorExpression.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -27,7 +27,7 @@ limitations under the License. */ ...@@ -27,7 +27,7 @@ limitations under the License. */
#include "MemoryHandle.h" #include "MemoryHandle.h"
#include "Vector.h" #include "Vector.h"
#include "paddle/utils/ThreadLocal.h" #include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -17,7 +17,7 @@ limitations under the License. */ ...@@ -17,7 +17,7 @@ limitations under the License. */
#include <cstddef> #include <cstddef>
#include "hl_tensor_ops.h" #include "hl_tensor_ops.h"
#include "paddle/utils/Logging.h" #include "paddle/utils/Logging.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -22,7 +22,7 @@ limitations under the License. */ ...@@ -22,7 +22,7 @@ limitations under the License. */
#include "BaseMatrix.h" #include "BaseMatrix.h"
#include "MemoryHandle.h" #include "MemoryHandle.h"
#include "paddle/utils/Thread.h" #include "paddle/utils/Thread.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -28,7 +28,7 @@ limitations under the License. */ ...@@ -28,7 +28,7 @@ limitations under the License. */
#include "paddle/parameter/ParameterUpdateFunctions.h" #include "paddle/parameter/ParameterUpdateFunctions.h"
#include "paddle/utils/Flags.h" #include "paddle/utils/Flags.h"
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
#include "ParameterConfig.pb.h" #include "ParameterConfig.pb.h"
......
...@@ -29,8 +29,8 @@ limitations under the License. */ ...@@ -29,8 +29,8 @@ limitations under the License. */
#include "paddle/utils/GlobalConstants.h" #include "paddle/utils/GlobalConstants.h"
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
#include "paddle/utils/ThreadLocal.h" #include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -15,7 +15,7 @@ limitations under the License. */ ...@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once #pragma once
#include "paddle/math/Vector.h" #include "paddle/math/Vector.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -18,7 +18,7 @@ limitations under the License. */ ...@@ -18,7 +18,7 @@ limitations under the License. */
#include "paddle/math/Matrix.h" #include "paddle/math/Matrix.h"
#include "paddle/pserver/ProtoServer.h" #include "paddle/pserver/ProtoServer.h"
#include "paddle/utils/Queue.h" #include "paddle/utils/Queue.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
namespace paddle { namespace paddle {
......
...@@ -26,8 +26,8 @@ limitations under the License. */ ...@@ -26,8 +26,8 @@ limitations under the License. */
#include "paddle/utils/Flags.h" #include "paddle/utils/Flags.h"
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
#include "paddle/utils/Queue.h" #include "paddle/utils/Queue.h"
#include "paddle/utils/TypeDefs.h"
#include "paddle/utils/Util.h" #include "paddle/utils/Util.h"
#include "paddle/utils/common.h"
#include "ParameterService.pb.h" #include "ParameterService.pb.h"
......
...@@ -32,7 +32,7 @@ limitations under the License. */ ...@@ -32,7 +32,7 @@ limitations under the License. */
#include "paddle/utils/Locks.h" #include "paddle/utils/Locks.h"
#include "paddle/utils/Stat.h" #include "paddle/utils/Stat.h"
#include "paddle/utils/ThreadLocal.h" #include "paddle/utils/ThreadLocal.h"
#include "paddle/utils/TypeDefs.h" #include "paddle/utils/common.h"
#include "ParameterService.pb.h" #include "ParameterService.pb.h"
......
...@@ -11,7 +11,7 @@ limitations under the License. */ ...@@ -11,7 +11,7 @@ limitations under the License. */
#pragma once #pragma once
#include "DisableCopy.h" #include "common.h"
namespace paddle { namespace paddle {
......
/* 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. */
#pragma once
/**
* Disable copy macro.
*/
#define DISABLE_COPY(CLASS_NAME) \
CLASS_NAME(CLASS_NAME &&) = delete; \
CLASS_NAME(const CLASS_NAME &other) = delete; \
CLASS_NAME &operator=(const CLASS_NAME &other) = delete
...@@ -19,7 +19,7 @@ limitations under the License. */ ...@@ -19,7 +19,7 @@ limitations under the License. */
#include <condition_variable> #include <condition_variable>
#include <mutex> #include <mutex>
#include "DisableCopy.h" #include "common.h"
namespace paddle { namespace paddle {
......
...@@ -26,12 +26,11 @@ limitations under the License. */ ...@@ -26,12 +26,11 @@ limitations under the License. */
#include <unordered_map> #include <unordered_map>
#include <vector> #include <vector>
#include "DisableCopy.h"
#include "Logging.h" #include "Logging.h"
#include "TrainerConfig.pb.h" #include "TrainerConfig.pb.h"
#include "common.h"
#include "Flags.h" #include "Flags.h"
#include "TypeDefs.h"
#include "hl_gpu.h" #include "hl_gpu.h"
/** /**
......
...@@ -15,7 +15,7 @@ limitations under the License. */ ...@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once #pragma once
#include <stddef.h> #include <stddef.h>
#include <iostream> #include <iostream>
#include "TypeDefs.h" #include "common.h"
namespace paddle { namespace paddle {
......
...@@ -15,12 +15,19 @@ limitations under the License. */ ...@@ -15,12 +15,19 @@ limitations under the License. */
#pragma once #pragma once
namespace paddle { namespace paddle {
/**
* Disable copy macro.
*/
#define DISABLE_COPY(class_name) \
class_name(class_name &&) = delete; \
class_name(const class_name &other) = delete; \
class_name &operator=(const class_name &other) = delete
#ifdef PADDLE_TYPE_DOUBLE #ifdef PADDLE_TYPE_DOUBLE
typedef double real; using real = double;
#else #else
typedef float real; using real = float;
#endif #endif
} // namespace paddle } // namespace paddle
using paddle::real;
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