Merge pull request #161 from tink2123/fix_doc_typo

fix doc typo for release/1.0.0

doc/fluid/advanced_usage/deploy/anakin_tutorial.md

@@ -114,67 +114,64 @@ Anakin中数据类型与基本数据类型的对应如下:
 
   理论上，Anakin支持申明1维以上的tensor，但是对于Anakin中的Op来说，只支持NW、NHW、NCHW、NCHW_C4这四种LayOut，其中NCHW是默认的LayOuteType，NCHW_C4是专门针对于int8这种数据类型的。
 
-  例子
+  **例子：**
 
-    下面的代码将展示如何使用tensor， 我们建议先看看这些示例。
+下面的代码将展示如何使用tensor， 我们建议先看看这些示例。
 
-    要想获得更多关于tensor的信息， 请参考 *soure_path/core/tensor.h*
+要想获得更多关于tensor的信息， 请参考 *soure_path/core/tensor.h*
 
-    > 1. 使用shape对象初始化tensor
+1. 使用shape对象初始化tensor
 
-    ```c++
-      //create a null tensor. A null tensor holds for nothing.
-      //tensor's buffer  is resident at CPU and its datatype is AK_FLOAT.
-      //tensor's Layout is NCHW(default)
-      Tensor<X86, AK_FLOAT> mytensor;
+    ```cpp
+    //create a null tensor. A null tensor holds for nothing.
+    //tensor's buffer  is resident at CPU and its datatype is AK_FLOAT.
+    //tensor's Layout is NCHW(default)
+    Tensor<X86, AK_FLOAT> mytensor;
 
-      //1. using shape object to create a tensor.
-      Shape shape1(NUM); //1-D shape. NUM is the number of dimention.
-      Tensor<X86, AK_FLOAT, W> mytensor1(shape1); //1-D tensor.
+    //1. using shape object to create a tensor.
+    Shape shape1(NUM); //1-D shape. NUM is the number of dimention.
+    Tensor<X86, AK_FLOAT, W> mytensor1(shape1); //1-D tensor.
 
-      // A 4-D shape
-      Shape shape2(N, C, H, W); // batch x channel x height x width
+    // A 4-D shape
+    Shape shape2(N, C, H, W); // batch x channel x height x width
     ```
 
-    >`注意：Shape的维度必须和tensor的`[LayoutType](#layout)`相同，比如Shape(N,C,H,W), 那么Tensor的 LayoutType必须是NCHW，否则会出错。如下列代码所示`
+    `注意：Shape的维度必须和tensor的`[LayoutType](#layout)`相同，比如Shape(N,C,H,W), 那么Tensor的 LayoutType必须是NCHW，否则会出错。如下列代码所示`
 
     ```c++
-       // A 4-D tensor.
-       Tensor<X86, AK_FLOAT> mytensor2(shape2);  //right
-
-       //A 4-D tensor which is resident at GPU and its datatype is AK_INT8
-       Tensor<NV, AK_INT8> mytensor3(shape2);   //right
+    // A 4-D tensor.
+    Tensor<X86, AK_FLOAT> mytensor2(shape2);  //right
 
-       Tensor<X86, AK_FLOAT, NHW> mytensor4(shape2); //wrong!! shape's dimetion must be equal to tensor's Layout.
-       Tensor<NV, AK_FLOAT, NCHW_C4> mytensor5(shape2); //wrong!!!!
+    //A 4-D tensor which is resident at GPU and its datatype is AK_INT8
+    Tensor<NV, AK_INT8> mytensor3(shape2);   //right
 
+    Tensor<X86, AK_FLOAT, NHW> mytensor4(shape2); //wrong!! shape's dimetion must be equal to tensor's Layout.
+    Tensor<NV, AK_FLOAT, NCHW_C4> mytensor5(shape2); //wrong!!!!
     ```
 
-    > 2. 使用现有的数据和shape初始化tensor
+2. 使用现有的数据和shape初始化tensor
 
     ```c++
+    /**
+    *  A construtor of Tensor.
+    *  data_ptr is a pointer to any data type of data
+    *  TargetType is type of a platform [Anakin TargetType]
+    *  id : device id
+    *  shape: a Anakin shape
+    */
+    Tensor(Dtype* data_ptr, TargetType_t target, int id, Shape shape);
 
-       /**
-       *  A construtor of Tensor.
-       *  data_ptr is a pointer to any data type of data
-       *  TargetType is type of a platform [Anakin TargetType]
-       *  id : device id
-       *  shape: a Anakin shape
-       */
-       Tensor(Dtype* data_ptr, TargetType_t target, int id, Shape shape);
-
-       //using existing data feed to a tensor
-       Tensor<X86, AK_FLOAT> mytensor(data_ptr, TargetType, device_id, shape); //shape must has dimention (N, C, H, W).
-
+    //using existing data feed to a tensor
+    Tensor<X86, AK_FLOAT> mytensor(data_ptr, TargetType, device_id, shape); //shape must has dimention (N, C, H, W).
     ```
 
-    > 3. 使用tensor初始化tensor
+3. 使用tensor初始化tensor
 
     ```c++
-       Tensor<NV, AK_FLOAT> tensor(exist_tensor);
+    Tensor<NV, AK_FLOAT> tensor(exist_tensor);
     ```
 
-    > 提示： 你可以用` typedef Tensor<X86, AK_FLOAT> Tensor4d_X86 `方便定义tensor
+>提示： 你可以用` typedef Tensor<X86, AK_FLOAT> Tensor4d_X86 `方便定义tensor
 
 #### 填充tensor数据区
 

doc/fluid/advanced_usage/development/gpu_profiling_cn.rst

@@ -63,10 +63,7 @@ above profilers.
 上述的代码片段包含了两种方法，您可以任意使用一个或两个来对感兴趣的代码段做性能分析。
 
 1. :code:`REGISTER_TIMER_INFO` 是一个内置的定时器封装，可以用来计算CPU函数或cuda内核的时间消耗。
-
-2. :code:`REGISTER_GPU_PROFILER` is a general purpose wrapper object of :code:`cudaProfilerStart` and :code:`cudaProfilerStop` to avoid
-program crashes when CPU version of PaddlePaddle invokes them.
-
+2. :code:`REGISTER_GPU_PROFILER` 是 :code:`cudaProfilerStart` 和 :code:`cudaProfilerStop` 的通用包装对象，避免当CPU版本的PaddlePaddle调用它们时程序崩溃。
 3. :code:`REGISTER_GPU_PROFILER` 是一个封装对象，封装了 :code:`cudaProfilerStart` 和 :code:`cudaProfileStop` 两个操作；同时其内部实现可以避免纯CPU版本PaddlePaddle在执行本语句时发生崩溃。
 
 您会在接下来的部分中获得更多的细节介绍。

doc/fluid/user_guides/howto/inference/native_infer.md

@@ -75,26 +75,26 @@ CHECK(predictor->Run(slots, &outputs));
 `PaddleBuf` 在内存管理方面有两种模式：
 
 1. 自动分配和管理内存
-
-```c++
-int some_size = 1024;
-PaddleTensor tensor;
-tensor.data.Resize(some_size);
-```
+    
+    ```c++
+    int some_size = 1024;
+    PaddleTensor tensor;
+    tensor.data.Resize(some_size);
+    ```
 
 2. 外部内存传入
-
-```c++
-int some_size = 1024;
-// 用户外部分配内存并保证 PaddleTensor 使用过程中，内存一直可用
-void* memory = new char[some_size]; 
-
-tensor.data.Reset(memory, some_size);
-// ...
-
-// 用户最后需要自行删除内存以避免内存泄漏
-delete[] memory;
-```
+    ```c++
+    int some_size = 1024;
+    // 用户外部分配内存并保证 PaddleTensor 使用过程中，内存一直可用
+    void* memory = new char[some_size]; 
+    
+    tensor.data.Reset(memory, some_size);
+    // ...
+    
+    // 用户最后需要自行删除内存以避免内存泄漏
+    
+    delete[] memory;
+    ```
 
 两种模式中，第一种比较方便；第二种则可以严格控制内存的管理，便于与 `tcmalloc` 等库的集成。
 

doc/fluid/user_guides/howto/inference/paddle_tensorrt_infer.md

@@ -113,12 +113,12 @@ int main() {
 
 **原始网络**
 <p align="center">
- <img src="https://raw.githubusercontent.com/NHZlX/FluidDoc/add_trt_doc/doc/fluid/user_guides/howto/inference/image/model_graph_original.png" width=800>
+ <img src="https://raw.githubusercontent.com/NHZlX/FluidDoc/add_trt_doc/doc/fluid/user_guides/howto/inference/image/model_graph_original.png" width="600">
 </p>
 
 **转换的网络**
 <p align="center">
- <img src="https://raw.githubusercontent.com/NHZlX/FluidDoc/add_trt_doc/doc/fluid/user_guides/howto/inference/image/model_graph_trt.png" width=800>
+ <img src="https://raw.githubusercontent.com/NHZlX/FluidDoc/add_trt_doc/doc/fluid/user_guides/howto/inference/image/model_graph_trt.png" width="600">
 </p>