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Merge branch 'develop' into 'master' 

Develop

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3rdParty/InstallOpenCVDependences.sh deleted 100644 → 0
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#! /bin/sh
############### Ubuntu ###############
# 参考：https://docs.opencv.org/3.4.11/d7/d9f/tutorial_linux_install.html
# apt-get install build-essential -y
# apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev -y
# apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev -y # 处理图像所需的包，可选
############### CentOS ###############
yum install gcc gcc-c++ gtk2-devel gimp-devel gimp-devel-tools gimp-help-browser zlib-devel libtiff-devel libjpeg-devel libpng-devel gstreamer-devel libavc1394-devel libraw1394-devel libdc1394-devel jasper-devel jasper-utils swig python libtool nasm -y
\ No newline at end of file
CMakeLists.txt
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...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
cmake_minimum_required(VERSION 3.5) cmake_minimum_required(VERSION 3.5)
# 设置项目名 # 设置项目名
project(MIGraphX_Samples) project(GPT2)
# 设置编译器 # 设置编译器
set(CMAKE_CXX_COMPILER g++) set(CMAKE_CXX_COMPILER g++)
...@@ -12,7 +12,6 @@ set(CMAKE_BUILD_TYPE release) ...@@ -12,7 +12,6 @@ set(CMAKE_BUILD_TYPE release)
# 添加头文件路径 # 添加头文件路径
set(INCLUDE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/Src/ set(INCLUDE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/Src/
${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/ ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/
${CMAKE_CURRENT_SOURCE_DIR}/Src/NLP/GPT2/
$ENV{DTKROOT}/include/ $ENV{DTKROOT}/include/
${CMAKE_CURRENT_SOURCE_DIR}/depend/include/) ${CMAKE_CURRENT_SOURCE_DIR}/depend/include/)
include_directories(${INCLUDE_PATH}) include_directories(${INCLUDE_PATH})
...@@ -23,11 +22,7 @@ set(LIBRARY_PATH ${CMAKE_CURRENT_SOURCE_DIR}/depend/lib64/ ...@@ -23,11 +22,7 @@ set(LIBRARY_PATH ${CMAKE_CURRENT_SOURCE_DIR}/depend/lib64/
link_directories(${LIBRARY_PATH}) link_directories(${LIBRARY_PATH})
# 添加依赖库 # 添加依赖库
set(LIBRARY opencv_core set(LIBRARY migraphx_ref
opencv_imgproc
opencv_imgcodecs
opencv_dnn
migraphx_ref
migraphx migraphx
migraphx_c migraphx_c
migraphx_device migraphx_device
...@@ -37,12 +32,10 @@ link_libraries(${LIBRARY}) ...@@ -37,12 +32,10 @@ link_libraries(${LIBRARY})
# 添加源文件 # 添加源文件
set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/Src/main.cpp set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/Src/main.cpp
${CMAKE_CURRENT_SOURCE_DIR}/Src/Sample.cpp ${CMAKE_CURRENT_SOURCE_DIR}/Src/GPT2.cpp
${CMAKE_CURRENT_SOURCE_DIR}/Src/NLP/GPT2/tokenization.cpp ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/tokenization.cpp
${CMAKE_CURRENT_SOURCE_DIR}/Src/NLP/GPT2/utf8proc.c ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/utf8proc.c
${CMAKE_CURRENT_SOURCE_DIR}/Src/NLP/GPT2/GPT2.cpp
${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/CommonUtility.cpp
${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/Filesystem.cpp) ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/Filesystem.cpp)
# 添加可执行目标 # 添加可执行目标
add_executable(MIGraphX_Samples ${SOURCE_FILES}) add_executable(GPT2 ${SOURCE_FILES})
Doc/Images/GPT_04.png
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Doc/Tutorial_Cpp/GPT2.md → Doc/Tutorial_Cpp.md
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...@@ -12,9 +12,9 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠transforme ...@@ -12,9 +12,9 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠transforme
| GPT-2 | 2019 年 2 月 | 15 亿 | 40GB | | GPT-2 | 2019 年 2 月 | 15 亿 | 40GB |
| GPT-3 | 2020 年 5 月 | 1,750 亿 | 45TB | | GPT-3 | 2020 年 5 月 | 1,750 亿 | 45TB |
本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw, 提取码：4pmh。将GPT2_shici.onnx模型文件保存在Resource/Models/NLP/GPT2文件夹下。整体模型结构如下图所示，也可以通过netron工具：https://netron.app/ 查看GPT-2的模型结构。 本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw, 提取码：4pmh。将GPT2_shici.onnx模型文件保存在Resource/文件夹下。整体模型结构如下图所示，也可以通过netron工具：https://netron.app/ 查看GPT-2的模型结构。
<img src="../Images/GPT_01.png" style="zoom:100%;" align=middle> <img src="./Images/GPT_01.png" style="zoom:100%;" align=middle>
## 预处理 ## 预处理
...@@ -27,11 +27,11 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠transforme ...@@ -27,11 +27,11 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠transforme
首先，根据提供的词汇表路径，通过cuBERT::FullTokenizer()函数加载词汇表，用于后续对输入文本的编码操作。其次，将词汇表中的内容依次保存到vector容器output中，用于数据后处理中的解码操作。 首先，根据提供的词汇表路径，通过cuBERT::FullTokenizer()函数加载词汇表，用于后续对输入文本的编码操作。其次，将词汇表中的内容依次保存到vector容器output中，用于数据后处理中的解码操作。
```c++ ```c++
cuBERT::FullTokenizer tokenizer = cuBERT::FullTokenizer("../Resource/Models/NLP/GPT2/vocab_shici.txt"); cuBERT::FullTokenizer tokenizer = cuBERT::FullTokenizer("../Resource/vocab_shici.txt");
std::ifstream infile; std::ifstream infile;
std::string buf; std::string buf;
std::vector<std::string> output; std::vector<std::string> output;
infile.open("../Resource/Models/NLP/GPT2/vocab_shici.txt"); infile.open("../Resource/vocab_shici.txt");
while (std::getline(infile,buf)) while (std::getline(infile,buf))
{ {
output.push_back(buf); output.push_back(buf);
...@@ -46,7 +46,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer, ...@@ -46,7 +46,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer,
std::vector<long unsigned int> &input_id) std::vector<long unsigned int> &input_id)
{ {
// 对问题进行分词操作 // 对问题进行分词操作
int max_seq_length =1024; int max_seq_length =1000;
std::vector<std::string> tokens_question; std::vector<std::string> tokens_question;
tokens_question.reserve(max_seq_length); tokens_question.reserve(max_seq_length);
tokenizer.tokenize(question, &tokens_question, max_seq_length); tokenizer.tokenize(question, &tokens_question, max_seq_length);
...@@ -65,7 +65,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer, ...@@ -65,7 +65,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer,
对于GPT-2这种生成式语言模型来说，模型不是仅执行一次推理就结束，而是需要执行多次推理，才能得到最终的答案。如下图所示，GPT-2模型每次推理仅生成一个词，通过将生成的词与输入数据拼接，输入到模型中继续下一次的推理，直到循环结果或者生成[SEP]结束标识符才结束推理。 对于GPT-2这种生成式语言模型来说，模型不是仅执行一次推理就结束，而是需要执行多次推理，才能得到最终的答案。如下图所示，GPT-2模型每次推理仅生成一个词，通过将生成的词与输入数据拼接，输入到模型中继续下一次的推理，直到循环结果或者生成[SEP]结束标识符才结束推理。
<img src="../Images/GPT_02.png" style="zoom:70%;" align=middle> <img src="./Images/GPT_02.png" style="zoom:70%;" align=middle>
具体GPT-2模型的推理，如下代码所示。首先，通过gpt2.Inference()函数实现模型的具体推理细节，推理结果保存在outputs中。其次，对每次推理结果进行判断，当判断为[SEP]结束标志符时，结束循环完成推理，否则就将推理结果outputs加入到输入数据input_id中，继续下一次的模型推理。 具体GPT-2模型的推理，如下代码所示。首先，通过gpt2.Inference()函数实现模型的具体推理细节，推理结果保存在outputs中。其次，对每次推理结果进行判断，当判断为[SEP]结束标志符时，结束循环完成推理，否则就将推理结果outputs加入到输入数据input_id中，继续下一次的模型推理。
...@@ -97,12 +97,12 @@ long unsigned int GPT2::Inference(const std::vector<long unsigned int> &input_id ...@@ -97,12 +97,12 @@ long unsigned int GPT2::Inference(const std::vector<long unsigned int> &input_id
input[0][j] = input_id[j]; input[0][j] = input_id[j];
} }
// 设置输入shape // 输入shape
std::vector<std::vector<std::size_t>> inputShapes; std::vector<std::vector<std::size_t>> inputShapes;
inputShapes.push_back({1,input_id.size()}); inputShapes.push_back({1,input_id.size()});
// 输入数据 // 输入数据
migraphx::parameter_map inputData; std::unordered_map<std::string, migraphx::argument> inputData;
inputData[inputName]=migraphx::argument{migraphx::shape(inputShape.type(),inputShapes[0]),(long unsigned int*)input}; inputData[inputName]=migraphx::argument{migraphx::shape(inputShape.type(),inputShapes[0]),(long unsigned int*)input};
// 推理 // 推理
......
Doc/Tutorial_Python/GPT2.md → Doc/Tutorial_Python.md
View file @ b2735663
...@@ -12,9 +12,9 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠的transfo ...@@ -12,9 +12,9 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠的transfo
| GPT-2 | 2019 年 2 月 | 15 亿 | 40GB | | GPT-2 | 2019 年 2 月 | 15 亿 | 40GB |
| GPT-3 | 2020 年 5 月 | 1,750 亿 | 45TB | | GPT-3 | 2020 年 5 月 | 1,750 亿 | 45TB |
本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw , 提取码：4pmh 。将GPT2_shici.onnx模型文件保存在Resource/Models/NLP/GPT2文件夹下。整体模型结构如下图所示，也可以通过netron工具：https://netron.app/ 查看GPT-2的模型结构。 本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw , 提取码：4pmh 。将GPT2_shici.onnx模型文件保存在Resource/文件夹下。整体模型结构如下图所示，也可以通过netron工具：https://netron.app/ 查看GPT-2的模型结构。
<img src="../Images/GPT_01.png" style="zoom:100%;" align=middle> <img src="./Images/GPT_01.png" style="zoom:100%;" align=middle>
## 预处理 ## 预处理
...@@ -28,7 +28,7 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠的transfo ...@@ -28,7 +28,7 @@ GPT（Generative Pre-trained Transformer）系列模型以不断堆叠的transfo
```python ```python
# 加载词汇表 # 加载词汇表
vocab_file = os.path.join('../../../Resource/Models/NLP/GPT2', 'vocab_shici.txt') vocab_file = os.path.join('../Resource/', 'vocab_shici.txt')
tokenizer = BertTokenizerFast(vocab_file, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]") tokenizer = BertTokenizerFast(vocab_file, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
``` ```
...@@ -63,7 +63,7 @@ maxInput={"input":[1,1000]} ...@@ -63,7 +63,7 @@ maxInput={"input":[1,1000]}
for _ in range(max_len): for _ in range(max_len):
# 推理 # 推理
result = model.run({inputName: migraphx.argument(input_ids)}) result = model.run({inputName: input_ids})
logits = [float(x) for x in result[0].tolist()] logits = [float(x) for x in result[0].tolist()]
... ...
...@@ -83,7 +83,7 @@ for _ in range(max_len): ...@@ -83,7 +83,7 @@ for _ in range(max_len):
2.循环推理，GPT-2模型不像其他模型一样只需要执行一次推理，而是需要循环执行多次推理才能完成。首先，模型推理限定在for循环中，将输入数据input_ids，输入到model.run({...})中执行推理，生成一个token的id。其次，将推理结果拼接到输入数据input_ids中，执行下一次循环。最后，当循环结束或者生成的词为[SEP]结束标志符时，完成GPT-2模型的整体推理。如下图所示，为GPT-2模型的一次完整推理过程。 2.循环推理，GPT-2模型不像其他模型一样只需要执行一次推理，而是需要循环执行多次推理才能完成。首先，模型推理限定在for循环中，将输入数据input_ids，输入到model.run({...})中执行推理，生成一个token的id。其次，将推理结果拼接到输入数据input_ids中，执行下一次循环。最后，当循环结束或者生成的词为[SEP]结束标志符时，完成GPT-2模型的整体推理。如下图所示，为GPT-2模型的一次完整推理过程。
<img src="../Images/GPT_02.png" style="zoom:70%;" align=middle> <img src="./Images/GPT_02.png" style="zoom:70%;" align=middle>
## 数据后处理 ## 数据后处理
......
Python/NLP/GPT2/gpt2.py → Python/gpt2.py
View file @ b2735663
...@@ -5,7 +5,7 @@ import migraphx ...@@ -5,7 +5,7 @@ import migraphx
# 加载词汇表 # 加载词汇表
print("INFO: Complete loading the vocabulary") print("INFO: Complete loading the vocabulary")
vocab_file = os.path.join('../../../Resource/Models/NLP/GPT2', 'vocab_shici.txt') vocab_file = os.path.join('../Resource/', 'vocab_shici.txt')
tokenizer = BertTokenizerFast(vocab_file, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]") tokenizer = BertTokenizerFast(vocab_file, sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]")
# 设置最大输入shape # 设置最大输入shape
...@@ -13,7 +13,7 @@ maxInput={"input":[1,1000]} ...@@ -13,7 +13,7 @@ maxInput={"input":[1,1000]}
# 加载模型 # 加载模型
print("INFO: Parsing and compiling the model") print("INFO: Parsing and compiling the model")
model = migraphx.parse_onnx("../../../Resource/Models/NLP/GPT2/GPT2_shici.onnx", map_input_dims=maxInput) model = migraphx.parse_onnx("../Resource/GPT2_shici.onnx", map_input_dims=maxInput)
inputName=model.get_parameter_names()[0] inputName=model.get_parameter_names()[0]
inputShape=model.get_parameter_shapes()[inputName].lens() inputShape=model.get_parameter_shapes()[inputName].lens()
print("inputName:{0} \ninputShape:{1}".format(inputName,inputShape)) print("inputName:{0} \ninputShape:{1}".format(inputName,inputShape))
......
README.md
View file @ b2735663
...@@ -6,30 +6,72 @@ GPT2模型：第二代生成式预训练模型（Generative Pre-Training2）。 ...@@ -6,30 +6,72 @@ GPT2模型：第二代生成式预训练模型（Generative Pre-Training2）。
## 模型结构 ## 模型结构
GPT2主要使用Transformer的Decoder模块为特征提取器，并对Transformer Decoder进行了一些改动，原本的Decoder包含了两个Multi-Head Attention结构，而GPT2只保留了Mask Multi-Head Attention。 GPT2主要使用Transformer的Decoder模块为特征提取器，并对Transformer Decoder进行了一些改动，原本的Decoder包含了两个Multi-Head Attention结构，而GPT2只保留了Mask Multi-Head Attention。
## 构建安装 ## Python版本推理
在光源可拉取推理的docker镜像，GPT2模型推理的镜像如下： 本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw , 提取码：4pmh ，并将GPT2_shici.onnx模型文件保存在Resource/文件夹下。下面介绍如何运行python代码示例，Python示例的详细说明见Doc目录下的Tutorial_Python.md。
### 下载镜像
在光源中下载MIGraphX镜像：
```python ```python
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1 docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1
``` ```
### 安装Opencv依赖 ### 设置Python环境变量
```
export PYTHONPATH=/opt/dtk/lib:$PYTHONPATH
```
### 安装依赖
```python ```python
cd <path_to_migraphx_samples> # 进入gpt2 migraphx工程根目录
sh ./3rdParty/InstallOpenCVDependences.sh cd <path_to_gpt2_migraphx>
# 进入示例程序目录
cd ./Python/
# 安装依赖
pip install -r requirements.txt
``` ```
### 修改CMakeLists.txt ### 设置动态shape模式
```python
export MIGRAPHX_DYNAMIC_SHAPE=1
```
### 运行示例
在Python目录下执行如下命令运行该示例程序：
```python
python gpt2.py
```
如下所示，采用交互式界面，通过输入开头诗词，GPT2模型可以生成后续的诗句。
<img src="./Doc/Images/GPT_03.png" style="zoom:80%;" align=middle>
## C++版本推理
本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw , 提取码：4pmh ，并将GPT2_shici.onnx模型文件保存在Resource/文件夹下。下面介绍如何运行C++代码示例，C++示例的详细说明见Doc目录下的Tutorial_Cpp.md。
### 下载镜像
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:ort1.14.0_migraphx3.0.0-dtk22.10.1
```
- 如果使用ubuntu系统，需要修改CMakeLists.txt中依赖库路径： ### 修改CMakeLists.txt
将"${CMAKE_CURRENT_SOURCE_DIR}/depend/lib64/"修改为"${CMAKE_CURRENT_SOURCE_DIR}/depend/lib/"
- **MIGraphX2.3.0及以上版本需要c++17** 如果使用ubuntu系统，需要修改CMakeLists.txt中依赖库路径：
将"${CMAKE_CURRENT_SOURCE_DIR}/depend/lib64/"修改为"${CMAKE_CURRENT_SOURCE_DIR}/depend/lib/"
### 安装OpenCV并构建工程 ### 构建工程
``` ```
rbuild build -d depend rbuild build -d depend
...@@ -42,13 +84,13 @@ rbuild build -d depend ...@@ -42,13 +84,13 @@ rbuild build -d depend
**Centos**: **Centos**:
``` ```
export LD_LIBRARY_PATH=<path_to_migraphx_samples>/depend/lib64/:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=<path_to_gpt2_migraphx>/depend/lib64/:$LD_LIBRARY_PATH
``` ```
**Ubuntu**: **Ubuntu**:
``` ```
export LD_LIBRARY_PATH=<path_to_migraphx_samples>/depend/lib/:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=<path_to_gpt2_migraphx>/depend/lib/:$LD_LIBRARY_PATH
``` ```
然后执行: 然后执行:
...@@ -57,61 +99,23 @@ export LD_LIBRARY_PATH=<path_to_migraphx_samples>/depend/lib/:$LD_LIBRARY_PATH ...@@ -57,61 +99,23 @@ export LD_LIBRARY_PATH=<path_to_migraphx_samples>/depend/lib/:$LD_LIBRARY_PATH
source ~/.bashrc source ~/.bashrc
``` ```
## 推理 ### 设置动态shape模式
本次采用GPT-2模型进行诗词生成任务，模型文件下载链接：https://pan.baidu.com/s/1KWeoUuakCZ5dualK69qCcw , 提取码：4pmh ，并将GPT2_shici.onnx模型文件保存在model文件夹下。下面介绍如何运行python代码和C++代码示例，具体推理代码解析，在Doc目录中有详细说明。
### python版本推理
1.参考《MIGraphX教程》中的安装方法安装MIGraphX并设置好PYTHONPATH
2.安装依赖：
```python
# 进入migraphx samples工程根目录
cd <path_to_migraphx_samples>
# 进入示例程序目录
cd Python/NLP/GPT2
# 安装依赖
pip install -r requirements.txt
``` ```
3.设置环境变量：
```python
# 设置动态shape模式
export MIGRAPHX_DYNAMIC_SHAPE=1 export MIGRAPHX_DYNAMIC_SHAPE=1
``` ```
4.在Python/NLP/GPT2目录下执行如下命令运行该示例程序： ### 运行示例
```python ```python
python gpt2.py # 进入gpt2 migraphx工程根目录
``` cd <path_to_gpt2_migraphx>
如下所示，采用交互式界面，通过输入开头诗词，GPT2模型可以生成后续的诗句。
<img src="./Doc/Images/GPT_03.png" style="zoom:80%;" align=middle>
### C++版本推理
切换到build目录中，执行如下命令：
```python # 进入build目录
cd ./build/ cd ./build/
./MIGraphX_Samples
```
根据提示选择运行GPT2模型的示例程序 # 执行示例程序
./GPT2
```python
# 设置动态shape模式
export MIGRAPHX_DYNAMIC_SHAPE=1
# 运行示例
./MIGraphX_Samples 0
``` ```
如下所示，采用交互式界面，通过输入开头诗词，GPT2模型可以推理出后续的诗句。 如下所示，采用交互式界面，通过输入开头诗词，GPT2模型可以推理出后续的诗句。
...@@ -124,6 +128,4 @@ https://developer.hpccube.com/codes/modelzoo/gpt2_migraphx ...@@ -124,6 +128,4 @@ https://developer.hpccube.com/codes/modelzoo/gpt2_migraphx
## 参考资料 ## 参考资料
https://github.com/yangjianxin1/GPT2-chitchat
https://github.com/Morizeyao/GPT2-Chinese https://github.com/Morizeyao/GPT2-Chinese
\ No newline at end of file
Resource/Configuration.xml deleted 100644 → 0
View file @ 5a36110c
<?xml version="1.0" encoding="GB2312"?>
<opencv_storage>
<!--GPT2-->
<GPT2>
<ModelPath>"../Resource/Models/NLP/GPT2/GPT2_shici.onnx"</ModelPath>
</GPT2>
</opencv_storage>
Src/NLP/GPT2/GPT2.cpp → Src/GPT2.cpp
View file @ b2735663
#include <fstream> #include <GPT2.h>
#include <sstream>
#include <migraphx/onnx.hpp> #include <migraphx/onnx.hpp>
#include <migraphx/gpu/target.hpp> #include <migraphx/gpu/target.hpp>
#include <migraphx/gpu/hip.hpp>
#include <migraphx/generate.hpp>
#include <migraphx/quantization.hpp>
#include <migraphx/reshape2.hpp>
#include <CommonUtility.h>
#include <Filesystem.h> #include <Filesystem.h>
#include <SimpleLog.h> #include <SimpleLog.h>
#include <algorithm> #include <algorithm>
#include <string>
#include <vector>
#include <stdexcept> #include <stdexcept>
#include <GPT2.h>
#include <tokenization.h> #include <tokenization.h>
namespace migraphxSamples namespace migraphxSamples
{ {
GPT2::GPT2():logFile(NULL) GPT2::GPT2()
{ {
} }
GPT2::~GPT2() GPT2::~GPT2()
{ {
configurationFile.release();
} }
ErrorCode GPT2::Initialize(InitializationParameterOfNLP initParamOfNLPGPT2) ErrorCode GPT2::Initialize()
{ {
// 初始化(获取日志文件,加载配置文件等) // 获取模型文件
ErrorCode errorCode=DoCommonInitialization(initParamOfNLPGPT2); std::string modelPath="../Resource/GPT2_shici.onnx";
if(errorCode!=SUCCESS)
{
LOG_ERROR(logFile,"fail to DoCommonInitialization\n");
return errorCode;
}
LOG_INFO(logFile,"succeed to DoCommonInitialization\n");
// 获取配置文件参数
FileNode netNode = configurationFile["GPT2"];
std::string modelPath=initializationParameter.parentPath+(std::string)netNode["ModelPath"];
// 设置最大输入shape // 设置最大输入shape
migraphx::onnx_options onnx_options; migraphx::onnx_options onnx_options;
...@@ -51,16 +34,16 @@ ErrorCode GPT2::Initialize(InitializationParameterOfNLP initParamOfNLPGPT2) ...@@ -51,16 +34,16 @@ ErrorCode GPT2::Initialize(InitializationParameterOfNLP initParamOfNLPGPT2)
// 加载模型 // 加载模型
if(Exists(modelPath)==false) if(Exists(modelPath)==false)
{ {
LOG_ERROR(logFile,"%s not exist!\n",modelPath.c_str()); LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
return MODEL_NOT_EXIST; return MODEL_NOT_EXIST;
} }
net = migraphx::parse_onnx(modelPath, onnx_options); net = migraphx::parse_onnx(modelPath, onnx_options);
LOG_INFO(logFile,"succeed to load model: %s\n",GetFileName(modelPath).c_str()); LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
// 获取模型输入属性 // 获取模型输入属性
std::pair<std::string, migraphx::shape> inputAttribute=*(net.get_parameter_shapes().begin()); std::unordered_map<std::string, migraphx::shape> inputMap=net.get_parameter_shapes();
inputName=inputAttribute.first; inputName=inputMap.begin()->first;
inputShape=inputAttribute.second; inputShape=inputMap.begin()->second;
// 设置模型为GPU模式 // 设置模型为GPU模式
migraphx::target gpuTarget = migraphx::gpu::target{}; migraphx::target gpuTarget = migraphx::gpu::target{};
...@@ -70,41 +53,8 @@ ErrorCode GPT2::Initialize(InitializationParameterOfNLP initParamOfNLPGPT2) ...@@ -70,41 +53,8 @@ ErrorCode GPT2::Initialize(InitializationParameterOfNLP initParamOfNLPGPT2)
options.device_id=0; // 设置GPU设备，默认为0号设备 options.device_id=0; // 设置GPU设备，默认为0号设备
options.offload_copy=true; // 设置offload_copy options.offload_copy=true; // 设置offload_copy
net.compile(gpuTarget,options); net.compile(gpuTarget,options);
LOG_INFO(logFile,"succeed to compile model: %s\n",GetFileName(modelPath).c_str()); LOG_INFO(stdout,"succeed to compile model: %s\n",GetFileName(modelPath).c_str());
return SUCCESS;
}
ErrorCode GPT2::DoCommonInitialization(InitializationParameterOfNLP initParamOfNLPGPT2)
{
initializationParameter = initParamOfNLPGPT2;
// 获取日志文件
logFile=LogManager::GetInstance()->GetLogFile(initializationParameter.logName);
// 加载配置文件
std::string configFilePath=initializationParameter.configFilePath;
if(!Exists(configFilePath))
{
LOG_ERROR(logFile, "no configuration file!\n");
return CONFIG_FILE_NOT_EXIST;
}
if(!configurationFile.open(configFilePath, FileStorage::READ))
{
LOG_ERROR(logFile, "fail to open configuration file\n");
return FAIL_TO_OPEN_CONFIG_FILE;
}
LOG_INFO(logFile, "succeed to open configuration file\n");
// 修改父路径
std::string &parentPath = initializationParameter.parentPath;
if (!parentPath.empty())
{
if(!IsPathSeparator(parentPath[parentPath.size() - 1]))
{
parentPath+=PATH_SEPARATOR;
}
}
return SUCCESS; return SUCCESS;
} }
...@@ -126,8 +76,8 @@ long unsigned int GPT2::Inference(const std::vector<long unsigned int> &input_id ...@@ -126,8 +76,8 @@ long unsigned int GPT2::Inference(const std::vector<long unsigned int> &input_id
std::vector<std::vector<std::size_t>> inputShapes; std::vector<std::vector<std::size_t>> inputShapes;
inputShapes.push_back({1,input_id.size()}); inputShapes.push_back({1,input_id.size()});
// 输入数据 // 创建输入数据
migraphx::parameter_map inputData; std::unordered_map<std::string, migraphx::argument> inputData;
inputData[inputName]=migraphx::argument{migraphx::shape(inputShape.type(),inputShapes[0]),(long unsigned int*)input}; inputData[inputName]=migraphx::argument{migraphx::shape(inputShape.type(),inputShapes[0]),(long unsigned int*)input};
// 推理 // 推理
...@@ -163,7 +113,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer, ...@@ -163,7 +113,7 @@ ErrorCode GPT2::Preprocessing(cuBERT::FullTokenizer tokenizer,
std::vector<long unsigned int> &input_id) std::vector<long unsigned int> &input_id)
{ {
// 分词操作 // 分词操作
int max_seq_length =1024; int max_seq_length =1000;
std::vector<std::string> tokens_question; std::vector<std::string> tokens_question;
tokens_question.reserve(max_seq_length); tokens_question.reserve(max_seq_length);
tokenizer.tokenize(question, &tokens_question, max_seq_length); tokenizer.tokenize(question, &tokens_question, max_seq_length);
......
Src/NLP/GPT2/GPT2.h → Src/GPT2.h
View file @ b2735663
#ifndef GPT2_H #ifndef __GPT2_H__
#define GPT2_H #define __GPT2_H__
#include <cstdint> #include <cstdint>
#include <string> #include <string>
#include <migraphx/program.hpp> #include <migraphx/program.hpp>
#include <CommonDefinition.h>
#include <tokenization.h> #include <tokenization.h>
using namespace cuBERT;
namespace migraphxSamples namespace migraphxSamples
{ {
typedef enum _ErrorCode
{
SUCCESS=0,
MODEL_NOT_EXIST,
CONFIG_FILE_NOT_EXIST,
FAIL_TO_LOAD_MODEL,
FAIL_TO_OPEN_CONFIG_FILE,
}ErrorCode;
typedef struct _Predictions typedef struct _Predictions
{ {
long unsigned int index; long unsigned int index;
...@@ -24,7 +31,7 @@ public: ...@@ -24,7 +31,7 @@ public:
~GPT2(); ~GPT2();
ErrorCode Initialize(InitializationParameterOfNLP initParamOfNLPGPT2); ErrorCode Initialize();
ErrorCode Preprocessing(cuBERT::FullTokenizer tokenizer, ErrorCode Preprocessing(cuBERT::FullTokenizer tokenizer,
char *question, char *question,
...@@ -33,13 +40,6 @@ public: ...@@ -33,13 +40,6 @@ public:
long unsigned int Inference(const std::vector<long unsigned int> &input_id); long unsigned int Inference(const std::vector<long unsigned int> &input_id);
private: private:
ErrorCode DoCommonInitialization(InitializationParameterOfNLP initParamOfNLPGPT2);
private:
FILE *logFile;
cv::FileStorage configurationFile;
InitializationParameterOfNLP initializationParameter;
migraphx::program net; migraphx::program net;
std::string inputName; std::string inputName;
migraphx::shape inputShape; migraphx::shape inputShape;
......
Src/Sample.cpp deleted 100644 → 0
View file @ 5a36110c
#include <Sample.h>
#include <SimpleLog.h>
#include <GPT2.h>
#include <tokenization.h>
#include <fstream>
using namespace std;
using namespace migraphx;
using namespace migraphxSamples;
void Sample_GPT2()
{
// 加载GPT2模型
GPT2 gpt2;
InitializationParameterOfNLP initParamOfNLPGPT2;
initParamOfNLPGPT2.parentPath = "";
initParamOfNLPGPT2.configFilePath = CONFIG_FILE;
initParamOfNLPGPT2.logName = "";
ErrorCode errorCode = gpt2.Initialize(initParamOfNLPGPT2);
if (errorCode != SUCCESS)
{
LOG_ERROR(stdout, "fail to initialize GPT2!\n");
exit(-1);
}
LOG_INFO(stdout, "succeed to initialize GPT2\n");
// 加载词汇表，用于编码和解码
cuBERT::FullTokenizer tokenizer = cuBERT::FullTokenizer("../Resource/Models/NLP/GPT2/vocab_shici.txt");
std::ifstream infile;
std::string buf;
std::vector<std::string> output;
infile.open("../Resource/Models/NLP/GPT2/vocab_shici.txt");
while (std::getline(infile,buf))
{
output.push_back(buf);
}
std::vector<long unsigned int> input_id;
char question[100];
std::vector<long unsigned int> score;
std::vector<std::string> result;
std::cout << "开始和GPT2对诗，输入CTRL + Z以退出" << std::endl;
while (true)
{
// 数据预处理
std::cout << "question: ";
cin.getline(question, 100);
gpt2.Preprocessing(tokenizer, question, input_id);
// 推理
for(int i=0;i<50;++i)
{
long unsigned int outputs = gpt2.Inference(input_id);
if(outputs == 102)
{
break;
}
input_id.push_back(outputs);
score.push_back(outputs);
}
// 将数值映射为字符
for(int i=0;i<score.size();++i)
{
result.push_back(output[score[i]]);
}
// 打印结果
std::cout << "chatbot: ";
std::cout << question;
for(int j=0; j<result.size();++j)
{
std::cout << result[j];
}
std::cout << std::endl;
// 清除数据
input_id.clear();
result.clear();
score.clear();
}
}
Src/Sample.h deleted 100644 → 0
View file @ 5a36110c
// 示例程序
#ifndef __SAMPLE_H__
#define __SAMPLE_H__
// GPT2 Dynamic sample
void Sample_GPT2();
#endif
\ No newline at end of file
Src/Utility/CommonDefinition.h deleted 100644 → 0
View file @ 5a36110c
// 常用数据类型和宏定义
#ifndef __COMMON_DEFINITION_H__
#define __COMMON_DEFINITION_H__
#include <string>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
namespace migraphxSamples
{
// 路径分隔符(Linux:‘/’,Windows:’\\’)
#ifdef _WIN32
#define PATH_SEPARATOR '\\'
#else
#define PATH_SEPARATOR '/'
#endif
#define CONFIG_FILE "../Resource/Configuration.xml"
typedef struct __Time
{
string year;
string month;
string day;
string hour;
string minute;
string second;
string millisecond; // ms
string microsecond; // us
string weekDay;
}_Time;
typedef enum _ErrorCode
{
SUCCESS=0, // 0
MODEL_NOT_EXIST, // 模型不存在
CONFIG_FILE_NOT_EXIST, // 配置文件不存在
FAIL_TO_LOAD_MODEL, // 加载模型失败
FAIL_TO_OPEN_CONFIG_FILE, // 加载配置文件失败
IMAGE_ERROR, // 图像错误
}ErrorCode;
typedef struct _ResultOfPrediction
{
float confidence;
int label;
_ResultOfPrediction():confidence(0.0f),label(0){}
}ResultOfPrediction;
typedef struct _ResultOfDetection
{
Rect boundingBox;
float confidence;
int classID;
string className;
bool exist;
_ResultOfDetection():confidence(0.0f),classID(0),exist(true){}
}ResultOfDetection;
typedef struct _InitializationParameterOfDetector
{
std::string parentPath;
std::string configFilePath;
cv::Size inputSize;
std::string logName;
}InitializationParameterOfDetector;
typedef struct _InitializationParameterOfDetector InitializationParameterOfClassifier;
typedef struct _InitializationParameterOfDetector InitializationParameterOfSuperresolution;
typedef struct _InitializationParameterOfDetector InitializationParameterOfSegmentation;
typedef struct _InitializationParameterOfDetector InitializationParameterOfNLP;
typedef struct _InitializationParameterOfDetector InitializationParameterOfOcr;
typedef struct _InitializationParameterOfDetector InitializationParameterOfDB;
typedef struct _InitializationParameterOfDetector InitializationParameterOfSVTR;
}
#endif
Src/Utility/CommonUtility.cpp deleted 100644 → 0
View file @ 5a36110c
#include <CommonUtility.h>
#include <assert.h>
#include <ctype.h>
#include <time.h>
#include <stdlib.h>
#include <algorithm>
#include <sstream>
#include <vector>
#ifdef _WIN32
#include <io.h>
#include <direct.h>
#include <Windows.h>
#else
#include <unistd.h>
#include <dirent.h>
#include <sys/stat.h>
#include <sys/time.h>
#endif
#include <SimpleLog.h>
namespace migraphxSamples
{
_Time GetCurrentTime3()
{
_Time currentTime;
#if (defined WIN32 || defined _WIN32)
SYSTEMTIME systemTime;
GetLocalTime(&systemTime);
char temp[8] = { 0 };
sprintf(temp, "%04d", systemTime.wYear);
currentTime.year=string(temp);
sprintf(temp, "%02d", systemTime.wMonth);
currentTime.month=string(temp);
sprintf(temp, "%02d", systemTime.wDay);
currentTime.day=string(temp);
sprintf(temp, "%02d", systemTime.wHour);
currentTime.hour=string(temp);
sprintf(temp, "%02d", systemTime.wMinute);
currentTime.minute=string(temp);
sprintf(temp, "%02d", systemTime.wSecond);
currentTime.second=string(temp);
sprintf(temp, "%03d", systemTime.wMilliseconds);
currentTime.millisecond=string(temp);
sprintf(temp, "%d", systemTime.wDayOfWeek);
currentTime.weekDay=string(temp);
#else
struct timeval tv;
struct tm *p;
gettimeofday(&tv, NULL);
p = localtime(&tv.tv_sec);
char temp[8]={0};
sprintf(temp,"%04d",1900+p->tm_year);
currentTime.year=string(temp);
sprintf(temp,"%02d",1+p->tm_mon);
currentTime.month=string(temp);
sprintf(temp,"%02d",p->tm_mday);
currentTime.day=string(temp);
sprintf(temp,"%02d",p->tm_hour);
currentTime.hour=string(temp);
sprintf(temp,"%02d",p->tm_min);
currentTime.minute=string(temp);
sprintf(temp,"%02d",p->tm_sec);
currentTime.second=string(temp);
sprintf(temp,"%03d",tv.tv_usec/1000);
currentTime.millisecond = string(temp);
sprintf(temp, "%03d", tv.tv_usec % 1000);
currentTime.microsecond = string(temp);
sprintf(temp, "%d", p->tm_wday);
currentTime.weekDay = string(temp);
#endif
return currentTime;
}
std::vector<std::string> SplitString(std::string str, std::string separator)
{
std::string::size_type pos;
std::vector<std::string> result;
str+=separator;//扩展字符串以方便操作
int size=str.size();
for(int i=0; i<size; i++)
{
pos=str.find(separator,i);
if(pos<size)
{
std::string s=str.substr(i,pos-i);
result.push_back(s);
i=pos+separator.size()-1;
}
}
return result;
}
bool CompareConfidence(const ResultOfDetection &L,const ResultOfDetection &R)
{
return L.confidence > R.confidence;
}
bool CompareArea(const ResultOfDetection &L,const ResultOfDetection &R)
{
return L.boundingBox.area() > R.boundingBox.area();
}
void NMS(vector<ResultOfDetection> &detections, float IOUThreshold)
{
// sort
std::sort(detections.begin(), detections.end(), CompareConfidence);
for (int i = 0; i<detections.size(); ++i)
{
if (detections[i].exist)
{
for (int j = i + 1; j<detections.size(); ++j)
{
if (detections[j].exist)
{
// compute IOU
float intersectionArea = (detections[i].boundingBox & detections[j].boundingBox).area();
float intersectionRate = intersectionArea / (detections[i].boundingBox.area() + detections[j].boundingBox.area() - intersectionArea);
if (intersectionRate>IOUThreshold)
{
detections[j].exist = false;
}
}
}
}
}
}
}
Src/Utility/CommonUtility.h deleted 100644 → 0
View file @ 5a36110c
// 常用工具
#ifndef __COMMON_UTILITY_H__
#define __COMMON_UTILITY_H__
#include <mutex>
#include <string>
#include <vector>
#include <CommonDefinition.h>
using namespace std;
namespace migraphxSamples
{
// 分割字符串
std::vector<std::string> SplitString(std::string str,std::string separator);
// 排序规则: 按照置信度或者按照面积排序
bool CompareConfidence(const ResultOfDetection &L,const ResultOfDetection &R);
bool CompareArea(const ResultOfDetection &L,const ResultOfDetection &R);
void NMS(std::vector<ResultOfDetection> &detections, float IOUThreshold);
}
#endif
Src/Utility/Filesystem.cpp
View file @ b2735663
...@@ -11,12 +11,7 @@ ...@@ -11,12 +11,7 @@
#include <unistd.h> #include <unistd.h>
#include <dirent.h> #include <dirent.h>
#endif #endif
#include <CommonUtility.h>
#include <opencv2/opencv.hpp>
#include <SimpleLog.h>
using namespace cv;
// 路径分隔符(Linux:‘/’,Windows:’\\’) // 路径分隔符(Linux:‘/’,Windows:’\\’)
#ifdef _WIN32 #ifdef _WIN32
#define PATH_SEPARATOR '\\' #define PATH_SEPARATOR '\\'
...@@ -24,9 +19,31 @@ using namespace cv; ...@@ -24,9 +19,31 @@ using namespace cv;
#define PATH_SEPARATOR '/' #define PATH_SEPARATOR '/'
#endif #endif
using namespace std;
namespace migraphxSamples namespace migraphxSamples
{ {
static std::vector<std::string> SplitString(std::string str, std::string separator)
{
std::string::size_type pos;
std::vector<std::string> result;
str+=separator;//扩展字符串以方便操作
int size=str.size();
for(int i=0; i<size; i++)
{
pos=str.find(separator,i);
if(pos<size)
{
std::string s=str.substr(i,pos-i);
result.push_back(s);
i=pos+separator.size()-1;
}
}
return result;
}
#if defined _WIN32 || defined WINCE #if defined _WIN32 || defined WINCE
const char dir_separators[] = "/\\"; const char dir_separators[] = "/\\";
...@@ -293,7 +310,7 @@ namespace migraphxSamples ...@@ -293,7 +310,7 @@ namespace migraphxSamples
} }
else else
{ {
LOG_INFO(stdout, "could not open directory: %s", directory.c_str()); printf("could not open directory: %s", directory.c_str());
} }
} }
...@@ -390,7 +407,7 @@ namespace migraphxSamples ...@@ -390,7 +407,7 @@ namespace migraphxSamples
#endif #endif
if (!result) if (!result)
{ {
LOG_INFO(stdout, "can't remove directory: %s\n", path.c_str()); printf("can't remove directory: %s\n", path.c_str());
} }
} }
else else
...@@ -402,7 +419,7 @@ namespace migraphxSamples ...@@ -402,7 +419,7 @@ namespace migraphxSamples
#endif #endif
if (!result) if (!result)
{ {
LOG_INFO(stdout, "can't remove file: %s\n", path.c_str()); printf("can't remove file: %s\n", path.c_str());
} }
} }
} }
...@@ -438,7 +455,7 @@ namespace migraphxSamples ...@@ -438,7 +455,7 @@ namespace migraphxSamples
{ {
RemoveAll(path); RemoveAll(path);
++numberOfFiles; ++numberOfFiles;
LOG_INFO(stdout, "%s deleted! number of deleted files:%d\n", path.c_str(), numberOfFiles); printf("%s deleted! number of deleted files:%d\n", path.c_str(), numberOfFiles);
} }
} }
...@@ -452,7 +469,7 @@ namespace migraphxSamples ...@@ -452,7 +469,7 @@ namespace migraphxSamples
} }
else else
{ {
LOG_INFO(stdout, "could not open directory: %s", directory.c_str()); printf("could not open directory: %s", directory.c_str());
} }
// ����RemoveAllɾ��Ŀ¼ // ����RemoveAllɾ��Ŀ¼
...@@ -592,17 +609,17 @@ namespace migraphxSamples ...@@ -592,17 +609,17 @@ namespace migraphxSamples
if(!srcFile.is_open()) if(!srcFile.is_open())
{ {
LOG_ERROR(stdout,"can not open %s\n",srcPath.c_str()); printf("can not open %s\n",srcPath.c_str());
return false; return false;
} }
if(!dstFile.is_open()) if(!dstFile.is_open())
{ {
LOG_ERROR(stdout, "can not open %s\n", dstPath.c_str()); printf("can not open %s\n", dstPath.c_str());
return false; return false;
} }
if(srcPath==dstPath) if(srcPath==dstPath)
{ {
LOG_ERROR(stdout, "src can not be same with dst\n"); printf("src can not be same with dst\n");
return false; return false;
} }
char buffer[2048]; char buffer[2048];
...@@ -622,7 +639,7 @@ namespace migraphxSamples ...@@ -622,7 +639,7 @@ namespace migraphxSamples
{ {
if(srcPath==dstPath) if(srcPath==dstPath)
{ {
LOG_ERROR(stdout, "src can not be same with dst\n"); printf("src can not be same with dst\n");
return false; return false;
} }
...@@ -662,9 +679,9 @@ namespace migraphxSamples ...@@ -662,9 +679,9 @@ namespace migraphxSamples
// process // process
double process = (1.0*(i + 1) / fileNameList.size()) * 100; double process = (1.0*(i + 1) / fileNameList.size()) * 100;
LOG_INFO(stdout, "%s done! %f% \n", GetFileName(fileNameList[i]).c_str(), process); printf("%s done! %f% \n", GetFileName(fileNameList[i]).c_str(), process);
} }
LOG_INFO(stdout, "all done!(the number of files:%d)\n", fileNameList.size()); printf("all done!(the number of files:%d)\n", fileNameList.size());
return true; return true;
......
Src/Utility/Filesystem.h
View file @ b2735663
...@@ -3,10 +3,8 @@ ...@@ -3,10 +3,8 @@
#ifndef __FILE_SYSTEM_H__ #ifndef __FILE_SYSTEM_H__
#define __FILE_SYSTEM_H__ #define __FILE_SYSTEM_H__
#include <vector>
#include <string> #include <string>
#include <vector>
using namespace std;
namespace migraphxSamples namespace migraphxSamples
{ {
...@@ -21,7 +19,7 @@ bool IsDirectory(const std::string &path); ...@@ -21,7 +19,7 @@ bool IsDirectory(const std::string &path);
bool IsPathSeparator(char c); bool IsPathSeparator(char c);
// 路径拼接 // 路径拼接
string JoinPath(const std::string &base, const std::string &path); std::string JoinPath(const std::string &base, const std::string &path);
// 创建多级目录,注意：创建多级目录的时候，目标目录是不能有文件存在的 // 创建多级目录,注意：创建多级目录的时候，目标目录是不能有文件存在的
bool CreateDirectories(const std::string &directoryPath); bool CreateDirectories(const std::string &directoryPath);
...@@ -49,14 +47,13 @@ void Remove(const std::string &directory, const std::string &extension=""); ...@@ -49,14 +47,13 @@ void Remove(const std::string &directory, const std::string &extension="");
/** 获取路径的文件名和扩展名 /** 获取路径的文件名和扩展名
* *
* 示例：path为D:/1/1.txt,则GetFileName()为1.txt,GetFileName_NoExtension()为1,GetExtension()为.txt,GetParentPath()为D:/1/ * 示例：path为D:/1/1.txt,则GetFileName()为1.txt,GetFileName_NoExtension()为1,GetExtension()为.txt,GetParentPath()为D:/1/
*/ */
string GetFileName(const std::string &path); // 1.txt std::string GetFileName(const std::string &path);
string GetFileName_NoExtension(const std::string &path); // 1 std::string GetFileName_NoExtension(const std::string &path);
string GetExtension(const std::string &path);// .txt std::string GetExtension(const std::string &path);
string GetParentPath(const std::string &path);// D:/1/ std::string GetParentPath(const std::string &path);
// 拷贝文件:CopyFile("D:/1.txt","D:/2.txt");将1.txt拷贝为2.txt // 拷贝文件
bool CopyFile(const std::string srcPath,const std::string dstPath); bool CopyFile(const std::string srcPath,const std::string dstPath);
/** 拷贝目录 /** 拷贝目录
......
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