pytorch学习003- -如何导出c++中可用的pytorch模型

在 C++ 中加载 TorchScript 模型

推荐阅读官方文档：
如何保存模型

如何在c++中加载模型

pytorch的c++ api

torchScript文档

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以下内容基于官方文档写一些注释~~

将pytorch模型转换为torch脚本

pytorch模型从python到c++是通过torchScript实现的
跟踪是指通过示例输入进行一次推理并获取模型的结构和参数，并记录下这些输入在模型中的流转。
注释是向模型中添加显式注释，通知编译器对模型代码进行解析。

通过跟踪转换为torch脚本并保存

official code：

import torch
import torchvision

# An instance of your model.
model = torchvision.models.resnet18()	# 从torchvision导入模型

# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)	#创建一个样例输入

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)	#调用torch.jit.trace()来进行跟踪

# 附加部分
traced_script_module.save('xxxx.pt')	#直接将跟踪结果进行保存

通过注释转换为torch脚本并保存

class MyModule(torch.nn.Module):	#示例模型的定义
    def __init__(self, N, M):
        super(MyModule, self).__init__()
        self.weight = torch.nn.Parameter(torch.rand(N, M))

    def forward(self, input):
        if input.sum() > 0:
          output = self.weight.mv(input)
        else:
          output = self.weight + input
        return output

my_module = MyModule(10,20)	# 将模型实例化
sm = torch.jit.script(my_module)	#使用script

sm.save('xxxxx.pt')	#保存为pt文件
  

在C++中加载torch脚本

最简单的c++程序，用来加载torch脚本

//torch/script.h在libtorch库中
#include  // One-stop header.

#include 
#include 

int main(int argc, const char* argv[]) {
  if (argc != 2) {
    std::cerr << "usage: example-app \n";
    return -1;
  }


  torch::jit::script::Module module;
  try {
    // Deserialize the ScriptModule from a file using torch::jit::load().
    module = torch::jit::load(argv[1]);
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }

  std::cout << "ok\n";
}

接下来写CMakeLists.txt文件

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(custom_ops)

find_package(Torch REQUIRED)

add_executable(example-app example-app.cpp)
target_link_libraries(example-app "${TORCH_LIBRARIES}")
set_property(TARGET example-app PROPERTY CXX_STANDARD 14)

然后下载libtorch
libtorch各个版本
官网下载
也可以选择手动编译

在libtorch文件夹中，应当有

此时c++项目目录：

编译构建应用程序

mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
cmake --build . --config Release

如果一切正常，显示如下

root@4b5a67132e81:/example-app# mkdir build
root@4b5a67132e81:/example-app# cd build
root@4b5a67132e81:/example-app/build# cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
-- The C compiler identification is GNU 5.4.0
-- The CXX compiler identification is GNU 5.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Looking for pthread_create
-- Looking for pthread_create - not found
-- Looking for pthread_create in pthreads
-- Looking for pthread_create in pthreads - not found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Found Threads: TRUE
-- Configuring done
-- Generating done
-- Build files have been written to: /example-app/build
root@4b5a67132e81:/example-app/build# make
Scanning dependencies of target example-app
[ 50%] Building CXX object CMakeFiles/example-app.dir/example-app.cpp.o
[100%] Linking CXX executable example-app
[100%] Built target example-app

测试一下：

./example-app xxxx.pt

正确的输出应该是ok，说明我们的模型已经成功加载。

现在来尝试进行推理：
在main函数的最后一行，添加代码

// Create a vector of inputs. 创建输入
std::vector<torch::jit::IValue> inputs;
inputs.push_back(torch::ones({1, 3, 224, 224}));

// Execute the model and turn its output into a tensor.
// 利用模型进行推导
at::Tensor output = module.forward(inputs).toTensor();
std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';

保存文件后使用同样的方法进行测试，得到：
-0.2698 -0.0381 0.4023 -0.3010 -0.0448
[ Variable[CPUFloatType]{1,5} ]

作为对比，之前python的输出为：
tensor([-0.2698, -0.0381, 0.4023, -0.3010, -0.0448], grad_fn=)

如果想基于GPU进行测试，那么在

module = torch::jit::load(argv[1]);

的后面加上

module.to(at::kCUDA)

同时保证其模型的输入也存在于CUDA内存中

tensor.to(at::kCUDA)

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