适合建设网站的国外服务器,seo基础入门视频教程,云建站平台哪家好,第9区在线观看完整版环境配置 建议在linux上配置对应环境 可以在autoDL上租一个服务器来搭建#xff0c;带有pytorch的环境 https://www.autodl.com/home 我自己的win电脑上安装了pytorch#xff0c;但是配置时会报错#xff0c;于是到ubuntu上配置
电脑上装有pytorch的就不需要再下载libtorc…环境配置 建议在linux上配置对应环境 可以在autoDL上租一个服务器来搭建带有pytorch的环境 https://www.autodl.com/home 我自己的win电脑上安装了pytorch但是配置时会报错于是到ubuntu上配置
电脑上装有pytorch的就不需要再下载libtorch了pytorch就带有libtorch
首先通过下面的代码找出pytorch自带的环境import torch
torch.utils.cmake_prefix_path整体架构图 编写CMakeLists.txt文件
cmake_minimum_required(VERSION 3.6 FATAL_ERROR)
project(test-libtorch)
# 设置libtorch的位置
set(CMAKE_PREFIX_PATH /root/miniconda3/lib/python3.8/site-packages/torch/share/cmake)
message(${CMAKE_PREFIX_PATH})
find_package(Torch REQUIRED)
#op.cpp exe
add_executable(test-libtorch op.cpp)# #link libtorch .a .so
target_link_libraries(test-libtorch ${TORCH_LIBRARIES})
#
set_property(TARGET test-libtorch PROPERTY CXX_STANDARD 14)编写op.cpp
#includetorch/torch.h
#include iostreamint main() {torch::Tensor tensor torch::rand({ 2, 3 });std::cout tensor std::endl;return 0;
}编译
先创建build文件夹再在build里面编译mkdir build
cd build
cmake ../
make
./test-libtorch配置opencv
编写第一个自定义算子
算子的定义与实现 由于我们重写算子一般都是为了性能所以多半使用C op.cpp
#include torch/torch.h
// 实现
torch::Tensor my_add(torch::Tensor t1, torch::Tensor t2)
{assert(t1.size(0) t2.size(0));assert(t1.size(1) t2.size(1));torch::Tensor tt1t2;return t;
}
// C函数绑定到python中
TORCH_LIBRARY(my_ops, m)
{m.def(my_add, my_add);
}CMakeLists.txt
cmake_minimum_required(VERSION 3.6 FATAL_ERROR)
project(test-libtorch)
# 设置libtorch的位置
set(CMAKE_PREFIX_PATH /root/miniconda3/lib/python3.8/site-packages/torch/share/cmake)
message(${CMAKE_PREFIX_PATH})
find_package(Torch REQUIRED)
# find_package(Opencv REQUIRED)
add_library(test-libtorch SHARED op.cpp)
# add_executable(test-libtorch op.cpp)# #link libtorch .a .so
target_link_libraries(test-libtorch ${TORCH_LIBRARIES})
#
set_property(TARGET test-libtorch PROPERTY CXX_STANDARD 14)test.py
import torchlib_path r/root/test/build/libtest-libtorch.so
torch.ops.load_library(lib_path)def test_add():a torch.rand([10, 10, 3])b torch.rand([10, 10, 3])c torch.ops.my_ops.my_add(a, b)d a bassert torch.allclose(c, d)
test_add()运行步骤
mkdir build
cd build
cmake ../
make
cd ../
python test.py将op.cpp替换 op.cpp
#include torch/torch.h
// 实现
torch::Tensor my_add(torch::Tensor t1, torch::Tensor t2)
{assert(t1.size(0) t2.size(0));assert(t1.size(1) t2.size(1));torch::Tensor tt1t2;return t;
}
// 直接用Pybind绑定了C函数
PYBIND11_MODULE(my_ops, m)
{m.def(my_add, my_add);
}编写setup.py
from setuptools import setup
from torch.utils import cpp_extensionsetup(namemy_add,ext_modules[cpp_extension.CppExtension(my_ops, [op.cpp])],cmdclass{build_ext: cpp_extension.BuildExtension})test.py
import torch
import my_opsdef test_add():a torch.rand([10, 10, 3])b torch.rand([10, 10, 3])c my_ops.my_add(a, b)d a bassert torch.allclose(c, d)
test_add()运行步骤
python setup.py install
python test.py
