OpenFOAM libtorch tutorial step by step

李东岳

一个libtorch下自编码器范例

可以自己添加skip connection变成Unet

class NN
:
    public torch::nn::Module 
{
    torch::nn::Sequential net_;

public:

    NN()
    {
        net_ = register_module
        (
            "net", 
            torch::nn::Sequential
            (
                // encoder
                torch::nn::Conv2d(2, 5, 3),// 2 5 138
                torch::nn::BatchNorm2d(5),
                torch::nn::Tanh(),
                torch::nn::MaxPool2d(2),// 2 5 69
                torch::nn::Conv2d(5, 10, 3), //5 10 67
                torch::nn::BatchNorm2d(10),
                torch::nn::Tanh(),
                torch::nn::MaxPool2d(2), // 5 10 33
                torch::nn::Conv2d(10, 10, 3), // 10 10 31
                torch::nn::BatchNorm2d(10),
                torch::nn::Tanh(),
                torch::nn::MaxPool2d(2), // 10 10 15
                torch::nn::Conv2d(10, 10, 3), // 10 10 13
                torch::nn::BatchNorm2d(10),
                torch::nn::Tanh(),
                torch::nn::MaxPool2d(2), // 10 10 6

                // decoder
                torch::nn::Upsample
                (
                    torch::nn::UpsampleOptions().scale_factor
                    (
                        std::vector<double>{2.0, 2.0}
                    ).mode(torch::kBilinear).align_corners(true)
                ), // 10 10 12
                torch::nn::Conv2d(10, 10, 2), // 10 10 11
                torch::nn::Tanh(),
                torch::nn::Upsample
                (
                    torch::nn::UpsampleOptions().scale_factor
                    (
                        std::vector<double>{2.0, 2.0}
                    ).mode(torch::kBilinear).align_corners(true)
                ),// 10 10 22
                torch::nn::Conv2d(10, 10, 3),// 10 10 20
                torch::nn::Tanh(),
                torch::nn::Upsample
                (
                    torch::nn::UpsampleOptions().scale_factor
                    (
                        std::vector<double>{2.0, 2.0}
                    ).mode(torch::kBilinear).align_corners(true)
                ),// 10 10 40
                torch::nn::Conv2d(10, 10, 3),// 10 10 38
                torch::nn::Tanh(),
                torch::nn::Upsample
                (
                    torch::nn::UpsampleOptions().scale_factor
                    (
                        std::vector<double>{2.0, 2.0}
                    ).mode(torch::kBilinear).align_corners(true)
                ),// 10 10 76
                torch::nn::Conv2d(10, 10, 3),// 10 10 74
                torch::nn::Tanh(),
                torch::nn::Upsample
                (
                    torch::nn::UpsampleOptions().scale_factor
                    (
                        std::vector<double>{2.0, 2.0}
                    ).mode(torch::kBilinear).align_corners(true)
                ),// 10 10 148
                torch::nn::Conv2d(10, 10, 3),// 10 10 146
                torch::nn::Tanh(),
                torch::nn::Conv2d(10, 5, 3),// 10 10 144
                torch::nn::Tanh(),
                torch::nn::Conv2d(5, 2, 3),// 10 10 142
                torch::nn::Tanh(),
                torch::nn::Conv2d(2, 2, 3)// 10 10 140
            )
        );
    }

    auto forward(torch::Tensor x)
    {
        return net_->forward(x);
    }
};

小泽同学

@李东岳感谢李老师，明白需要配合版本使用，不升级是因为课题组在做的求解器模型是从4.1开始的，我也在高版本上运行过，但是总是报错就只能用4.1了，感谢李老师的回复。

李东岳

你可以尝试把openfoam低版本的求解器升级到高版本。

如果没兴趣不整也行。或者马上毕业了也属实没必要。这东西基本是兴趣驱动。。

火丙三金

请问各位有没有用pytorch的

Zhy2022

想请问李老师：我使用全系列虚拟机（您已提前配置好），再运行第6步这个简单例子，报错未找到torchFoam这个命令，是啥原因造成的呢编译过程中有很多warning和note应该不影响吧

李东岳

warning note不影响，把你log上传一下看看

Zhy2022

@李东岳李老师，问题解决了，是我把option文件改错了个地方。。新的问题又来了，我在OF11中（仍是该虚拟机）对bed案例进行foamRun或foamRun -solver multiphaseEuler时，为何报错：```
--> FOAM FATAL ERROR:
solvers table is empty

From function static Foam::autoPtr<Foam::solver> Foam::solver::New(const Foam::word&, Foam::fvMesh&)
in file solver/solverNew.C at line 48.

李东岳

openfoam11直接运行foamRun就行了，不需要指定solver

Zhy2022

@李东岳李老师，直接foamRun还是报一样的错误

hurricane007

添加几点注意

第一步libtorch需要下载到OpenFOAM的目录里面并解压。
第二步WSL2 里面可能没装 uzip，用 sudo apt-install zip uzip 安装
解压torchFoam.tar.xz 后进入torchFoam wmake 的时候，需要把 torchFoam/Make 里面的options里面所有的dyfluid换成自己的这个WSL的用户名，pwd一下看/home/后面是啥就知道了

chengan.wang

@李东岳在 OpenFOAM libtorch tutorial step by step 中说：

更新gcc之后你openfoam就编译不了了。那你尝试安装老版本的libtorch吧

李老师，我用的是ubuntu18.04，Of2.4.0，您有推荐的libtorch版本么

李东岳

@chengan-wang 太老了，gcc版本啥的都太低了，我没有折腾过，你得亲自自己都尝试一下看看哪个能用

chengan.wang

@李东岳李老师，http://dyfluid.com/pinn.html ，#include "output.H"这个文件能补充上传么，想研究一下您的完整代码，谢谢

李东岳

@chengan-wang 那个我已经删了，你可以参考这个写个类似的，纯粹是C++的输出数据：

        if (iter % 500 == 0) 
        {
            cout<< iter << " loss = " << loss.item<double>() << endl;

            auto meshxy = torch::cat({mesh,x,y}, 1);
            std::ofstream filex("results");
            filex << meshxy << "\n";
            filex.close();
        }

http://dyfluid.com/pinn2.html

李东岳

我给你手写了一个output.H：

        if (iter % 500 == 0) 
        {
            cout<< iter << " loss = " << loss.item<double>() << endl;
 
            std::ofstream filex("results");
            filex << upred << "\n";
            filex.close();
        }

chengan.wang

@李东岳谢谢李老师

chengan.wang

@李东岳我看了您提供的PINN算例，感觉目前还没有真正跟of结合起来，是吧？只不过用了of的c++编译环境，可以这么理解么？

李东岳

是。PINN不需要跟FVM结合，只不过用了of的c++编译环境。想跟OpenFOAM结合，除了画网格能结合一下，其他的也结合不上。

李东岳

@chengan-wang

You can use the following code to initialize libtorch tensors from OpenFOAM mesh. It is much easier to generate any kinds of geometries. Please see the following clipped cavity geometry. I am using English since I hope it can be helpful for all the people.

微信图片_20241017082858.png

    // openfoam initialization
    vectorField ofMesh = mesh.C();
    auto meshTorch = torch::full({ofMesh.size(),1,2}, 0.0);
    forAll(ofMesh, i)
    {
        double x = ofMesh[i].x();
        double y = ofMesh[i].y();
        auto posi = torch::tensor({x,y}).reshape({1,2});
        meshTorch[i] = posi;
    }

    const surfaceVectorField& Cf = mesh.Cf(); 
    label topID = mesh.boundaryMesh().findPatchID("top");
    label leftID = mesh.boundaryMesh().findPatchID("left");
    label rightID = mesh.boundaryMesh().findPatchID("right");
    label bottomID = mesh.boundaryMesh().findPatchID("bottom");
    vectorField cfTop = Cf.boundaryField()[topID];
    vectorField cfLeft = Cf.boundaryField()[leftID];
    vectorField cfRight = Cf.boundaryField()[rightID];
    vectorField cfBottom = Cf.boundaryField()[bottomID];

    auto top = torch::full({cfTop.size(),1,2}, 0.0);
    auto left = torch::full({cfLeft.size(),1,2}, 0.0);
    auto right = torch::full({cfRight.size(),1,2}, 0.0);
    auto bottom = torch::full({cfBottom.size(),1,2}, 0.0);

    forAll(cfTop, i)
    {
        double x = cfTop[i].x();
        double y = cfTop[i].y();
        auto posi = torch::tensor({x,y}).reshape({1,2});
        top[i] = posi;
    }

    forAll(cfRight, i)
    {
        double x = cfRight[i].x();
        double y = cfRight[i].y();
        auto posi = torch::tensor({x,y}).reshape({1,2});
        right[i] = posi;
    }

    forAll(cfLeft, i)
    {
        double x = cfLeft[i].x();
        double y = cfLeft[i].y();
        auto posi = torch::tensor({x,y}).reshape({1,2});
        left[i] = posi;
    }

    forAll(cfBottom, i)
    {
        double x = cfBottom[i].x();
        double y = cfBottom[i].y();
        auto posi = torch::tensor({x,y}).reshape({1,2});
        bottom[i] = posi;
    }

    meshTorch = meshTorch.reshape({-1,2});
    top = top.reshape({-1,2});
    left = left.reshape({-1,2});
    right = right.reshape({-1,2});
    bottom = bottom.reshape({-1,2});

    auto u_top = torch::full({cfTop.size()}, vel);
    auto v_top = torch::full({cfTop.size()}, 0.0);

    std::cout<< "top: " << top << std::endl;
    std::cout<< "left: " << left << std::endl;
    std::cout<< "right: " << right << std::endl;
    std::cout<< "bottom: " << bottom << std::endl;

水润晨阳

@李东岳在 OpenFOAM libtorch tutorial step by step 中说：

net.H

#include <torch/torch.h>

class NN
:
    public torch::nn::Module 
{
    torch::nn::Sequential net_;

public:

    NN();

    torch::Tensor forward(torch::Tensor x);
};

net.C

/*---------------------------------------------------------------------------*\
  =========                 |
  \\      /  F ield         | OpenFOAM: The Open Source CFD Toolbox
   \\    /   O peration     | Website:  https://openfoam.org
    \\  /    A nd           | Copyright (C) 2022-2023 OpenFOAM Foundation
     \\/     M anipulation  |
-------------------------------------------------------------------------------
License
    This file is part of OpenFOAM.

    OpenFOAM is free software: you can redistribute it and/or modify it
    under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    OpenFOAM is distributed in the hope that it will be useful, but WITHOUT
    ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
    FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
    for more details.

    You should have received a copy of the GNU General Public License
    along with OpenFOAM.  If not, see <http://www.gnu.org/licenses/>.

Class

See also

SourceFiles

\*---------------------------------------------------------------------------*/
#include "net.H"

NN::NN()
{
    net_ = register_module
    (
        "net", 
        torch::nn::Sequential
        (
            torch::nn::Linear(2,20),
            torch::nn::ReLU(),
            torch::nn::Linear(20,30),
            torch::nn::ReLU(),
            torch::nn::Linear(30,30),
            torch::nn::ReLU(),
            torch::nn::Linear(30,20),
            torch::nn::ReLU(),
            torch::nn::Linear(20,20),
            torch::nn::ReLU(),
            torch::nn::Linear(20,1)
        )
    );
};

torch::Tensor NN::forward(torch::Tensor x) 
{
    return net_->forward(x);
}

这个要编译成库，挂到PINNFoam上面

李老师请问这个如何编译并挂载呢可以详细的指导一下吗

CFD中文网

OpenFOAM libtorch tutorial step by step

一个libtorch下自编码器范例