Then, I will explain the specific procedure.

Advance preparation

--Required --Ubuntu installation (18.04, 20.04)

--When using GPU --Installing the NVIDIA driver ([18.04](https://qiita.com/koba-jon/items/a7c5239fb5c05172c1b3#1-nvidia%E3%83%89%E3%83%A9%E3%82%A4%E3%" 83% 90% E3% 83% BC% E3% 81% AE% E3% 82% A4% E3% 83% B3% E3% 82% B9% E3% 83% 88% E3% 83% BC% E3% 83% AB))) --Installing CUDA ([18.04](https://qiita.com/koba-jon/items/a7c5239fb5c05172c1b3#2-cuda%E3%81%AE%E3%82%A4%E3%83%B3%E3%82 % B9% E3% 83% 88% E3% 83% BC% E3% 83% AB)) --Installing cuDNN ([18.04](https://qiita.com/koba-jon/items/a7c5239fb5c05172c1b3#3-cudnn%E3%81%AE%E3%82%A4%E3%83%B3%E3%82] % B9% E3% 83% 88% E3% 83% BC% E3% 83% AB))

Download LibTorch

PyTorch Official: https://pytorch.org/ Access the link and follow the steps below to download LibTorch.

• PyTorchBuild
  Select ** "Stable" ** if you want to use the stable version, ** "Preview" ** if you want to use the latest version
• Your OS
  I'm using Ubuntu this time, so select ** "Linux" **
• Package
  Since LibTorch will be used this time, select ** "LibTorch" **
• Language
  Since C ++ is used this time, select ** "C ++ / Java" **
• CUDA
  Select ** "Version Name" ** if you use GPU, ** "None" ** if you do not use it
• Run this Command
  Since C ++ 11 or later compiler is used, select ** "Download here (cxx11 ABI)" **

This completes the download of LibTorch itself. It is recommended to place the directory itself in an easy-to-understand location (such as directly under your home directory).

Operation check

First, create a ** temporary source code **.

$ mkdir libtorch_test
$ cd libtorch_test
$ vi main.cpp

Add the following code and save it. This is a program that creates a 3x3 matrix (second-order tensor) with Float type element 1.5 and displays it on the console screen.

main.cpp

#include <iostream>
#include <torch/torch.h>

int main(void){
    torch::Tensor x = torch::full({3, 3}, 1.5, torch::TensorOptions().dtype(torch::kFloat));
    std::cout << x << std::endl;
    return 0;
}

This time, we will use "Makefile" to easily build the source code. Also, create "CMakeLists.txt" to create the "Makefile".

$ vi CMakeLists.txt

The simplest CMakeLists.txt is: ** Copy and save ** this.

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)

# Project Name
project(test CXX)

# Find Package
find_package(Torch REQUIRED)

# Create Executable File
add_executable(${PROJECT_NAME} main.cpp)
target_link_libraries(${PROJECT_NAME} ${TORCH_LIBRARIES})
set_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 17)

To briefly explain, the first line is "specify the version of cmake", the next is "specify the project name and C ++ compiler", the next is "specify the library to search", the next is "specify the file to compile", and the next is It means "specify the library" and finally "specify the version of the C ++ compiler".

Next, create a build directory and run cmake. Here, ** enter the path of the libtorch directory you downloaded earlier ** together. (If "~ / libtorch" is directly under your home directory)

$ mkdir build
$ cd build
$ cmake .. -DCMAKE_PREFIX_PATH=~/libtorch

This will create a Makefile, so run make to build it.

$ make

Finally, execute the executable file.

$ ./test

If the following is displayed, it is successful.

 1.5000  1.5000  1.5000
 1.5000  1.5000  1.5000
 1.5000  1.5000  1.5000
[ CPUFloatType{3,3} ]

** LibTorch's own environment construction is now complete </ font> **! After that, install the dependent library and set it so that the sample code can be executed.

$ sudo apt install libboost-dev libboost-all-dev

$ sudo apt install gnuplot

$ sudo apt install libpng++-dev

$ cd ~
$ git clone https://github.com/koba-jon/pytorch_cpp.git
$ cd pytorch_cpp

$ vi utils/CMakeLists.txt

3: # LibTorch
4: set(LIBTORCH_DIR $ENV{HOME}/libtorch)
5: list(APPEND CMAKE_PREFIX_PATH ${LIBTORCH_DIR})

$ cd AE2d
$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ cd ..

$ cd datasets
$ ln -s <dataset_path> ./celebA_org

$ sudo apt install python3 python3-pip
$ pip3 install natsort
$ sh hold_out.sh
$ mv ./celebA/valid ./celebA/test
$ cd ..

$ vi scripts/train.sh

#!/bin/bash

DATA='celebA'

./AE2d \
    --train true \
    --epochs 300 \ #Change to 1
    --dataset ${DATA} \
    --size 256 \ #Change to 64
    --batch_size 16 \
    --gpu_id 0 \
    --nc 3

$ sh scripts/train.sh

$ vi scripts/test.sh

#!/bin/bash

DATA='celebA'

./AE2d \
    --test true \
    --dataset ${DATA} \
    --test_in_dir "test" \
    --test_out_dir "test" \
    --size 256 \ #Change to 64
    --gpu_id 0 \
    --nc 3

$ sh scripts/test.sh