Linux on xgDebug的博客 https://xgdebug.com/zh/tags/linux/ Recent content in Linux on xgDebug的博客 xgDebug的博客 https://xgdebug.com/images/avatar.png https://xgdebug.com/images/avatar.png Hugo zh-cn Sun, 30 Nov 2025 01:39:49 +0000 OpenWrt双路由DHCPv6-PD前缀委派配置教程 https://xgdebug.com/zh/posts/tech/openwrt/openwrt-dual-router-dhcpv6-pd-prefix-delegation-tutorial/ Fri, 07 Nov 2025 11:40:50 +0000 https://xgdebug.com/zh/posts/tech/openwrt/openwrt-dual-router-dhcpv6-pd-prefix-delegation-tutorial/ 详解如何通过DHCPv6-PD在OpenWrt路由器级联环境下获取并分配IPv6公网前缀，包括WAN/LAN接口、RA/DHCPv6服务、防火墙与验证排错。 核心方案：配置OpenWRT_B通过DHCPv6-PD获取子前缀

核心关键是要给对应的接口分配一个合适的前缀长度，并确保DHCPv6和RA服务正确启用。,最小也要63,才能给下一级路由器分配64位子网

第一步：配置OpenWRT_B的WAN口（连接OpenWRT_A）

进入OpenWRT_B管理界面 → 网络 → 接口

  1. 编辑WAN接口（物理接口对应连接OpenWRT_A的网口）
  2. 协议：选择 “DHCPv6客户端” （如果原来是静态或禁用）
  3. 切换到"高级设置"标签页：
    • “请求IPv6前缀” ：勾选 ✓
    • “请求的IPv6前缀长度” ：选择 “自动” 或手动填写 60
  4. 切换到"物理设置"标签页：确认接口绑定正确（如eth0.2或lan口）
  5. 保存

第二步：配置OpenWRT_B的LAN口（连接你的电脑）

仍在网络 → 接口 → 编辑LAN接口

  1. 切换到"常规设置"标签页：

    • IPv6分配长度：选择 63 （关键！）

  2. 切换到"IPv6设置"标签页：

    • “IPv6 assignment hint” ：填写一个子网ID（如 1 或 2，避免与OpenWRT_A的LAN冲突）
    • “IPv6后缀” ：可留空或设置为 ::1
    • RA服务：选择 “服务器模式”
    • DHCPv6服务：选择 “服务器模式”
    • NDP代理：选择 “已禁用”
    • RA管理：选择 “已启用”
    • Always announce default router：勾选 ✓

  3. 保存并应用

第三步：检查DHCPv6服务器配置

进入 服务 → DHCP/DNS

  1. 切换到"高级设置"标签页：
    • 确保 “禁止解析IPv6 DNS记录” 未勾选
  2. 切换到"IPv6 RA设置"标签页：
    • RA Flags：勾选 managed 和 other
  3. 保存并应用

第四步：调整防火墙（关键）

进入 网络 → 防火墙

  1. 编辑"wan"区域：

    • 转发：选择 “接受” 或确保有规则允许转发到lan区域
    • 涵盖的网络：确认包含 wan 和 wan6

  2. 编辑"lan"区域：

    • 转发：选择 “接受”
    • 涵盖的网络：确认包含 lan

  3. 保存并应用

验证与排错

在OpenWRT_B上执行以下命令：

# 检查是否成功获取PD
ifstatus wan6

# 查看LAN口IPv6地址
ifconfig br-lan | grep inet6

# 查看路由表
ip -6 route

# 查看DHCPv6服务器状态
logread | grep dhcp6

成功标志：

  • ifstatus wan6 应显示获取的IPv6-PD（如 240e:39c:2bae:7001::/60）
  • LAN口应有公网IPv6地址（如 240e:39c:2bae:7001::1/64）
  • 路由表中有 240e:39c:2bae:7000::/56 的默认路由指向pppoe-wan

在电脑上验证：

# Windows
ipconfig /all | findstr IPv6

# Linux/macOS
ifconfig | grep inet6

# 测试连通性
ping -6 ipv6.google.com

常见问题与解决

问题 原因 解决方案
OpenWRT_B获取不到PD OpenWRT_A未正确分配PD前缀 在OpenWRT_A的WAN6接口高级设置中，将"IPv6前缀长度"改为60或56
电脑获取不到地址 RA/DHCPv6服务未启用或防火墙阻拦 检查LAN口的IPv6设置为"服务器模式"，并确认防火墙允许转发
能获取地址但无法上网 缺少默认路由或DNS 确保RA设置中"Always announce default router"已勾选，并检查DNS配置
IPv6地址冲突 子网ID与OpenWRT_A冲突 修改OpenWRT_B LAN的"IPv6 assignment hint"为其他值（1,2,3…）

推荐配置总结

OpenWRT_A（确保）：

  • WAN6接口 → 高级设置 → IPv6前缀长度：60 或 56
  • DHCP/DNS → 高级设置 → 勾选"动态DHCP"和"RA"

OpenWRT_B（关键）：

  • WAN接口：DHCPv6客户端 + 请求IPv6前缀
  • LAN接口：IPv6分配长度64 + RA/DHCPv6服务器模式
  • 防火墙：允许wan到lan的IPv6转发

配置完成后，重启OpenWRT_B的WAN接口或整个路由器，等待1-2分钟让DHCPv6完成前缀协商，你的电脑应该就能获取到公网IPv6地址了。

]]> OpenWRT配置IPV6中继 https://xgdebug.com/zh/posts/tech/openwrt/openwrt-ipv6-relay-configuration/ Sun, 26 Oct 2025 14:03:45 +0000 https://xgdebug.com/zh/posts/tech/openwrt/openwrt-ipv6-relay-configuration/ 为了跟进时代，本文介绍了如何在OpenWRT上配置二级路由的IPV6中继模式，使内网设备也能获取IPV6地址。 为了跟进一下时代，尝尝IPV6的鲜，我打算给我的二级内网搞出IPV6来

因为我是家网络有两级，一级是主路由，他可以获取到由运营商分配的IPV6和IPV6-PD，并且可以给接入他的设备分配一个公网IPV6；还有一级时我书房的，他只能自己获取到一个公网IPV6，给接入设备的却只有一个内网的IPV6

打开二级路由 OpenWRT设置–>接口–>WAN6–>DHCP服务器–>IPV6设置

把路由通告服务、DHCPv6 服务、NDP 代理全部设置为中继模式，并且勾上选项主

Designated master 打勾

Set this interface as master for RA and DHCPv6 relaying as well as NDP proxying.
RA-Service relay mode

Configures the operation mode of the RA service on this interface.
DHCPv6-Service relay mode

Configures the operation mode of the DHCPv6 service on this interface.
NDP-Proxy relay mode

然后打开OpenWRT设置–>接口–>LAN->DHCP服务器–>IPV6设置

把路由通告服务、DHCPv6 服务、NDP 代理全部设置为中继模式，注意此时不勾 Designated master

最后全部保存并应用，然后再次测试IPV6，发现IPV6已经可以正常获取到了

最终 uci

root@OpenWRT-MY:~# uci show dhcp.lan
dhcp.lan=dhcp
dhcp.lan.interface='lan'
dhcp.lan.start='100'
dhcp.lan.limit='150'
dhcp.lan.leasetime='12h'
dhcp.lan.dhcpv4='server'
dhcp.lan.ra='relay'
dhcp.lan.dhcpv6='relay'
dhcp.lan.ndp='relay'
root@OpenWRT-MY:~# uci show dhcp.wan6
dhcp.wan6=dhcp
dhcp.wan6.interface='wan6'
dhcp.wan6.ignore='1'
dhcp.wan6.master='1'
dhcp.wan6.ra='relay'
dhcp.wan6.dhcpv6='relay'
dhcp.wan6.ndp='relay'
]]> 使用 Deploy Key自动部署hugo博客到github https://xgdebug.com/zh/posts/tech/linux/deploy-hugo-blog-with-deploy-key/ Sun, 29 Sep 2024 10:08:49 +0800 https://xgdebug.com/zh/posts/tech/linux/deploy-hugo-blog-with-deploy-key/ <h2 id="1生成-ssh-密钥">1：生成 SSH 密钥</h2> <p>在本地终端生成一个新的 SSH 密钥对：</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"><span class="line"><span class="cl">ssh-keygen -t rsa -b <span class="m">4096</span> -C <span class="s2">&#34;your_email@example.com&#34;</span> </span></span></code></pre></div><p>执行后，它会要求你指定文件名，按回车即可使用默认路径（~/.ssh/id_rsa），然后你会得到两个文件：</p> 1：生成 SSH 密钥

在本地终端生成一个新的 SSH 密钥对：

ssh-keygen -t rsa -b 4096 -C "your_email@example.com"

执行后，它会要求你指定文件名，按回车即可使用默认路径（~/.ssh/id_rsa），然后你会得到两个文件：

id_rsa（私钥） id_rsa.pub（公钥）

2：在 GitHub Pages 仓库中添加 Deploy Key

打开你的 GitHub Pages 仓库 xgDebug/xgdebug.github.io。 进入 Settings -> Deploy keys。 点击 Add deploy key。 title = 取个描述性标题（如 “Hugo Blog Deployment Key”）。 Key: 将上一步生成的公钥 id_rsa.pub 的内容复制粘贴进去。 勾选 Allow write access，因为需要写权限。 点击 Add key。

3：将私钥添加到 GitHub Secrets

在你的 Hugo 仓库 xgDebug/xgDebug_blog 中，进入 Settings -> Secrets -> Actions。 点击 New repository secret，创建新的密钥： Name: DEPLOY_KEY Value: 将生成的私钥 id_rsa 的内容粘贴进去。

4：更新 GitHub Actions 配置

在 .github/workflows/gh-pages.yml 中使用 DEPLOY_KEY 进行身份验证：

name: Deploy Hugo Site to GitHub Pages

on:
push:
branches: - master

jobs:
deploy:
runs-on: ubuntu-latest
steps: - name: Checkout repository
uses: actions/checkout@v3

    - name: Set up Hugo
      uses: peaceiris/actions-hugo@v2
      with:
        hugo-version: 'latest'

    - name: Install Hugo themes
      run: git submodule update --init --recursive

    - name: Build Hugo site
      run: hugo --minify

    - name: Deploy to GitHub Pages
      uses: peaceiris/actions-gh-pages@v3
      with:
        deploy_key: ${{ secrets.DEPLOY_KEY }}
        publish_dir: ./public
        external_repository: xgDebug/xgdebug.github.io
        publish_branch: gh-pages
]]> 使用ncnn布署pytorch模型到Android手机 https://xgdebug.com/zh/posts/tech/ai/deploy-pytorch-model-to-android-with-ncnn/ Sat, 28 Sep 2024 05:08:49 +0000 https://xgdebug.com/zh/posts/tech/ai/deploy-pytorch-model-to-android-with-ncnn/ 本文档介绍了如何使用 NCNN 部署 PyTorch 模型到 Android 手机，包括编译 NCNN、训练 YOLO、转换模型以及在不同平台上构建 NCNN 的详细步骤，涵盖了 Linux、Windows、macOS、ARM、Hisilicon、Android、iOS 和 WebAssembly 等多种平台。 使用 ncnn 布署 pytorch 模型到 Android 手机
  1. 编译 NCNN 时要打开显卡支持 vulkan 是针对 gpu 的 -DNCNN_VULKAN=ON
  2. MobileNetV3

編譯成 MT 時要打開 CMAKE 0091 特性

cmake_minimum_required(VERSION 3.20.0)
cmake_policy(SET CMP0091 NEW)
set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded$<$<CONFIG:Debug>:Debug>")
project("client-project")

训练 YOLO

\Envs\torch\Scripts\activate.ps1
python train.py --batch 6 --workers 2 --imgsz 960 --epochs 300 --data "\Core\yaml\data.yaml" --cfg "\Core\yaml\cfg.yaml" --weights \Core\weights\best.pt --device 0

转换模型

from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.onnx
from libs import define
from libs.net import Net
from libs.dataset import ImageDataset
import os

test_data = ImageDataset(define.testPath,False)
test_loader = torch.utils.data.DataLoader( test_data, batch_size=1, shuffle=True)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net(out_dim=19).to(device)
model.load_state_dict(torch.load( "./widget/last.pt" ))
model.eval()

def saveOnnx():
    for data, target in test_loader:
        data, target = data.to(device), target.to(device)
        label = target.long()
        y = model(data)
        # Export the model
        torch.onnx.export(model,                   # model being run
                        data,                      # model input (or a tuple for multiple inputs)
                        "./widget/best.onnx",            # where to save the model (can be a file or file-like object)
                        export_params=True,        # store the trained parameter weights inside the model file
                        opset_version=10,          # the ONNX version to export the model to
                        do_constant_folding=True,  # whether to execute constant folding for optimization
                        input_names = ['input'],   # the model's input names
                        output_names = ['output'],  # the model's output names
                        dynamic_axes={'input' : {0 : 'batch_size'},    # variable lenght axes
                                        'output' : {0 : 'batch_size'}})

        traced_script_module = torch.jit.trace(model, data)
        return

saveOnnx()
# 转换
os.system("python -m onnxsim ./widget/best.onnx ./widgetbest-sim.onnx")
os.system("./bin/onnx2ncnn.exe ./widget/best-sim.onnx ./widget/best.param ./widget/best.bin")
os.system("./bin/ncnnoptimize.exe ./widget/best.param ./widget/best.bin ./widget/best-opt.param ./widget/best-opt.bin 65536")

python .\export.py --weights weights/best.pt --img 960 --batch 1 --train
python -m onnxsim best.onnx best-sim.onnx
.\onnx2ncnn.exe best-sim.onnx best.param best.bin
ncnnoptimize best.param best.bin best-opt.param best-opt.bin 65536

Git clone ncnn repo with submodule

$ git clone https://github.com/Tencent/ncnn.git
$ cd ncnn
$ git submodule update --init

Build for Linux

Install required build dependencies:

  • git
  • g++
  • cmake
  • protocol buffer (protobuf) headers files and protobuf compiler
  • vulkan header files and loader library
  • glslang
  • (optional) opencv # For building examples

Generally if you have Intel, AMD or Nvidia GPU from last 10 years, Vulkan can be easily used.

On some systems there are no Vulkan drivers easily available at the moment (October 2020), so you might need to disable use of Vulkan on them. This applies to Raspberry Pi 3 (but there is experimental open source Vulkan driver in the works, which is not ready yet). Nvidia Tegra series devices (like Nvidia Jetson) should support Vulkan. Ensure you have most recent software installed for best expirience.

On Debian, Ubuntu or Raspberry Pi OS, you can install all required dependencies using:

sudo apt install build-essential git cmake libprotobuf-dev protobuf-compiler libvulkan-dev vulkan-utils libopencv-dev

To use Vulkan backend install Vulkan header files, a vulkan driver loader, GLSL to SPIR-V compiler and vulkaninfo tool. Preferably from your distribution repositories. Alternatively download and install full Vulkan SDK (about 200MB in size; it contains all header files, documentation and prebuilt loader, as well some extra tools and source code of everything) from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/linux/vulkansdk-linux-x86_64-1.2.189.0.tar.gz?Human=true -O vulkansdk-linux-x86_64-1.2.189.0.tar.gz
tar -xf vulkansdk-linux-x86_64-1.2.189.0.tar.gz
export VULKAN_SDK=$(pwd)/1.2.189.0/x86_64

To use Vulkan after building ncnn later, you will also need to have Vulkan driver for your GPU. For AMD and Intel GPUs these can be found in Mesa graphics driver, which usually is installed by default on all distros (i.e. sudo apt install mesa-vulkan-drivers on Debian/Ubuntu). For Nvidia GPUs the proprietary Nvidia driver must be downloaded and installed (some distros will allow easier installation in some way). After installing Vulkan driver, confirm Vulkan libraries and driver are working, by using vulkaninfo or vulkaninfo | grep deviceType, it should list GPU device type. If there are more than one GPU installed (including the case of integrated GPU and discrete GPU, commonly found in laptops), you might need to note the order of devices to use later on.

Nvidia Jetson devices the Vulkan support should be present in Nvidia provided SDK (Jetpack) or prebuild OS images.

Raspberry Pi Vulkan drivers do exists, but are not mature. You are free to experiment at your own discretion, and report results and performance.

cd ncnn
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON ..
make -j$(nproc)

You can add -GNinja to cmake above to use Ninja build system (invoke build using ninja or cmake --build .).

For Nvidia Jetson devices, add -DCMAKE_TOOLCHAIN_FILE=../toolchains/jetson.toolchain.cmake to cmake.

For Rasberry Pi 3, add -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake -DPI3=ON to cmake. You can also consider disabling Vulkan support as the Vulkan drivers for Rasberry Pi are still not mature, but it doesn’t hurt to build the support in, but not use it.

Verify build by running some examples:

cd ../examples
../build/examples/squeezenet ../images/256-ncnn.png
[0 AMD RADV FIJI (LLVM 10.0.1)]  queueC=1[4]  queueG=0[1]  queueT=0[1]
[0 AMD RADV FIJI (LLVM 10.0.1)]  bugsbn1=0  buglbia=0  bugcopc=0  bugihfa=0
[0 AMD RADV FIJI (LLVM 10.0.1)]  fp16p=1  fp16s=1  fp16a=0  int8s=1  int8a=1
532 = 0.163452
920 = 0.093140
716 = 0.061584

You can also run benchmarks (the 4th argument is a GPU device index to use, refer to vulkaninfo, if you have more than one GPU):

cd ../benchmark
../build/benchmark/benchncnn 10 $(nproc) 0 0
[0 AMD RADV FIJI (LLVM 10.0.1)]  queueC=1[4]  queueG=0[1]  queueT=0[1]
[0 AMD RADV FIJI (LLVM 10.0.1)]  bugsbn1=0  buglbia=0  bugcopc=0  bugihfa=0
[0 AMD RADV FIJI (LLVM 10.0.1)]  fp16p=1  fp16s=1  fp16a=0  int8s=1  int8a=1
num_threads = 4
powersave = 0
gpu_device = 0
cooling_down = 1
          squeezenet  min =    4.68  max =    4.99  avg =    4.85
     squeezenet_int8  min =   38.52  max =   66.90  avg =   48.52
...

To run benchmarks on a CPU, set the 5th argument to -1.

Build for Windows x64 using Visual Studio Community 2017

Download and Install Visual Studio Community 2017 from https://visualstudio.microsoft.com/vs/community/

Start the command prompt: Start → Programs → Visual Studio 2017 → Visual Studio Tools → x64 Native Tools Command Prompt for VS 2017

Download protobuf-3.4.0 from https://github.com/google/protobuf/archive/v3.4.0.zip

Build protobuf library:

cd <protobuf-root-dir>
mkdir build
cd build
cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -Dprotobuf_BUILD_TESTS=OFF -Dprotobuf_MSVC_STATIC_RUNTIME=OFF ../cmake
nmake
nmake install

(optional) Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Build ncnn library (replace protobuf-root-dir with a proper path):

cd <ncnn-root-dir>
mkdir -p build
cd build
cmake -G"NMake Makefiles" -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=%cd%/install -DProtobuf_INCLUDE_DIR=<protobuf-root-dir>/build/install/include -DProtobuf_LIBRARIES=<protobuf-root-dir>/build/install/lib/libprotobuf.lib -DProtobuf_PROTOC_EXECUTABLE=<protobuf-root-dir>/build/install/bin/protoc.exe -DNCNN_VULKAN=ON ..
nmake
nmake install

Note: To speed up compilation process on multi core machines, configuring cmake to use jom or ninja using -G flag is recommended.

Build for macOS

First install Xcode or Xcode Command Line Tools according to your needs.

Then install protobuf and libomp via homebrew

brew install protobuf libomp

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/mac/vulkansdk-macos-1.2.189.0.dmg?Human=true -O vulkansdk-macos-1.2.189.0.dmg
hdiutil attach vulkansdk-macos-1.2.189.0.dmg
sudo /Volumes/vulkansdk-macos-1.2.189.0/InstallVulkan.app/Contents/MacOS/InstallVulkan --root `pwd`/vulkansdk-macos-1.2.189.0 --accept-licenses --default-answer --confirm-command install
hdiutil detach /Volumes/vulkansdk-macos-1.2.189.0

# setup env
export VULKAN_SDK=`pwd`/vulkansdk-macos-1.2.189.0/macOS

cd <ncnn-root-dir>
mkdir -p build
cd build

cmake -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64" \
    -DVulkan_INCLUDE_DIR=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/include \
    -DVulkan_LIBRARY=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/dylib/macOS/libMoltenVK.dylib \
    -DNCNN_VULKAN=ON -DNCNN_BUILD_EXAMPLES=ON ..

cmake --build . -j 4
cmake --build . --target install

Note: If you encounter libomp related errors during installation, you can also check our GitHub Actions at here to install and use openmp.

Build for ARM Cortex-A family with cross-compiling

Download ARM toolchain from https://developer.arm.com/open-source/gnu-toolchain/gnu-a/downloads

export PATH="<your-toolchain-compiler-path>:${PATH}"

Alternatively install a cross-compiler provided by the distribution (i.e. on Debian / Ubuntu, you can do sudo apt install g++-arm-linux-gnueabi g++-arm-linux-gnueabihf g++-aarch64-linux-gnu).

Depending on your needs build one or more of the below targets.

AArch32 target with soft float (arm-linux-gnueabi)

cd <ncnn-root-dir>
mkdir -p build-arm-linux-gnueabi
cd build-arm-linux-gnueabi
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/arm-linux-gnueabi.toolchain.cmake ..
make -j$(nproc)

AArch32 target with hard float (arm-linux-gnueabihf)

cd <ncnn-root-dir>
mkdir -p build-arm-linux-gnueabihf
cd build-arm-linux-gnueabihf
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/arm-linux-gnueabihf.toolchain.cmake ..
make -j$(nproc)

AArch64 GNU/Linux target (aarch64-linux-gnu)

cd <ncnn-root-dir>
mkdir -p build-aarch64-linux-gnu
cd build-aarch64-linux-gnu
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake ..
make -j$(nproc)

Build for Hisilicon platform with cross-compiling

Download and install Hisilicon SDK. The toolchain should be in /opt/hisi-linux/x86-arm

cd <ncnn-root-dir>
mkdir -p build
cd build

# Choose one cmake toolchain file depends on your target platform
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/hisiv300.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/hisiv500.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/himix100.toolchain.cmake ..
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/himix200.toolchain.cmake ..

make -j$(nproc)
make install

Build for Android

You can use the pre-build ncnn-android-lib.zip from https://github.com/Tencent/ncnn/releases

Download Android NDK from http://developer.android.com/ndk/downloads/index.html and install it, for example:

unzip android-ndk-r21d-linux-x86_64.zip
export ANDROID_NDK=<your-ndk-root-path>

(optional) remove the hardcoded debug flag in Android NDK android-ndk issue

# open $ANDROID_NDK/build/cmake/android.toolchain.cmake
# delete "-g" line
list(APPEND ANDROID_COMPILER_FLAGS
  -g
  -DANDROID

Build armv7 library

cd <ncnn-root-dir>
mkdir -p build-android-armv7
cd build-android-armv7

cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
    -DANDROID_ABI="armeabi-v7a" -DANDROID_ARM_NEON=ON \
    -DANDROID_PLATFORM=android-14 ..

# If you want to enable Vulkan, platform api version >= android-24 is needed
cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
  -DANDROID_ABI="armeabi-v7a" -DANDROID_ARM_NEON=ON \
  -DANDROID_PLATFORM=android-24 -DNCNN_VULKAN=ON ..

make -j$(nproc)
make install

Pick build-android-armv7/install folder for further JNI usage.

Build aarch64 library:

cd <ncnn-root-dir>
mkdir -p build-android-aarch64
cd build-android-aarch64

cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake"\
  -DANDROID_ABI="arm64-v8a" \
  -DANDROID_PLATFORM=android-21 ..

# If you want to enable Vulkan, platform api version >= android-24 is needed
cmake -DCMAKE_TOOLCHAIN_FILE="$ANDROID_NDK/build/cmake/android.toolchain.cmake" \
  -DANDROID_ABI="arm64-v8a" \
  -DANDROID_PLATFORM=android-24 -DNCNN_VULKAN=ON ..

make -j$(nproc)
make install

Pick build-android-aarch64/install folder for further JNI usage.

Build for iOS on macOS with xcode

You can use the pre-build ncnn.framework glslang.framework and openmp.framework from https://github.com/Tencent/ncnn/releases

Install xcode

You can replace -DENABLE_BITCODE=0 to -DENABLE_BITCODE=1 in the following cmake arguments if you want to build bitcode enabled libraries.

Download and install openmp for multithreading inference feature on iPhoneOS

wget https://github.com/llvm/llvm-project/releases/download/llvmorg-11.0.0/openmp-11.0.0.src.tar.xz
tar -xf openmp-11.0.0.src.tar.xz
cd openmp-11.0.0.src

# apply some compilation fix
sed -i'' -e '/.size __kmp_unnamed_critical_addr/d' runtime/src/z_Linux_asm.S
sed -i'' -e 's/__kmp_unnamed_critical_addr/___kmp_unnamed_critical_addr/g' runtime/src/z_Linux_asm.S

mkdir -p build-ios
cd build-ios

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install \
    -DIOS_PLATFORM=OS -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 -DIOS_ARCH="armv7;arm64;arm64e" \
    -DPERL_EXECUTABLE=/usr/local/bin/perl \
    -DLIBOMP_ENABLE_SHARED=OFF -DLIBOMP_OMPT_SUPPORT=OFF -DLIBOMP_USE_HWLOC=OFF ..

cmake --build . -j 4
cmake --build . --target install

# copy openmp library and header files to xcode toolchain sysroot
sudo cp install/include/* /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/include
sudo cp install/lib/libomp.a /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib

Download and install openmp for multithreading inference feature on iPhoneSimulator

wget https://github.com/llvm/llvm-project/releases/download/llvmorg-11.0.0/openmp-11.0.0.src.tar.xz
tar -xf openmp-11.0.0.src.tar.xz
cd openmp-11.0.0.src

# apply some compilation fix
sed -i'' -e '/.size __kmp_unnamed_critical_addr/d' runtime/src/z_Linux_asm.S
sed -i'' -e 's/__kmp_unnamed_critical_addr/___kmp_unnamed_critical_addr/g' runtime/src/z_Linux_asm.S

mkdir -p build-ios-sim
cd build-ios-sim

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install \
    -DIOS_PLATFORM=SIMULATOR -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 -DIOS_ARCH="i386;x86_64" \
    -DPERL_EXECUTABLE=/usr/local/bin/perl \
    -DLIBOMP_ENABLE_SHARED=OFF -DLIBOMP_OMPT_SUPPORT=OFF -DLIBOMP_USE_HWLOC=OFF ..

cmake --build . -j 4
cmake --build . --target install

# copy openmp library and header files to xcode toolchain sysroot
sudo cp install/include/* /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/include
sudo cp install/lib/libomp.a /Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/lib

Package openmp framework:

cd <openmp-root-dir>

mkdir -p openmp.framework/Versions/A/Headers
mkdir -p openmp.framework/Versions/A/Resources
ln -s A openmp.framework/Versions/Current
ln -s Versions/Current/Headers openmp.framework/Headers
ln -s Versions/Current/Resources openmp.framework/Resources
ln -s Versions/Current/openmp openmp.framework/openmp
lipo -create build-ios/install/lib/libomp.a build-ios-sim/install/lib/libomp.a -o openmp.framework/Versions/A/openmp
cp -r build-ios/install/include/* openmp.framework/Versions/A/Headers/
sed -e 's/__NAME__/openmp/g' -e 's/__IDENTIFIER__/org.llvm.openmp/g' -e 's/__VERSION__/11.0/g' Info.plist > openmp.framework/Versions/A/Resources/Info.plist

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

wget https://sdk.lunarg.com/sdk/download/1.2.189.0/mac/vulkansdk-macos-1.2.189.0.dmg?Human=true -O vulkansdk-macos-1.2.189.0.dmg
hdiutil attach vulkansdk-macos-1.2.189.0.dmg
sudo /Volumes/vulkansdk-macos-1.2.189.0/InstallVulkan.app/Contents/MacOS/InstallVulkan --root `pwd`/vulkansdk-macos-1.2.189.0 --accept-licenses --default-answer --confirm-command install
hdiutil detach /Volumes/vulkansdk-macos-1.2.189.0

# setup env
export VULKAN_SDK=`pwd`/vulkansdk-macos-1.2.189.0/macOS

Build library for iPhoneOS:

cd <ncnn-root-dir>
mkdir -p build-ios
cd build-ios

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=OS -DIOS_ARCH="armv7;arm64;arm64e" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib/libomp.a" \
    -DNCNN_BUILD_BENCHMARK=OFF ..

# vulkan is only available on arm64 devices
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=OS64 -DIOS_ARCH="arm64;arm64e" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS.sdk/usr/lib/libomp.a" \
    -DVulkan_INCLUDE_DIR=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/include \
    -DVulkan_LIBRARY=`pwd`/../vulkansdk-macos-1.2.189.0/MoltenVK/dylib/iOS/libMoltenVK.dylib \
    -DNCNN_VULKAN=ON -DNCNN_BUILD_BENCHMARK=OFF ..

cmake --build . -j 4
cmake --build . --target install

Build library for iPhoneSimulator:

cd <ncnn-root-dir>
mkdir -p build-ios-sim
cd build-ios-sim

cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/ios.toolchain.cmake -DIOS_PLATFORM=SIMULATOR -DIOS_ARCH="i386;x86_64" \
    -DENABLE_BITCODE=0 -DENABLE_ARC=0 -DENABLE_VISIBILITY=0 \
    -DOpenMP_C_FLAGS="-Xclang -fopenmp" -DOpenMP_CXX_FLAGS="-Xclang -fopenmp" \
    -DOpenMP_C_LIB_NAMES="libomp" -DOpenMP_CXX_LIB_NAMES="libomp" \
    -DOpenMP_libomp_LIBRARY="/Applications/Xcode.app/Contents/Developer/Platforms/iPhoneSimulator.platform/Developer/SDKs/iPhoneSimulator.sdk/usr/lib/libomp.a" \
    -DNCNN_BUILD_BENCHMARK=OFF ..

cmake --build . -j 4
cmake --build . --target install

Package glslang framework:

cd <ncnn-root-dir>

mkdir -p glslang.framework/Versions/A/Headers
mkdir -p glslang.framework/Versions/A/Resources
ln -s A glslang.framework/Versions/Current
ln -s Versions/Current/Headers glslang.framework/Headers
ln -s Versions/Current/Resources glslang.framework/Resources
ln -s Versions/Current/glslang glslang.framework/glslang
libtool -static build-ios/install/lib/libglslang.a build-ios/install/lib/libSPIRV.a build-ios/install/lib/libOGLCompiler.a build-ios/install/lib/libOSDependent.a -o build-ios/install/lib/libglslang_combined.a
libtool -static build-ios-sim/install/lib/libglslang.a build-ios-sim/install/lib/libSPIRV.a build-ios-sim/install/lib/libOGLCompiler.a build-ios-sim/install/lib/libOSDependent.a -o build-ios-sim/install/lib/libglslang_combined.a
lipo -create build-ios/install/lib/libglslang_combined.a build-ios-sim/install/lib/libglslang_combined.a -o glslang.framework/Versions/A/glslang
cp -r build/install/include/glslang glslang.framework/Versions/A/Headers/
sed -e 's/__NAME__/glslang/g' -e 's/__IDENTIFIER__/org.khronos.glslang/g' -e 's/__VERSION__/1.0/g' Info.plist > glslang.framework/Versions/A/Resources/Info.plist

Package ncnn framework:

cd <ncnn-root-dir>

mkdir -p ncnn.framework/Versions/A/Headers
mkdir -p ncnn.framework/Versions/A/Resources
ln -s A ncnn.framework/Versions/Current
ln -s Versions/Current/Headers ncnn.framework/Headers
ln -s Versions/Current/Resources ncnn.framework/Resources
ln -s Versions/Current/ncnn ncnn.framework/ncnn
lipo -create build-ios/install/lib/libncnn.a build-ios-sim/install/lib/libncnn.a -o ncnn.framework/Versions/A/ncnn
cp -r build-ios/install/include/* ncnn.framework/Versions/A/Headers/
sed -e 's/__NAME__/ncnn/g' -e 's/__IDENTIFIER__/com.tencent.ncnn/g' -e 's/__VERSION__/1.0/g' Info.plist > ncnn.framework/Versions/A/Resources/Info.plist

Pick ncnn.framework glslang.framework and openmp.framework folder for app development.

Build for WebAssembly

Install Emscripten

git clone https://github.com/emscripten-core/emsdk.git
cd emsdk
./emsdk install 2.0.8
./emsdk activate 2.0.8

source emsdk/emsdk_env.sh

Build without any extension for general compatibility:

mkdir -p build
cd build
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=OFF -DNCNN_OPENMP=OFF -DNCNN_SIMPLEOMP=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=OFF -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM SIMD extension:

mkdir -p build-simd
cd build-simd
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=OFF -DNCNN_OPENMP=OFF -DNCNN_SIMPLEOMP=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=ON -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM Thread extension:

mkdir -p build-threads
cd build-threads
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=ON -DNCNN_OPENMP=ON -DNCNN_SIMPLEOMP=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=OFF -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Build with WASM SIMD and Thread extension:

mkdir -p build-simd-threads
cd build-simd-threads
cmake -DCMAKE_TOOLCHAIN_FILE=../emsdk/upstream/emscripten/cmake/Modules/Platform/Emscripten.cmake \
    -DNCNN_THREADS=ON -DNCNN_OPENMP=ON -DNCNN_SIMPLEOMP=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_SSE2=ON -DNCNN_AVX2=OFF -DNCNN_AVX=OFF \
    -DNCNN_BUILD_TOOLS=OFF -DNCNN_BUILD_EXAMPLES=OFF -DNCNN_BUILD_BENCHMARK=OFF ..
cmake --build . -j 4
cmake --build . --target install

Pick build-XYZ/install folder for further usage.

Build for AllWinner D1

Download c906 toolchain package from https://occ.t-head.cn/community/download?id=3913221581316624384

tar -xf riscv64-linux-x86_64-20210512.tar.gz
export RISCV_ROOT_PATH=/home/nihui/osd/riscv64-linux-x86_64-20210512

Build ncnn with riscv-v vector and simpleocv enabled:

mkdir -p build-c906
cd build-c906
cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/c906.toolchain.cmake \
    -DCMAKE_BUILD_TYPE=relwithdebinfo -DNCNN_OPENMP=OFF -DNCNN_THREADS=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_RVV=ON \
    -DNCNN_SIMPLEOCV=ON -DNCNN_BUILD_EXAMPLES=ON ..
cmake --build . -j 4
cmake --build . --target install

Pick build-c906/install folder for further usage.

You can upload binary inside build-c906/examples folder and run on D1 board for testing.

Build for Loongson 2K1000

For gcc version < 8.5, you need to fix msa.h header for workaround msa fmadd bug.

Open /usr/lib/gcc/mips64el-linux-gnuabi64/8/include/msa.h, find __msa_fmadd_w and apply changes as the following

// #define __msa_fmadd_w __builtin_msa_fmadd_w
#define __msa_fmadd_w(a, b, c) __builtin_msa_fmadd_w(c, b, a)

Build ncnn with mips msa and simpleocv enabled:

mkdir -p build
cd build
cmake -DNCNN_DISABLE_RTTI=ON -DNCNN_DISABLE_EXCEPTION=ON -DNCNN_RUNTIME_CPU=OFF -DNCNN_MSA=ON -DNCNN_MMI=ON -DNCNN_SIMPLEOCV=ON ..
cmake --build . -j 2
cmake --build . --target install

Pick build/install folder for further usage.

You can run binary inside build/examples folder for testing.

Build for Termux on Android

Install app Termux on your phone,and install Ubuntu in Termux.

If you want use ssh, just install openssh in Termux

pkg install proot-distro
proot-distro install ubuntu

or you can see what system can be installed using proot-distro list

while you install ubuntu successfully, using proot-distro login ubuntu to login Ubuntu.

Then make ncnn,no need to install any other dependencies.

git clone https://github.com/Tencent/ncnn.git
cd ncnn
git submodule update --init
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_BUILD_EXAMPLES=ON -DNCNN_PLATFORM_API=OFF -DNCNN_SIMPLEOCV=ON ..
make -j$(nproc)

Then you can run a test

on my Pixel 3 XL using Qualcomm 845,cant load 256-ncnn.png

cd ../examples
../build/examples/squeezenet ../images/128-ncnn.png
]]> Arch安装stable-diffusion-webui中遇到的一些坑 https://xgdebug.com/zh/posts/tech/linux/arch-sd-webui-pitfalls/ Tue, 12 Sep 2023 15:21:53 +0000 https://xgdebug.com/zh/posts/tech/linux/arch-sd-webui-pitfalls/ <p>1. 不要使用清华的源，要用阿里的，因为清华的源不全<br> 2. 要使用 python launch.py 来安装一些 git 库<br> 3. 要安装 requirements_versions.txt 带版本的 pip 库<br> 4. 可以使用 python webui.py &ndash;port=7860 &ndash;server=0.0.0.0 &ndash;medvram 节省显存</p> 1. 不要使用清华的源，要用阿里的，因为清华的源不全
2. 要使用 python launch.py 来安装一些 git 库
3. 要安装 requirements_versions.txt 带版本的 pip 库
4. 可以使用 python webui.py –port=7860 –server=0.0.0.0 –medvram 节省显存

]]>