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    2022年7月版——在“哪吒”上跑AI 全志D1 ncnn框架移植笔记

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    • Y
      giao LV 5 last edited by q1215200171

      ncnn框架是当前边缘计算的主流框架,关于全志D1运行ncnn框架的笔记,nihui大佬曾经于2021年发表了相关的帖子。但是目前工具链和ncnn版本有所变化,因此在大佬的基础上将,完整过程记录如下。

      相关链接:
      1.https://bbs.aw-ol.com/topic/705/
      2.https://zhuanlan.zhihu.com/p/386312071?utm_source=qq&utm_medium=social&utm_oi=872955404320141312

      1 准备交叉编译工具链

      目前使用的交叉工具链是v2.2.6版本,具体下载链接见:https://occ.t-head.cn/community/download?id=4046947553902661632

      下载这个文件:Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz

      tar -xf Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
      export RISCV_ROOT_PATH=/home/{你的用户名}/Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6
      

      2 下载和编译ncnn

      这边的toolchain file选用c906-v226.toolchain.cmake,其余的不用修改,在toolchain file里面都已经改好了。其中可能有路径问题,根据ncnn文件夹,实际所在的路径修改即可。

      git clone https://github.com/Tencent/ncnn.git
      cd ncnn
      mkdir build-c906
      cd build-c906
      cmake -DCMAKE_TOOLCHAIN_FILE=../toolchains/c906-v226.toolchain.cmake -DCMAKE_BUILD_TYPE=release -DNCNN_OPENMP=OFF -DNCNN_THREADS=OFF -DNCNN_RUNTIME_CPU=OFF -DNCNN_RVV=ON -DNCNN_SIMPLEOCV=ON -DNCNN_BUILD_EXAMPLES=ON ..
      make -j4
      

      3 测试benchncnn

      目前2.0版本的TinaLinux 执行 ncnn 程序已经不会有问题

      将 ncnn/build-c906/benchmark/benchncnn 和 ncnn/benchmark/*.param 拷贝到 d1开发板上,路径结构如下:

      80abd156fc86ba2c318912d87e220577.png

      chmod 0777 benchncnn 
      ./benchncnn 4 1 0 -1 0
      
      oot@TinaLinux:~/ncnn# ./benchncnn 4 1 0 -1 0
      syscall error -1
      loop_count = 4
      num_threads = 1
      powersave = 0
      gpu_device = -1
      cooling_down = 0
      fopen squeezenet.param failed
      network graph not ready
      Segmentation fault
      root@TinaLinux:~/ncnn# ./benchncnn 4 1 0 -1 0
      syscall error -1
      loop_count = 4
      num_threads = 1
      powersave = 0
      gpu_device = -1
      cooling_down = 0
                squeezenet  min =  382.77  max =  385.92  avg =  384.66
           squeezenet_int8  min = 8975.18  max = 8979.84  avg = 8977.72
                 mobilenet  min =  702.76  max =  704.76  avg =  703.74
            mobilenet_int8  min = 29501.19  max = 29546.07  avg = 29514.59
              mobilenet_v2  min =  451.34  max =  452.27  avg =  451.78
              mobilenet_v3  min =  358.12  max =  359.24  avg =  358.42
                shufflenet  min =  527.04  max =  528.48  avg =  527.69
             shufflenet_v2  min =  310.96  max =  312.30  avg =  311.59
                   mnasnet  min =  435.13  max =  437.02  avg =  435.94
           proxylessnasnet  min =  473.05  max =  476.37  avg =  475.19
           efficientnet_b0  min =  601.75  max =  610.41  avg =  606.18
         efficientnetv2_b0  min =  859.37  max =  861.75  avg =  860.67
              regnety_400m  min =  653.71  max =  654.24  avg =  653.88
                 blazeface  min =  154.08  max =  154.67  avg =  154.38
                 googlenet  min = 1404.80  max = 1409.50  avg = 1407.33
            googlenet_int8  min = 42586.56  max = 42608.04  avg = 42597.36
                  resnet18  min = 1047.81  max = 1050.59  avg = 1049.40
             resnet18_int8  min = 45011.41  max = 45185.10  avg = 45098.12
                   alexnet  min =  955.34  max =  956.98  avg =  956.06
                     vgg16  min = 5355.40  max = 5356.05  avg = 5355.75
      

      如果一切运行正常,则会出现上述信息。

      4 测试example

      将 ncnn/build-c906/examples/nanodet 和测试图片拷贝到 d1开发板上

      从这里下载 nanodet 模型文件并拷贝到 d1开发板上

      nihui/ncnn-assets​github.com/nihui/ncnn-assets/tree/master/models

      目录结构如下:

      35b2dd1eb5e8a25b37f92325da1c0b16.png

      注意,进过测试,jpg格式的图像读取时会报错,png格式没有问题

      ./nanodet test.png
      

      bd0e8ab3ee50032c1a82e873a5aa8576.png
      177b58a5b1e4b24788974154c648b8b8.png

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      • xiaowenge
        DOT小文哥 LV 7 last edited by

        @yanmingjian 在 2022年7月版——在“哪吒”上跑AI 全志D1 ncnn框架移植笔记 中说:

        交叉工具链

        跟之前版本的不同点:交叉边缘工具链升级?(v2.2.6)
        能测到性能上有提升吗?

        Y 1 Reply Last reply Reply Quote Share 0
        • Y
          giao LV 5 @xiaowenge last edited by

          @xiaowenge 有提升的;

          2.2.2 的性能数据在这里:https://bbs.aw-ol.com/topic/705

          2.2.6和 2.2.2相比,各模型性能均有一定程度提升

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