• 基于BM1684移植YOLOV7

    一、背景

    为了方便后续客户在BM1684平台使用YOLOV7，这里基于官方YOLOV7原生模型进行适配。

    1. 官方仓库：https://github.com/WongKinYiu/yolov7
    2. 模型地址：https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt

    二、环境

    BM1684环境则基于官方BMNNSDK2开发手册，环境搭建可以参考我另外一篇总结-BMNNSDK2实战记录，这里直接基于该环境进行，环境大致配置如下，采用官方docker：

    1. docker环境：bmnnsdk2-bm1684-ubuntu-docker-py37
    2. BMNNSDK2包：bmnnsdk2_bm1684_v2.7.0_20220531patched.zip

    三、迁移

    3.1 模型迁移

    创建如下目录结构：

    1. #目录结构
    2. YOLOv7_object/
    3. `-- model
    4.     |-- download_yolov7_model.sh     #下载原始模型
    5.     |-- gen_bmodel.sh                #生成fp32 bmodel
    6.     |-- gen_umodel_int8bmodel.sh     #生成int8 bmodel
    7.     |-- out                          #bmode等输出目录
    8.     |   `-- YOLOv7
    9.     `-- yolov7.pt                    #原生模型

    3.1.1 生成fp32 bmodel

    3.1.1.1 脚本实现

    采用bmnetp工具，脚本实现如下：

    1. #!/bin/bash
    2.  
    3. model_dir=$(dirname $(readlink -f "$0"))
    4. echo "model path: ${model_dir}"
    5. top_dir=$model_dir/../../..
    6. sdk_dir=$top_dir
    7.  
    8. export LD_LIBRARY_PATH=${sdk_dir}/lib/bmcompiler:${sdk_dir}/lib/bmlang:${sdk_dir}/lib/thirdparty/x86:${sdk_dir}/lib/bmnn/cmodel
    9. export PATH=$PATH:${sdk_dir}/bmnet/bmnetp
    10.  
    11. #generate output directory
    12. mkdir -p out/YOLOv7
    13.  
    14. # python 
    15. echo "start model transform......"
    16. python3 -m bmnetp \
    17.        --net_name=yolov7 \
    18.        --target=BM1684 \
    19.        --opt=1 \
    20.        --cmp=true \
    21.        --shapes="[1,3,640,640]" \
    22.        --model="${model_dir}/yolov7.pt" \
    23.        --outdir=output/YOLOv7 \
    24.        --dyn=false
    25. if [ $? -eq 0 ]; then
    26.     echo "Congratulation! Everything is OK!"
    27. else
    28.     echo "Something is wrong, pleae have a check!"
    29.     exit -1
    30. fi

    运行脚本报错如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# ./gen_bmodel.sh
    2. model path: /workspace/examples/YOLOv7_object/model
    3. start model transform......
    4. Namespace(cmp=True, desc=None, descs=None, dyn=False, enable_profile=False, input_structure=None, log_dir='', log_prefix=True, mode='compile', model='/workspace/examples/YOLOv7_object/model/yolov7.pt', net_name='yolov7', op_list=False, opt=1, outdir='output/YOLOv7', seed=42, shapes=[[1, 3, 640, 640]], target='BM1684', v=3)
    5. python3 -m bmnetp --model=/workspace/examples/YOLOv7_object/model/yolov7.pt --net_name=yolov7 --target=BM1684 --outdir=output/YOLOv7 --shapes="[1,3,640,640]" --opt=1 --cmp=true --dyn=false --enable_profile=false --mode=compile --seed=42
    6. /root/.local/lib/python3.7/site-packages/bmnetp/bin/bmnetp.bin --model=/workspace/examples/YOLOv7_object/model/yolov7.pt --net_name=yolov7 --target=BM1684 --outdir=output/YOLOv7 --shapes="[1,3,640,640]" --opt=1 --cmp=true --dyn=false --enable_profile=false --mode=0 --seed=42
    7. terminate called after throwing an instance of 'c10::Error'
    8.   what():  [enforce fail at inline_container.cc:222] . file not found: archive/constants.pkl
    9. frame #0: c10::ThrowEnforceNotMet(char const*, int, char const*, std::string const&, void const*) + 0x47 (0x7f990e0c80e7 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libc10.so)
    10. frame #1: caffe2::serialize::PyTorchStreamReader::getRecordID(std::string const&) + 0xed (0x7f98fe93accd in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    11. frame #2: caffe2::serialize::PyTorchStreamReader::getRecord(std::string const&) + 0x21 (0x7f98fe93ad41 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    12. frame #3: torch::jit::readArchiveAndTensors(std::string const&, c10::optional<std::function<c10::StrongTypePtr (c10::QualifiedName const&)> >, c10::optional<std::function<c10::intrusive_ptr<c10::ivalue::Object, c10::detail::intrusive_target_default_null_type<c10::ivalue::Object> > (c10::StrongTypePtr, c10::IValue)> >, c10::optional<c10::Device>, caffe2::serialize::PyTorchStreamReader&) + 0x62 (0x7f98ffe322f2 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    13. frame #4: <unknown function> + 0x348b8b4 (0x7f98ffe328b4 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    14. frame #5: <unknown function> + 0x348d193 (0x7f98ffe34193 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    15. frame #6: torch::jit::load(std::shared_ptr<caffe2::serialize::ReadAdapterInterface>, c10::optional<c10::Device>, std::unordered_map<std::string, std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, std::string> > >&) + 0x193 (0x7f98ffe352f3 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    16. frame #7: torch::jit::load(std::string const&, c10::optional<c10::Device>, std::unordered_map<std::string, std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, std::string> > >&) + 0xad (0x7f98ffe376bd in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    17. frame #8: torch::jit::load(std::string const&, c10::optional<c10::Device>) + 0x54 (0x7f98ffe37794 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libtorch_cpu.so)
    18. frame #9: <unknown function> + 0x177ed2 (0x7f9911793ed2 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libbmnetp.so)
    19. frame #10: bm::check(std::string const&) + 0x2f (0x7f9911797df7 in /root/.local/lib/python3.7/site-packages/bmnetp/lib/libbmnetp.so)
    20. frame #11: main + 0xdc (0x445195 in /root/.local/lib/python3.7/site-packages/bmnetp/bin/bmnetp.bin)
    21. frame #12: __libc_start_main + 0xf0 (0x7f98fb57a840 in /lib/x86_64-linux-gnu/libc.so.6)
    22. frame #13: _start + 0x2a (0x4418aa in /root/.local/lib/python3.7/site-packages/bmnetp/bin/bmnetp.bin)
    23.  
    24. Aborted (core dumped)
    25. compile failed, exit code=134
    26. <class 'SystemExit'> 134 <traceback object at 0x7fa01fcd1448>
    27. Something is wrong, pleae have a check!

    首先怀疑是torch版本问题，通过查阅YOLOV7官方资料，YOLOV7要求torch>=1.7.0,!=1.12.0，torchvision>=0.8.1,!=0.13.0，而当前docker内相关包版本不匹配，如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# pip list | grep torch
    2. torch                         1.5.0+cpu
    3. torchvision                   0.6.0+cpu

    后面通过咨询同事，发现模型迁移的原生模型必须要通过torchscript处理后才能进行迁移。鉴于此，需要重新配置docker环境。

    3.1.1.2 docker环境配置

    下面通过conda，进行docker环境管理。首先，需要安装conda，在上述docker中，命令如下：

    1. [2022-07-14 20:02:54]  root@bitmain-SYS-4028GR-TR2:/workspace# mkdir miniconda
    2. [2022-07-14 20:03:29]  root@bitmain-SYS-4028GR-TR2:/workspace# cd miniconda/
    3. [2022-07-14 20:03:31]  root@bitmain-SYS-4028GR-TR2:/workspace/miniconda# wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Minicond aot@bitmain-SYS-4028GR-TR2:/workspace/miniconda# w
    4. ......
    5. [2022-07-14 20:04:07]  2022-07-14 20:04:15 (3.83 MB/s) - 'Miniconda3-latest-Linux-x86_64.sh' saved [76607678/76607678]
    6. [2022-07-14 20:04:37]  root@bitmain-SYS-4028GR-TR2:/workspace/miniconda# bash Miniconda3-latest-Linux-x86_64.sh 
    7. ......
    8. [2022-07-14 20:05:34]  conda config --set auto_activate_base false
    9. [2022-07-14 20:05:34]  
    10. [2022-07-14 20:05:34]  Thank you for installing Miniconda3!

    退出docker后，再次进入，conda就会默认启用了，注意看如下日志最后一行的(base)即表示已启用conda，日志如下：

    1. [2022-07-14 20:05:53]  root@bitmain-SYS-4028GR-TR2:/workspace/miniconda# exit
    2. [2022-07-14 20:06:09]  exit
    3. [2022-07-14 20:06:09]  (base) ningbo.wang@bitmain-SYS-4028GR-TR2:~$docker exec -it ubuntu16.0-py37-wnb bash
    4. [2022-07-14 20:06:12]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# conda

    为了使用方便，这里配置conda开机不默认启动，顺便把镜像源都配置为国内，下载包的速度会比较快，这里配置为清华镜像源，配置命令如下：

    1. [2022-07-14 20:06:18]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# con   condaexitconda config --set auto_activate_base fals
    2. [2022-07-14 20:08:11]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anacon 
    3. da/pkgs/free/
    4. [2022-07-14 20:08:34]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# onda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anacond 
    5. a/pkgs/main/bitmain-SYS-4028GR-TR2:/workspace# conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda
    6. [2022-07-14 20:08:45]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# conda config --set show_channel_urls yes
    7. [2022-07-14 20:09:02]  (base) root@bitmain-SYS-4028GR-TR2:/workspace# pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simpl 
    8. e

    上述配置完成后，可以退出docker后，重新进入，然后创建yolov7环境，进行环境配置，如下：

    1. [2022-07-14 20:14:09]  root@bitmain-SYS-4028GR-TR2:/workspace# conda create -n yolov7 python=3.7
    2. ......
    3. [2022-07-14 20:16:16]  # To activate this environment, use
    4. [2022-07-14 20:16:16]  #
    5. [2022-07-14 20:16:16]  #     $ conda activate yolov7
    6. [2022-07-14 20:16:16]  #
    7. [2022-07-14 20:16:16]  # To deactivate an active environment, use
    8. [2022-07-14 20:16:16]  #
    9. [2022-07-14 20:16:16]  #     $ conda deactivate
    10. [2022-07-14 20:16:16]  root@bitmain-SYS-4028GR-TR2:/workspace# conda activate yolov7
    11. [2022-07-14 20:17:01]  (yolov7) root@bitmain-SYS-4028GR-TR2:/workspace#

    3.1.1.3 YOLOv7模型准备

    拉取官方代码仓库，并下载原生模型，之后将原生模型转换为torchscript模型，目录结构如下：

    1. [2022-07-14 21:00:58]  (yolov7) root@bitmain-SYS-4028GR-TR2:/workspace/code/yolov7# python m models/export.py --weights yolov7.pt 
    2. ......
    3. [2022-07-14 21:01:24]  Export complete (10.26s). Visualize with https://github.com/lutzroeder/netron.
    4.  
    5. root@bitmain-SYS-4028GR-TR2:/workspace/code/yolov7# tree -L 1
    6. .
    7. |-- LICENSE.md
    8. |-- README.md
    9. |-- cfg
    10. |-- data
    11. |-- detect.py
    12. |-- figure
    13. |-- hubconf.py
    14. |-- inference
    15. |-- models
    16. |-- requirements.txt
    17. |-- scripts
    18. |-- test.py
    19. |-- tools
    20. |-- train.py
    21. |-- train_aux.py
    22. |-- utils
    23. |-- yolov7.onnx
    24. |-- yolov7.pt
    25. `-- yolov7.torchscript.pt

    3.1.1.4 模型转换

    下面基于yolov7.torchscript.pt进行模型迁移，这里需要将停止docker，采用官方原生的docker环境，即【3.1.1.1】中的环境，命令如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/code/yolov7# cp yolov7.torchscript.pt ../../examples/YOLOv7_object/model/
    2. core                      gen_bmodel.sh             out/
    3. download_yolov7_model.sh  gen_umodel_int8bmodel.sh  yolov7.pt
    4. root@bitmain-SYS-4028GR-TR2:/workspace/code/yolov7# cp yolov7.torchscript.pt ../../examples/YOLOv7_object/model/
    5. root@bitmain-SYS-4028GR-TR2:/workspace/code/yolov7# cd ../../examples/YOLOv7_object/model
    6. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# ./gen_bmodel.sh
    7. model path: /workspace/examples/YOLOv7_object/model
    8. start model transform......
    9. ......
    10. BMLIB Send Quit Message
    11. Compiling succeeded.
    12. Congratulation! Everything is OK!
    13.  
    14. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# tree output/YOLOv7/
    15. output/YOLOv7/
    16. |-- compilation.bmodel
    17. |-- input_ref_data.dat
    18. |-- io_info.dat
    19. `-- output_ref_data.dat
    20.  
    21. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# bm_model.bin --info ./output/YOLOv7/compilation.bmodel
    22. bmodel version: B.2.2
    23. chip: BM1684
    24. create time: Fri Jul 15 10:40:10 2022
    25.  
    26. ==========================================
    27. net 0: [yolov7]  static
    28. ------------
    29. stage 0:
    30. input: x.1, [1, 3, 640, 640], float32, scale: 1
    31. output: 756, [1, 3, 80, 80, 85], float32, scale: 1
    32. output: 757, [1, 3, 40, 40, 85], float32, scale: 1
    33. output: 758, [1, 3, 20, 20, 85], float32, scale: 1
    34.

    3.1.1.5 精度回归

    下面借助官方工具，进行转换模型精度回归，精度符合预期，如下所示：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# bmrt_test --context_dir=./output/YOLOv7/
    2. [BMRT][deal_with_options:1412] INFO:Loop num: 1
    3. bmcpu init: skip cpu_user_defined
    4. ......
    5. [BMRT][bmrt_test:1038] INFO:==>comparing #0 output ...
    6. [BMRT][bmrt_test:1043] INFO:+++ The network[yolov7] stage[0] cmp success +++
    7. [BMRT][bmrt_test:1063] INFO:load input time(s): 0.004891
    8. [BMRT][bmrt_test:1064] INFO:calculate  time(s): 0.084028
    9. [BMRT][bmrt_test:1065] INFO:get output time(s): 0.007568
    10. [BMRT][bmrt_test:1066] INFO:compare    time(s): 0.027697
    11.

    至此，fp32bmodel生成完毕。

    3.1.2 生成int8 bmodel

    int8量化模型相较于fp32复杂一些，大致需要一下步骤。

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    3.1.2.1 量化数据集准备

    这里基于coco128数据进行处理，主要参考YOLOv7前处理，需要保持一致，主要是等比例加框处理、归一化，将数据集处理成lmdb格式的文件，命令执行如下:

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/data# python3 convert_imageset.py --imageset_rootfolder ./coco128/images/train2017/ --imageset_lmdbfolder ./ --image_size  640 --bgr2rgb True --gray False
    2. remove original lmdb file /workspace/examples/YOLOv7_object/data/data.mdb
    3. remove original lmdb file /workspace/examples/YOLOv7_object/data/data.mdb Ok!
    4.  
    5. reading image /workspace/examples/YOLOv7_object/data/coco128/images/train2017/000000000472.jpg
    6. original shape: (226, 640, 3)
    7. save test.jpg done
    8. ......
    9.  
    10. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/data# tree -L 1
    11. .
    12. |-- coco128
    13. |-- convert_imageset.py
    14. |-- data.mdb
    15. `-- download_coco128.sh

    这里为了方便查看前处理图片是否正确，将加框处理后图片存出后，对比查看，如下可以看出前处理正确:

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    3.1.2.2 fp32umodel生成

    采用ufw.tools.pt_to_umode工具，进行fp32umodel生成，命令如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# ./gen_fp32umodel.sh
    2. /workspace/examples/YOLOv7_object/model
    3. /usr/local/lib/python3.7/runpy.py:125: RuntimeWarning: 'ufw.tools.pt_to_umodel' found in sys.modules after import of package 'ufw.tools', but prior to execution of 'ufw.tools.pt_to_umodel'; this may result in unpredictable behaviour
    4.   warn(RuntimeWarning(msg))
    5. python3 -m bmnetp --model=./yolov7.torchscript.pt --net_name=yolov7.torchscript --target=BM1684 --outdir=compilation_fp32umodel --shapes="[1,3,640,640]" --opt=2 --cmp=true --dyn=false --enable_profile=false --mode=GenUmodel
    6. /root/.local/lib/python3.7/site-packages/bmnetp/bin/bmnetp.bin --model=./yolov7.torchscript.pt --net_name=yolov7.torchscript --target=BM1684 --outdir=compilation_fp32umodel --shapes="[1,3,640,640]" --opt=2 --cmp=true --dyn=false --enable_profile=false --mode=1
    7. All ops supported.
    8. ......
    9. Compiling succeeded.
    10. ####################################
    11. Converting Process Done Sucessfully
    12. ####################################
    13. fp32umodel done
    14.

    3.1.2.3 int8umodel生成

    下面，基于上述章节生成的数据集、fp32umode等成果物，进行int8umodel转换，主要包含两部分：

    • 对输入浮点网络进行图优化，这一步在【3.1.2.2】中已包含，也可以在此处做

    • 对浮点网络进行量化，得到int8的网络及权重文件

    这里我们只进行int8的量化，不进行图优化，迭代200次，命令执行如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# ./gen_int8umodel.sh
    2. /workspace/examples/YOLOv7_object/model
    3. I0718 10:21:30.706017  6933 common.cpp:62] ufw version with commit id:bc3faf38c90b7216f95796e9edaa8cecd9227d8d
    4. I0718 10:21:30.706442  6933 calibration_use_pb.cpp:171] calibration-tools version with commit id:bc3faf38c90b7216f95796e9edaa8cecd9227d8d
    5. ......
    6. /usr/bin/dot
    7. I0718 10:46:52.879577  6933 cali_core.cpp:1474] used time=0 hour:25 min:22 sec
    8. I0718 10:46:52.879654  6933 cali_core.cpp:1476] int8 calibration done.
    9. Congratulation! Everything is OK!
    10.  
    11. #目录结构
    12. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# tree compilation_fp32umodel/
    13. compilation_fp32umodel/
    14. |-- io_info.dat
    15. |-- yolov7.fp32umodel -> yolov7.torchscript_bmnetp.fp32umodel
    16. |-- yolov7.int8umodel
    17. |-- yolov7.prototxt -> yolov7.torchscript_bmnetp_test_fp32.prototxt
    18. |-- yolov7.torchscript_bmnetp.fp32umodel
    19. |-- yolov7.torchscript_bmnetp_test_fp32.prototxt
    20. |-- yolov7_deploy_fp32_unique_top.prototxt
    21. `-- yolov7_deploy_int8_unique_top.prototxt

    注意，官方工具存在一些问题，手册讲解与工具实际表现不一致，如下：

    • -winograd配置为false或者true均会无报错，返回0状态退出，经过尝试，实际是只要-winograd则是使能（true），否则不配置该参数即为false，而官方手册及工具本身的help都是错误的，需要更新，另外，通过试探-save_test_proto、-graph_transform均是如此

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    3.1.2.4 int8bmodel生成

    下面生成板上部署使用的bmodel，代码大致如下：

    1. #!/bin/bash
    2. #1batch bmodel
    3. mkdir int8model
    4. bmnetu \
    5.     -model compilation_fp32umodel/yolov7_deploy_int8_unique_top.prototxt \
    6.     -weight compilation_fp32umodel/yolov7.int8umodel \
    7.     -outdir=./int8model \
    8.     -cmp true
    9.  
    10. if [ $? -eq 0 ]; then
    11.     cp ./int8model/compilation.bmodel ./int8model/yolov7_int8_1b.bmodel
    12.     echo "Congratulation! Everything is OK!"
    13. else
    14.     echo "Something is wrong, pleae have a check!"
    15.     exit -1
    16. fi

    运行脚本，命令执行及最终成果物路径如下：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# ./gen_int8bmodel.sh
    2. /workspace/examples/YOLOv7_object/model
    3. mkdir: cannot create directory 'int8model': File exists
    4. ......
    5. ============================================================
    6. *** Store bmodel of BMCompiler...
    7. ============================================================
    8. BMLIB Send Quit Message
    9. Congratulation! Everything is OK!
    10. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# tree -L 1 int8model/
    11. int8model/
    12. |-- compilation.bmodel
    13. |-- input_ref_data.dat
    14. |-- io_info.dat
    15. |-- output_ref_data.dat
    16. `-- yolov7_int8_1b.bmodel  #1batch的最终成果物

    3.1.2.5 精度回归

    下面借助官方工具，进行转换模型精度回归，精度符合预期，如下所示：

    1. root@bitmain-SYS-4028GR-TR2:/workspace/examples/YOLOv7_object/model# bmrt_test --context_dir=./int8model/
    2. [BMRT][deal_with_options:1412] INFO:Loop num: 1
    3. bmcpu init: skip cpu_user_defined
    4. open usercpu.so, init user_cpu_init
    5. [BMRT][load_bmodel:1018] INFO:Loading bmodel from [./int8model//compilation.bmodel]. Thanks for your patience...
    6. [BMRT][load_bmodel:982] INFO:pre net num: 0, load net num: 1
    7. [BMRT][show_net_info:1336] INFO: ########################
    8. [BMRT][show_net_info:1337] INFO: NetName: yolov7, Index=0
    9. [BMRT][show_net_info:1339] INFO: ---- stage 0 ----
    10. [BMRT][show_net_info:1347] INFO:   Input 0) 'x.1' shape=[ 1 3 640 480 ] dtype=INT8 scale=127.031
    11. [BMRT][show_net_info:1356] INFO:   Output 0) '756' shape=[ 1 3 80 60 85 ] dtype=INT8 scale=0.198189
    12. [BMRT][show_net_info:1356] INFO:   Output 1) '757' shape=[ 1 3 40 30 85 ] dtype=INT8 scale=0.202178
    13. [BMRT][show_net_info:1356] INFO:   Output 2) '758' shape=[ 1 3 20 15 85 ] dtype=INT8 scale=0.169756
    14. [BMRT][show_net_info:1359] INFO: ########################
    15. [BMRT][bmrt_test:770] INFO:==> running network #0, name: yolov7, loop: 0
    16. [BMRT][bmrt_test:834] INFO:reading input #0, bytesize=921600
    17. [BMRT][bmrt_test:987] INFO:reading output #0, bytesize=1224000
    18. [BMRT][bmrt_test:987] INFO:reading output #1, bytesize=306000
    19. [BMRT][bmrt_test:987] INFO:reading output #2, bytesize=76500
    20. [BMRT][bmrt_test:1019] INFO:net[yolov7] stage[0], launch total time is 32659 us (npu 32530 us, cpu 129 us)
    21. [BMRT][bmrt_test:1022] INFO:+++ The network[yolov7] stage[0] output_data +++
    22. [BMRT][bmrt_test:1038] INFO:==>comparing #0 output ...
    23. [BMRT][bmrt_test:1043] INFO:+++ The network[yolov7] stage[0] cmp success +++
    24. [BMRT][bmrt_test:1063] INFO:load input time(s): 0.000951
    25. [BMRT][bmrt_test:1064] INFO:calculate  time(s): 0.032664
    26. [BMRT][bmrt_test:1065] INFO:get output time(s): 0.001572
    27. [BMRT][bmrt_test:1066] INFO:compare    time(s): 0.005961

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  • 原文地址：https://blog.csdn.net/captain_wangnb/article/details/126178134