Ubuntu Linux下安装 TensorFlow等开发环境

1. 安装基本工具

sudo apt-get install build-essential
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev libgtkglext1-dev libgtk-3-dev
sudo apt install libcanberra-gtk-module libcanberra-gtk3-module

2. 安装 OPENMP

sudo apt install libomp-dev

3. 打开 WayLand

编辑 /etc/gdm3/custom.conf，修改配置使得，“WaylandEnable=true”

# edit /etc/gdm3/custom.conf
sudo nano /etc/gdm3/custom.conf
******************************
+ WaylandEnable=true 
******************************# restart GDM
sudo systemctl restart gdm3

4. [可选] 安装 SCR

>> tesseract是一个开源的OCR引擎，最初是由惠普公司开发用来作为其平板扫描仪的 OCR引擎，2005年惠普将其开源出来，之后google接手负责维护

sudo add-apt-repository ppa:alex-p/tesseract-ocr
sudo apt-get update 
sudo apt-get install tesseract-ocr

>> 字库下载 : tesseract支持60多种语言的识别不同，使用之前需要先下载对应语言的字库，下载地址：https://github.com/tesseract-ocr/tessdata
latest update : 2021 . 5 years ago generically
>> 下载完成之后把.traineddata字库文件放到tessdata目录下，默认路径是/usr/share/tesseract-ocr /4.00/tessdata

wget https://gitcode.net/mirrors/tesseract-ocr/tessdata/-/archive/4.1.0/tessdata-4.1.0.tar.gz
tar xf tessdata-4.1.0.tar.gz
sudo cp -a tessdata-4.1.0/*.traineddata /usr/share/tesseract-ocr/4.00/tessdata/

5. 安装 Intel@ openCL

( https://github.com/intel/compute-runtime/releases )

 mkdir neo && cd neo
# Download all *.deb packages
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.14062.11/intel-igc-core_1.0.14062.11_amd64.deb
wget https://github.com/intel/intel-graphics-compiler/releases/download/igc-1.0.14062.11/intel-igc-opencl_1.0.14062.11_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/intel-level-zero-gpu-dbgsym_1.3.26516.18_amd64.ddeb
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/intel-level-zero-gpu_1.3.26516.18_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/intel-opencl-icd-dbgsym_23.22.26516.18_amd64.ddeb
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/intel-opencl-icd_23.22.26516.18_amd64.deb
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/libigdgmm12_22.3.0_amd64.deb 
# Verify sha256 sums for packages
wget https://github.com/intel/compute-runtime/releases/download/23.22.26516.18/ww22.sum
sha256sum -c ww22.sum
# Install all packages as root
sudo dpkg -i *.deb
# In case of installation problems, please install required dependencies, for example:
sudo apt install ocl-icd-libopencl1

6. 安装 Intel@ oneAPI MKL (Math Kernel Library)

6.1 在线安装

( https://www.intel.com/content/www/us/en/docs/oneapi/installation-guide-linux/2023-0/apt.html )

# download the key to system keyring
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | sudo tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null
# add signed entry to apt sources and configure the APT client to use Intel repository:
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
sudo apt update
sudo apt install intel-basekit 
# Intel® oneAPI HPC Toolkit
sudo apt install intel-hpckit
# Intel® oneAPI IoT Toolkit
sudo apt install intel-iotkit
# Intel® oneAPI DL Framework Developer Toolkit
sudo apt install intel-dlfdkit 
# Intel® AI Analytics Toolkit
sudo apt install intel-aikit
# Intel® oneAPI Rendering Toolkit
sudo apt install intel-renderkit

6.2 离线安装

( https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html )

wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/adb8a02c-4ee7-4882-97d6-a524150da358/l_onemkl_p_2023.2.0.49497_offline.sh
sudo sh ./l_onemkl_p_2023.2.0.49497_offline.sh

+------------------------------------------------------------------------
        | Recommended for host machines with poor or no internet connection
        | Size     913.25 MB
        | Version     2023.2.0
        | Date     July 13, 2023
        | SHA384     f5cc20cdd92ab961693c7649fb0b046937ae8aae92eb1464090a187816e7bad3ccd6ef5bf90924226d5f4d1314fe57ab
        +------------------------------------------------------------------------

7. 编译 open-cv 带 opencv_contrib

# Install minimal prerequisites (Ubuntu 18.04 as reference)
sudo apt update && sudo apt install -y cmake g++ wget unzip
# Download and unpack sources
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.x.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.x.zip
unzip opencv.zip
unzip opencv_contrib.zip
# Create build directory and switch into it
mkdir -p build && cd build
# Configure
cmake -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib-4.x/modules ../opencv-4.x
# or debug mode
cmake -DCMAKE_BUILD_TYPE=Debug -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib-4.x/modules ../opencv-4.x
# Build
cmake --build . -j 8

8. 直接安装预编译好的 TensorFlow

(https://tensorflow.google.cn/install/source?hl=zh-cn)

pip install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple

或

pip install tensorflow==2.13 -i https://pypi.tuna.tsinghua.edu.cn/simple

TensorFlow 将被安装在 ~/.local/lib/python3.8/site-packages/tensorflow 路径下，使用TensorFlow 库的项目将需要包含这个路径 ~/.local/lib/python3.8/site-packages/tensorflow/include as include directory.
use ~/.local/lib/python3.8/site-packages/tensorflow as lib path

建议添加指向库的软链接，方便部分基于这些库的开源项目的编译。

cd ~/.local/lib/python3.8/site-packages/tensorflow
ln -s libtensorflow_cc.so.2        libtensorflow_cc.so
ln -s libtensorflow_framework.so.2 libtensorflow_framework.socd ~/.local/lib/python3.8/site-packages/numpy.libs
ln -s libopenblas64_p-r0-15028c96.3.21.so  libopenblas.so
ln -s libquadmath-96973f99.so.0.0.0        libquadmath.so

9. 手动编译 TenserFlow C++ 库

（https://blog.csdn.net/MOU_IT/article/details/87976152)
   9.1 install protobuf
       wget https://github.com/protocolbuffers/protobuf/releases/download/v3.7.1/protobuf-cpp-3.7.1.tar.gz
       tar -xzvf protobuf-cpp-3.7.1.tar.gz
       sudo apt-get install automake libtool
       ./autogen.sh
       ./configure
       make
       sudo make install
       sudo ldconfig
       # sudo make uninstall 安装错版本后卸载指令
       protoc --version # 查看protobuf版本

   9.2 Install bazel
       +--------------------------------+--------------------------+
       | tensorflow 2.15.0 rc | Bazel 6.1.0 |
       | tensorflow 2.13.0 | Bazel 5.3.0 |
       +--------------------------------+--------------------------+
       bazel是Google开源的一套编译构建工具，广泛应用于Google内部，包括TensorFlow项目。
       # prepare tools
       sudo apt-get install pkg-config zip g++ zlib1g-dev unzip python
       # download bazel 5.3.0
#       wget https://github.com/bazelbuild/bazel/releases/download/6.1.0/bazel-6.1.0-installer-linux-x86_64.sh
       wget https://github.com/bazelbuild/bazel/releases/download/5.3.0/bazel-5.3.0-installer-linux-x86_64.sh
       sudo chmod +x bazel-5.3.0-installer-linux-x86_64.sh
       # install bazel to $HOME/.bazel/bin
       ./bazel-5.3.0-installer-linux-x86_64.sh --user
       export PATH="$PATH:$HOME/bin"

   9.3 downlaod tenserFlow
       git clone --recursive https://github.com/tensorflow/tensorflow.git
       cd tensorflow
       # switch to the branch you want to build
       git checkout r2.13 # r1.9, r1.10, etc.

   9.4 build tenserFlow library (libtensorflow_cc.so & libtensorflow_cc_framework.so)
       ./configure
       bazel build --config=opt //tensorflow:libtensorflow_cc.so
       bazel build --config=opt --config=cuda //tensorflow:libtensorflow_cc.so # no NVidia display card
       bazel build -c opt --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma //tensorflow/tools/pip_package:build_pip_package

       bazel build //tensorflow/tools/pip_package:build_pip_package
       bazel-bin/tensorflow/tools/pip_package/build_pip_package package/20230912
       pip uninstall tensorflow
       pip install package/20230912/tensorflow-*.whl

   9.5 Install eigen(https://eigen.tuxfamily.org/index.php?title=Main_Page)

       wget https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.tar.bz2
       ## git clone https://github.com/eigenteam/eigen-git-mirror ## 3.3.99 cause compile error
       git clone https://gitlab.com/libeigen/eigen.git
       #安装
       wget https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.tar.bz2
       tar xf eigen-3.4.0.tar.bz2
       cd eigen-3.4.0
       cmake .. && sudo make install
       # 安装后,头文件安装在/usr/local/include/eigen3/
       # 很多程序中include时经常使用#include <Eigen/Dense>
       # 而不是使用#include <eigen3/Eigen/Dense>
       # 所以要做下处理
       sudo ln -s /usr/local/include/eigen3/Eigen /usr/local/include/Eigen
   9.6 Install ml_types (https://pypi.org/project/ml-dtypes/) (https://github.com/jax-ml/ml_dtypes)
       ml_dtypes is a stand-alone implementation of several NumPy dtype extensions used in machine learning libraries,
       sudo apt install python3-pip
       sudo pip install ml_dtypes

       /usr/local/lib/python3.8/dist-packages/third_party/eigen/Eigen
       +--------------------------------------------------solve compile error ----------------------------------------------+
       | [ 50%] Building CXX object CMakeFiles/tf_test.dir/src/hello.cpp.o
       | In file included from /home/rd/tensorflow/tensorflow/core/platform/float8.h:19,
       | from /home/rd/tensorflow/tensorflow/core/platform/types.h:20,
       | from /home/rd/tensorflow/tensorflow/core/platform/env_time.h:20,
       | from /home/rd/tensorflow/tensorflow/core/platform/env.h:26,
       | from /home/rd/tensorflow-test-prog/src/hello.cpp:1:
       |       /home/rd/tensorflow/tensorflow/tsl/platform/float8.h:19:10: fatal error: include/float8.h:没有那个文件或目录
       | 19 | #include "include/float8.h" // from @ml_dtypes
       | | ^~~~~~~~~~~~~~~~~~
       | compilation terminated.
       +--------------------------------------------------------------------------------------------------------------------+

   9.7 Install absl
       wget https://github.com/abseil/abseil-cpp/archive/refs/tags/20230125.3.tar.gz
       tar xf 20230125.3.tar.gz
       cd abseil-cpp-20230125.3
       mkdir build
       cd build
       cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local
       make -j8
       sudo make install
       +--------------------------------------------------solve compile error ----------------------------------------------+
       | In file included from /home/rd/tensorflow/tensorflow/core/platform/cord.h:19,
       | from /home/rd/tensorflow/tensorflow/core/platform/tstring.h:19,
       | from /home/rd/tensorflow/tensorflow/core/platform/types.h:22,
       | from /home/rd/tensorflow/tensorflow/core/platform/env_time.h:20,
       | from /home/rd/tensorflow/tensorflow/core/platform/env.h:26,
       | from /home/rd/tensorflow-test-prog/src/hello.cpp:1:
       | /home/rd/tensorflow/tensorflow/tsl/platform/cord.h:21:10: fatal error: absl/strings/cord.h: 没有那个文件或目录
       | 21 | #include "absl/strings/cord.h" // IWYU pragma: export
       | | ^~~~~~~~~~~~~~~~~~~~~
       +--------------------------------------------------------------------------------------------------------------------+

   9.8 Install protoc & protobuf
       sudo apt-get install autoconf automake libtool curl make g++ unzip
       wget https://github.com/protocolbuffers/protobuf/releases/download/v24.2/protobuf-24.2.tar.gz
       tar xf protobuf-24.2.tar.gz
       cd protobuf-24.2
       cp -a abseil-cpp-20230125.3 thirdparty/abseil-cpp
       mkdir build && cd build
       cmake -Dprotobuf_BUILD_TESTS=OFF ..
       make -j8
       sudo make install
       sudo ldconfig # refresh shared library cache.
       -- or --
       wget https://github.com/protocolbuffers/protobuf/releases/download/v24.2/protoc-24.2-linux-x86_64.zip
       unzip protoc-24.2-linux-x86_64.zip
       sudo cp bin/protoc /usr/local/bin/
       sudo cp -a include/google /usr/local/include/
       +--------------------------------------------------solve compile error ----------------------------------------------+
       |    In file included from /home/rd/tensorflow/tensorflow/tsl/platform/status.h:39,
       | from /home/rd/tensorflow/tensorflow/core/platform/status.h:23,
       | from /home/rd/tensorflow/tensorflow/core/platform/errors.h:27,
       | from /home/rd/tensorflow/tensorflow/core/platform/env.h:27,
       | from /home/rd/tensorflow-test-prog/src/hello.cpp:1:
       | /home/rd/tensorflow/tensorflow/tsl/protobuf/error_codes.pb.h:11:10: fatal error: google/protobuf/port_def.inc: 没有那个文件或目录
       | 11 | #include "google/protobuf/port_def.inc"
       | | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
       | compilation terminated.
       | make[2]: *** [CMakeFiles/tf_test.dir/build.make:63：CMakeFiles/tf_test.dir/src/hello.cpp.o] 错误 1
       | make[1]: *** [CMakeFiles/Makefile2:76：CMakeFiles/tf_test.dir/all] 错误 2
       | make: *** [Makefile:84：all] 错误 2
       +--------------------------------------------------------------------------------------------------------------------+

   9.9   Build error_codes.pb.h & error_codes.pb.cc and so on
       cd ~/tensorflow
       ls tensorflow/tsl/protobuf
           bfc_memory_map.proto coordination_config.proto distributed_runtime_payloads.proto error_codes.proto rpc_options.proto test_log.proto
           BUILD coordination_service.proto dnn.proto histogram.proto status.proto
       protoc --cpp_out=. tensorflow/tsl/protobuf/*.proto
       ls tensorflow/tsl/protobuf/
           bfc_memory_map.pb.cc coordination_config.proto distributed_runtime_payloads.proto error_codes.proto rpc_options.proto test_log.proto
           bfc_memory_map.pb.h coordination_service.pb.cc dnn.pb.cc histogram.pb.cc status.pb.cc
           bfc_memory_map.proto coordination_service.pb.h dnn.pb.h histogram.pb.h status.pb.h
           BUILD coordination_service.proto dnn.proto histogram.proto status.proto
           coordination_config.pb.cc distributed_runtime_payloads.pb.cc error_codes.pb.cc rpc_options.pb.cc test_log.pb.cc
           coordination_config.pb.h distributed_runtime_payloads.pb.h error_codes.pb.h rpc_options.pb.h test_log.pb.h

       protoc --cpp_out=. tensorflow/*/*/*.proto
       protoc --cpp_out=. tensorflow/*/*/*/*.proto
       +--------------------------------------------------solve compile error ----------------------------------------------+
       |            [ 50%] Building CXX object CMakeFiles/tf_test.dir/src/hello.cpp.o
       | In file included from /home/rd/tensorflow/tensorflow/core/platform/status.h:23,
       | from /home/rd/tensorflow/tensorflow/core/platform/errors.h:27,
       | from /home/rd/tensorflow/tensorflow/core/platform/env.h:27,
       | from /home/rd/tensorflow-test-prog/src/hello.cpp:1:
       | /home/rd/tensorflow/tensorflow/tsl/platform/status.h:39:10: fatal error: tensorflow/tsl/protobuf/error_codes.pb.h: 没有那个文件或目录
       | 39 | #include "tensorflow/tsl/protobuf/error_codes.pb.h"
       | | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
       | compilation terminated.
       | make[2]: *** [CMakeFiles/tf_test.dir/build.make:63：CMakeFiles/tf_test.dir/src/hello.cpp.o] 错误 1
       | make[1]: *** [CMakeFiles/Makefile2:76：CMakeFiles/tf_test.dir/all] 错误 2
       | make: *** [Makefile:84：all] 错误 2
       +--------------------------------------------------------------------------------------------------------------------+
       [ 50%] Building CXX object CMakeFiles/tf_test.dir/src/hello.cpp.o
       | In file included from /home/rd/tensorflow-test-prog/src/hello.cpp:2:
       | /home/rd/tensorflow/tensorflow/core/public/session.h:24:10: fatal error: tensorflow/core/framework/device_attributes.pb.h: 没有那个文件或目录
        | 24 | #include "tensorflow/core/framework/device_attributes.pb.h"
       | | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
       | compilation terminated.
       | make[2]: *** [CMakeFiles/tf_test.dir/build.make:63：CMakeFiles/tf_test.dir/src/hello.cpp.o] 错误 1
       | make[1]: *** [CMakeFiles/Makefile2:76：CMakeFiles/tf_test.dir/all] 错误 2
       | make: *** [Makefile:84：all] 错误 2
       +--------------------------------------------------------------------------------------------------------------------+

   9.10   # create symbolic link to libtensorflow_framework.so.2.15.0
       cd ~/tensorflow/bazel-bin/tensorflow
       ln -s libtensorflow_framework.so.2.15.0 libtensorflow_framework.so
       ln -s libtensorflow_framework.so.2.15.0 libtensorflow_framework.so.2
           lrwxrwxrwx 1 rd rd 33 9月 6 16:43 libtensorflow_framework.so -> libtensorflow_framework.so.2.15.0
           lrwxrwxrwx 1 rd rd 33 9月 6 16:43 libtensorflow_framework.so.2 -> libtensorflow_framework.so.2.15.0
       +--------------------------------------------------solve compile error ----------------------------------------------+
       |    -- Build files have been written to: /home/rd/tensorflow-test-prog
       | [ 50%] Building CXX object CMakeFiles/tf_test.dir/src/hello.cpp.o
       | [100%] Linking CXX executable tf_test
       | /usr/bin/ld: 找不到 -ltensorflow_framework
       | collect2: error: ld returned 1 exit status
       | make[2]: *** [CMakeFiles/tf_test.dir/build.make:84：tf_test] 错误 1
       | make[1]: *** [CMakeFiles/Makefile2:76：CMakeFiles/tf_test.dir/all] 错误 2
       | make: *** [Makefile:84：all] 错误 2s
       +--------------------------------------------------------------------------------------------------------------------+

10. compile FaceRecognition_MTCNN_FaceNet-master
       git clone https://github.com/Chanstk/FaceRecognition_MTCNN_FaceNet.git
       cd FaceRecognition_MTCNN_FaceNet
       mkdir build && cd build
       cmake .. && make

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem : error adding symbols: DSO missing from command line
       cause : shared lib called by main calls another shared lib, but CMakeLists.txt has not add -llibother.so
       solution : CXX_FLAGS += -Wl,--copy-dt-needed-entries
       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem : /usr/bin/ld: warning: libmkl_intel_lp64.so.2, needed by /usr/local/lib/libopencv_core.so.4.8.0, not found (try using -rpath or -rpath-link)
       solution : CMakeLists.txt : link_directories( /opt/intel/oneapi/mkl/2023.2.0/lib/intel64 )
        +--------------------------------------------------------------------------------------------------------------------+
        problem : 2023-09-08 14:13:58.777362: E /home/rd/NN/FaceRecognition_MTCNN_FaceNet-master/src/main.cpp:107] Read proto
        solution : vim ../src/main.cpp
                   - string graph_path = "./model/20170323-142841.pb";
                   + string graph_path = "./model/mtcnn_frozen_model.pb";
        +--------------------------------------------------------------------------------------------------------------------+
       problem : has not enablesd AVX2 FMA
           2023-09-08 14:16:17.991925: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
           2023-09-08 14:16:18.070278: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
           To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
           2023-09-08 14:16:18.218639: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled
           terminate called after throwing an instance of 'cv::Exception'
           what(): OpenCV(4.8.0) /home/rd/opencv/opencv-4.8.0/modules/core/src/arithm.cpp:652: error: (-215:Assertion failed) type2 == CV_64F && (sz2.height == 1 || sz2.height == 4) in function 'arithm_op'
       solution : mkdir model; cp ../../facenet-compare-cpp/facenet-compare-cpp-master/models/mtcnn_frozen_model.pb model/
        +--------------------------------------------------------------------------------------------------------------------+
        (https://www.intel.com/content/www/us/en/developer/articles/guide/optimization-for-tensorflow-installation-guide.html)
        wget https://repo.anaconda.com/archive/Anaconda3-2023.07-2-Linux-x86_64.sh
       sudo chmod +x Anaconda3-2023.07-2-Linux-x86_64.sh
       sudo ./Anaconda3-2023.07-2-Linux-x86_64.sh

        sudo conda install intel-tensorflow -c intel

        [ Fail ] bazel build --cxxopt=-D\_GLIBCXX\_USE\_CXX11\_ABI=0 --copt=-march=corei7-avx --copt=-mtune=core-avx-i --copt=-O3 --copt=-Wformat --copt=-Wformat-security --copt=-fstack-protector --copt=-fPIC --copt=-fpic --linkopt=-znoexecstack --linkopt=-zrelro --linkopt=-znow --linkopt=-fstack-protector //tensorflow/tools/pip_package:build_pip_package

        [ OK ] bazel clean
            bazel build -c opt --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma //tensorflow/tools/pip_package:build_pip_package

11. test facenet
git clone https://github.com/davidsandberg/facenet.git

11.1 align dataset

       for N in {1..4}; do \
       python3 src/align/align_dataset_mtcnn.py \
       ~/datasets/lfw/raw \
       ~/datasets/lfw/lfw_mtcnnpy_160 \
       --image_size 160 \
       --margin 32 \
       --random_order \
       --gpu_memory_fraction 0.25 \
       & done

       (https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw)
       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   AttributeError: module ‘tensorflow‘ has no attribute 'GPUOptions'
                   AttributeError: module 'tensorflow' has no attribute 'Session'
                   AttributeError: module 'tensorflow' has no attribute 'variable_scope'
       cause :   Tensorflow 1.X和 2.X不兼容。
       Solution :   [-]sed -i "s/tf.GPUOptions/tf.compat.v1.GPUOptions/g" */*.py */*/*.py */*/*/*.py
                   [-]sed -i "s/tf.Session/tf.compat.v1.Session/g" */*.py */*/*.py */*/*/*.py
                   [-]sed -i "s/tf.ConfigProto/tf.compat.v1.ConfigProto/g" */*.py */*/*.py */*/*/*.py

                   [+]sed -i "s/import tensorflow as tf/import tensorflow.compat.v1 as tf/g" */*.py */*/*.py */*/*/*.py

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   ValueError: Object arrays cannot be loaded when allow_pickle=False
       cause :   Tensorflow 1.X和 2.X不兼容。
       Solution :   1. pip install numpy=1.16.2
                   2. vim src/align/detect_face.py +85
                       - data_dict = np.load(data_path, encoding='latin1' ).item() #pylint: disable=no-member
                       + data_dict = np.load(data_path, encoding='latin1', allow_pickle=True).item() #pylint: disable=no-member

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   AtributeError: 'int' object has no attribute 'value'
       cause :   Tensorflow 1.X和 2.X不兼容。
       Solution :   vim src/align/detect_face.py +194
                   - feed_in, dim = (inp, input_shape[-1].value)
                   + feed_in, dim = (inp, input_shape[-1])

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   AttributeError: scipy.misc is deprecated and has no attribute imread
                   AttributeError: scipy.misc is deprecated and has no attribute imresize.
       cause :   官方scipy中提到，imread is deprecated! imread is deprecated in SciPy 1.0.0, and will be removed in 1.2.0. Use imageio.imread instead
       Solution :   sudo pip3 install imageio
                   sed -i "s/misc.im/imageio.im/g" */*.py */*/*.py
                   sed -i "s/from scipy import misc/import imageio.v2 as imageio/g" */*.py */*/*.py */*/*/*.py
                   vim src/align/align_dataset_mtcnn.py
                   + import imageio
                   - img = misc.imread(image_path)
                   + img = imageio.imread(image_path)
       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   AttributeError: module 'imageio.v2' has no attribute 'imresize'
       Solution :   sed -i "s/imageio.imresize(cropped,/Image.fromarray(cropped).resize(/g" */*.py */*/*.py
                   sed -i "s/imageio.imresize(img,/Image.fromarray(img).resize(/g" */*.py */*/*.py */*/*/*.py
                   sed -i "s/import imageio.v2 as imageio/import imageio.v2 as imageio\nfrom PIL import Image/g" */*.py */*/*.py */*/*/*.py
                   sed -i "s/interp='bilinear'/Image.BILINEAR/g" */*.py */*/*.py
       +--------------------------------------------------solve compile error ----------------------------------------------+
#       sudo apt install libjpeg-dev libtiff-dev
#       sudo pip install imageio iio # image I/O

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   div (from tensorflow.python.ops.math_ops) is deprecated and will be removed
       Solution :   https://docs.w3cub.com/tensorflow~1.15/div.html
                   1. vim src/align/detect_face.py +214
                       - tf.div
                       + tf.compat.v1.div\

   11.2 validate on lfw dataset
           python src/validate_on_lfw.py \
           ~/datasets/lfw/lfw_mtcnnpy_160 \
           ~/models/facenet/20180402-114759 \
           --distance_metric 1 \
           --use_flipped_images \
           --subtract_mean \
           --use_fixed_image_standardization
       +--------------------------------------------------solve compile error ----------------------------------------------+
       ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions.

WARNING:tensorflow:From /home/rd/NN/facenet/src/facenet.py:112: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   WARNING:tensorflow:From /home/rd/NN/facenet/src/facenet.py:131: batch_join (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
                   Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.interleave(...).batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).
       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   WARNING:tensorflow:From /home/rd/.local/lib/python3.8/site-packages/tensorflow/python/training/input.py:738: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
                   Instructions for updating:
                   To construct input pipelines, use the `tf.data` module.
                   WARNING:tensorflow:From /home/rd/.local/lib/python3.8/site-packages/tensorflow/python/training/input.py:738: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
                   Instructions for updating:
                   To construct input pipelines, use the `tf.data` module.

       sudo pip install tf-slim
       sed -i "s/import tensorflow.contrib.slim as slim/import tf_slim as slim/g" */*.py */*/*.py

   11.3 compare images
           python3 src/compare.py ~/NN/facenet-pre-trained-model/20180408-102900/20180408-102900.pb \
                                   ~/datasets/lfw/Tony_Liu/Tony_Liu_0001.jpg \
                                   ~/datasets/lfw/lfw_mtcnnpy_160/Tony_Blair/Tony_Blair_0001.png \
                                   ~/opencv/study/video/Face-Samples/stark-face/stark-6.png \
                                   ~/opencv/study/video/Face-Samples/stark-face/stark-2.png \
                                   ~/opencv/study/video/Face-Samples/tony-face/tony-2.png \
                                  ~/opencv/study/video/Face-Samples/tony-face/tony-5.png

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   raise ValueError("Expect x to not have duplicates")
               ValueError: Expect x to not have duplicates
           ERROR: Could not find a version that satisfies the requirement numpy<1.23.0,>=1.16.5 (from scipy==1.7.1) (from versions: none)
           ERROR: No matching distribution found for numpy<1.23.0,>=1.16.5 (from scipy==1.7.1)

       cause : scipy包下得interpolate.interp1d()函数问题
       requirement : numpy<1.23.0,>=1.16.5 (from scipy==1.7.1)
       solution ：降低scipy版本，我的是scipy（1.10.1）版本现换为版本1.7.1，可行！
                   pip uninstall numpy scipy
                   pip install scipy==1.7.1

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   ValueError: Node 'gradients/InceptionResnetV1/Bottleneck/BatchNorm/cond/FusedBatchNorm_1_grad/FusedBatchNormGrad' has an _output_shapes attribute inconsistent with the GraphDef for output #3: Dimension 0 in both shapes must be equal, but are 0 and 512. Shapes are [0] and [512]
       cause :   refer to https://github.com/davidsandberg/facenet/issues/1227 / https://github.com/openvinotoolkit/openvino/pull/11078
       Solution :   For those who undergo this problem. I would suggest following actions:
                   Add the directive "import tensorflow.compat.v1 as tf" to the corresponding .py files.
                   Use the arguments to specify the model file and pair.txt with absolute full path, as following,

python3 FaceNet/src/validate_on_lfw.py ../Inventory/Aligned /Users/xxxx/Projects/Inventory/Models/20180402-114759.pb --distance_metric 1 --use_flipped_images --subtract_mean --use_fixed_image_standardization --lfw_pairs /Users/xxxx/Projects//FaceNet/data/pairs.txt

       +--------------------------------------------------solve compile error ----------------------------------------------+
       problem :   2023-09-13 17:14:46.844967: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled
                   Traceback (most recent call last):
                   File "src/compare.py", line 131, in <module>
                       main(parse_arguments(sys.argv[1:]))
                   File "src/compare.py", line 42, in main
                       images = load_and_align_data(args.image_files, args.image_size, args.margin, args.gpu_memory_fraction)
                   File "src/compare.py", line 111, in load_and_align_data
                       prewhitened = facenet.prewhiten(aligned)
                   File "/home/rd/NN/facenet/src/facenet.py", line 220, in prewhiten
                       y = np.multiply(np.subtract(x, mean), 1/std_adj)
                   ValueError: operands could not be broadcast together with shapes (160,160,3) (2,)
       solution :   vim src/facenet.py +220
                   std_adj = np.maximum(std, 1.0/np.sqrt(np.prod(x.size)))